Vector of initial weights for each site. These equal the reciprocal of the site's inclusion probability.

Vector containing each site's weight adjustment category name. The default is NULL, which assumes every site is in the same category.

Vector containing the known size of the frame for each category name in wgtcat. If wgtcat is provided, the names in framesize must match the names in wgtcat. If wgtcat is not provided, an unnamed scalar is given to framesize.

Vector indicating site use; TRUE indicates the site should be included in the weight adjustment and FALSE indicates the site should not be included in the weight adjustment. The default is NULL, which assumes every site should be included.

vector of weights for each sample unit that will be adjusted for non-response. Weights must be weights for the design as implemented. All weights must be greater than zero.

vector that identifies for each sample unit the category that will be used in non-response weight adjustment for sample units that are known to be target. Within each missing at random (MAR) category, the missing sample units that are not sampled are assumed to be missing at random.

vector of the evaluation status for each sample unit. Values must include the values given in TNRclass and TRClass. May include other values not required for the non-response adjustment.

subset of values in EvalStatus that identify sample units whose target status is known and that do not respond (i.e., are not sampled).

Subset of values in EvalStatus that identify sample units whose target status is known and that respond (i.e., are target and sampled).

Vector used to estimate the density. NA values are allowed.

Vector of weights for each observation from a probability sample. The default assigns equal weights (equal probability).

Number of empty bins to add to the ends when the range is not completely specified. The default is 5.

Number of bins for density estimation. The default is 50.

Optional range for support associated with the density. Both values may be equal to NA. If equal to NA, then corresponding limit will be based on nicerange(). The default is NULL.

Type of support. If equal to "Continuous", then data are from a continuous distribution. If equal to "Ordinal", then data are from a discrete distribution defined for integers only. The default is "Continuous".

Data to be analyzed (analysis data). A data frame or sf object containing survey design variables, response variables, stressor variables, and subpopulation (domain) variables.

Vector composed of character values that identify the names of response variables in dframe. Each response variable must have two category values (levels), where one level is associated with poor condition and the other level is associated with good condition.

Vector composed of character values that identify the names of stressor variables in dframe. Each stressor variable must have two category values (levels), where one level is associated with poor condition and the other level is associated with good condition.

List providing the category values (levels) for each element in the vars_response argument. Each element in the list must contain two values, where the first value identifies poor condition, and the second value identifies good condition. This argument must be named and must be the same length as argument vars_response. Names for this argument must match the values in the vars_response argument. If this argument equals NULL, then a named list is created that contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_response argument and that uses values in the vars_response argument as names for the list. If response_levels is provided without names, then the names of response_levels are set to vars_response. The default value is NULL.

List providing the category values (levels) for each element in the vars_stressor argument. Each element in the list must contain two values, where the first value identifies poor condition, and the second value identifies good condition. This argument must be named and must be the same length as argument vars_stressor. Names for this argument must match the values in the vars_stressor argument. If this argument equals NULL, then a named list is created that contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_stressor argument and that uses values in the vars_stressor argument as names for the list. If stressor_levels is provided without names, then the names of stressor_levels are set to vars_stressor. The default value is NULL.

Vector composed of character values that identify the names of subpopulation (domain) variables in dframe. If a value is not provided, the value "All_Sites" is assigned to the subpops argument and a factor variable named "All_Sites" that takes the value "All Sites" is added to dframe. The default value is NULL.

Character value providing the name of the site ID variable in dframe. For a two-stage sample, the site ID variable identifies stage two site IDs. The default value is NULL, which assumes that each row in dframe represents a unique site.

Character value providing the name of the design weight variable in dframe. For a two-stage sample, the weight variable identifies stage two weights. The default value is "weight".

Character value providing name of the x-coordinate variable in dframe. For a two-stage sample, the x-coordinate variable identifies stage two x-coordinates. Note that x-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the x-coordinate). The default value is NULL.

Character value providing name of the y-coordinate variable in dframe. For a two-stage sample, the y-coordinate variable identifies stage two y-coordinates. Note that y-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the t-coordinate). The default value is NULL.

Character value providing the name of the stratum ID variable in dframe. The default value is NULL.

Character value providing the name of the cluster (stage one) ID variable in dframe. Note that cluster IDs are required for a two-stage sample. The default value is NULL.

Character value providing the name of the stage one weight variable in dframe. The default value is NULL.

Character value providing the name of the stage one x-coordinate variable in dframe. Note that x coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing the name of the stage one y-coordinate variable in dframe. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Logical value that indicates whether size weights should be used during estimation, where TRUE uses size weights and FALSE does not use size weights. To employ size weights for a single-stage sample, a value must be supplied for argument weight. To employ size weights for a two-stage sample, values must be supplied for arguments weight and weight1. The default value is FALSE.

Character value providing the name of the size weight variable in dframe. For a two-stage sample, the size weight variable identifies stage two size weights. The default value is NULL.

Character value providing the name of the stage one size weight variable in dframe. The default value is NULL.

Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator.

Example fpc for a single-stage unstratified survey design:

⁠fpc <- 15000⁠

Example fpc for a single-stage stratified survey design:

⁠fpc <- list( Stratum_1 = 9000, Stratum_2 = 6000) ⁠

Example fpc for a two-stage unstratified survey design:

⁠fpc <- c( Ncluster = 150, Cluster_1 = 150, Cluster_2 = 75, Cluster_3 = 75, Cluster_4 = 125, Cluster_5 = 75) ⁠

Example fpc for a two-stage stratified survey design:

⁠fpc <- list( Stratum_1 = c( Ncluster_1 = 100, Cluster_1 = 125, Cluster_2 = 100, Cluster_3 = 100, Cluster_4 = 125, Cluster_5 = 50), Stratum_2 = c( Ncluster_2 = 50, Cluster_1 = 75, Cluster_2 = 150, Cluster_3 = 75, Cluster_4 = 75, Cluster_5 = 125)) ⁠

Object that provides values for the population argument of the calibrate or postStratify functions in the survey package. If a value is provided for popsize, then either the calibrate or postStratify function is used to modify the survey design object that is required by functions in the survey package. Whether to use the calibrate or postStratify function is dictated by the format of popsize, which is discussed below. Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Calibration, generalized raking, or GREG estimators generalize post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. For the calibrate function, the object is a named list, where the names identify factor variables in dframe. Each element of the list is a named vector containing the population total for each level of the associated factor variable. For the postStratify function, the object is either a data frame, table, or xtabs object that provides the population total for all combinations of selected factor variables in the dframe data frame. If a data frame is used for popsize, the variable containing population totals must be the last variable in the data frame. If a table is used for popsize, the table must have named dimnames where the names identify factor variables in the dframe data frame. If the popsize argument is equal to NULL, then neither calibration nor post-stratification is performed. The default value is NULL.

Example popsize for calibration:

⁠popsize <- list( Ecoregion = c( East = 750, Central = 500, West = 250), Type = c( Streams = 1150, Rivers = 350)) ⁠

Example popsize for post-stratification using a data frame:

⁠popsize <- data.frame( Ecoregion = rep(c("East", "Central", "West"), rep(2, 3)), Type = rep(c("Streams", "Rivers"), 3), Total = c(575, 175, 400, 100, 175, 75)) ⁠

Example popsize for post-stratification using a table:

⁠popsize <- with(MySurveyFrame, table(Ecoregion, Type))⁠

Example popsize for post-stratification using an xtabs object:

⁠popsize <- xtabs(~Ecoregion + Type, data = MySurveyFrame)⁠

Character value providing the choice of the variance estimator, where "Local" indicates the local mean estimator and "SRS" indicates the simple random sampling estimator. The default value is "Local".

Numeric value providing the Gaussian-based confidence level. The default value is 95.

A logical variable used when subpops is not NULL. If All_Sites is TRUE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is returned for each variable in vars. If All_Sites is FALSE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is not returned for each variable in vars. The default is FALSE.

Data to be analyzed (analysis data). A data frame or sf object containing survey design variables, response variables, and subpopulation (domain) variables.

Vector composed of character values that identify the names of response variables in dframe.

Vector composed of character values that identify the names of subpopulation (domain) variables in dframe. If a value is not provided, the value "All_Sites" is assigned to the subpops argument and a factor variable named "All_Sites" that takes the value "All Sites" is added to the dframe data frame. The default value is NULL.

Character value providing name of the site ID variable in the dframe data frame. For a two-stage sample, the site ID variable identifies stage two site IDs. The default value is NULL, which assumes that each row in dframe represents a unique site.

Character value providing name of the design weight variable in dframe. For a two-stage sample, the weight variable identifies stage two weights. The default value is "weight".

Character value providing name of the x-coordinate variable in the dframe data frame. For a two-stage sample, the x-coordinate variable identifies stage two x-coordinates. Note that x-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the x-coordinate). The default value is NULL.

Character value providing name of the y-coordinate variable in the dframe data frame. For a two-stage sample, the y-coordinate variable identifies stage two y-coordinates. Note that y-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the y-coordinate). The default value is NULL.

Character value providing name of the stratum ID variable in the dframe data frame. The default value is NULL.

Character value providing the name of the cluster (stage one) ID variable in dframe. Note that cluster IDs are required for a two-stage sample. The default value is NULL.

Character value providing name of the stage one weight variable in dframe. The default value is NULL.

Character value providing the name of the stage one x-coordinate variable in dframe. Note that x coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing the name of the stage one y-coordinate variable in dframe. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Logical value that indicates whether size weights should be used during estimation, where TRUE uses size weights and FALSE does not use size weights. To employ size weights for a single-stage sample, a value must be supplied for argument weight. To employ size weights for a two-stage sample, values must be supplied for arguments weight and weight1. The default value is FALSE.

Character value providing the name of the size weight variable in dframe. For a two-stage sample, the size weight variable identifies stage two size weights. The default value is NULL.

Character value providing name of the stage one size weight variable in dframe. The default value is NULL.

Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator.

Example fpc for a single-stage unstratified survey design:

⁠fpc <- 15000⁠

Example fpc for a single-stage stratified survey design:

⁠fpc <- list( Stratum_1 = 9000, Stratum_2 = 6000) ⁠

Example fpc for a two-stage unstratified survey design:

⁠fpc <- c( Ncluster = 150, Cluster_1 = 150, Cluster_2 = 75, Cluster_3 = 75, Cluster_4 = 125, Cluster_5 = 75) ⁠

Example fpc for a two-stage stratified survey design:

⁠fpc <- list( Stratum_1 = c( Ncluster_1 = 100, Cluster_1 = 125, Cluster_2 = 100, Cluster_3 = 100, Cluster_4 = 125, Cluster_5 = 50), Stratum_2 = c( Ncluster_2 = 50, Cluster_1 = 75, Cluster_2 = 150, Cluster_3 = 75, Cluster_4 = 75, Cluster_5 = 125)) ⁠

Object that provides values for the population argument of the calibrate or postStratify functions in the survey package. If a value is provided for popsize, then either the calibrate or postStratify function is used to modify the survey design object that is required by functions in the survey package. Whether to use the calibrate or postStratify function is dictated by the format of popsize, which is discussed below. Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Calibration, generalized raking, or GREG estimators generalize post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. For the calibrate function, the object is a named list, where the names identify factor variables in dframe. Each element of the list is a named vector containing the population total for each level of the associated factor variable. For the postStratify function, the object is either a data frame, table, or xtabs object that provides the population total for all combinations of selected factor variables in the dframe data frame. If a data frame is used for popsize, the variable containing population totals must be the last variable in the data frame. If a table is used for popsize, the table must have named dimnames where the names identify factor variables in the dframe data frame. If the popsize argument is equal to NULL, then neither calibration nor post-stratification is performed. The default value is NULL.

Example popsize for calibration:

⁠popsize <- list( Ecoregion = c( East = 750, Central = 500, West = 250), Type = c( Streams = 1150, Rivers = 350)) ⁠

Example popsize for post-stratification using a data frame:

⁠popsize <- data.frame( Ecoregion = rep(c("East", "Central", "West"), rep(2, 3)), Type = rep(c("Streams", "Rivers"), 3), Total = c(575, 175, 400, 100, 175, 75)) ⁠

Example popsize for post-stratification using a table:

⁠popsize <- with(MySurveyFrame, table(Ecoregion, Type))⁠

Example popsize for post-stratification using an xtabs object:

⁠popsize <- xtabs(~Ecoregion + Type, data = MySurveyFrame)⁠

Character value providing the choice of the variance estimator, where "Local" indicates the local mean estimator, "SRS" indicates the simple random sampling estimator, "HT" indicates the Horvitz-Thompson estimator, and "YG" indicates the Yates-Grundy estimator. The default value is "Local".

Character value providing the choice of joint inclusion probability approximation for use with Horvitz-Thompson and Yates-Grundy variance estimators, where "overton" indicates the Overton approximation, "hr" indicates the Hartley-Rao approximation, and "brewer" equals the Brewer approximation. The default value is "overton".

Numeric value providing the Gaussian-based confidence level. The default value is 95.

A logical variable used when subpops is not NULL. If All_Sites is TRUE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is returned for each variable in vars. If All_Sites is FALSE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is not returned for each variable in vars. The default is FALSE.

Data frame with the same structure as the "CDF" output from cont_analysis.

If cdfest has multiple variables in the "Indicator" column, then var is the single variable to be plotted. The default is NULL, which assumes that only one variable is in the "Indicator" column of cdfest.

If cdfest has multiple variables in the "Type" column, then subpop is the single variable to be plotted. The default is NULL, which assumes that only one variable is in the "Type" column of cdfest.

If cdfest has multiple levels of subpop in the "Subpopulation" column, then subpop_level is the single level to be plotted. The default is NULL, which assumes that only one level is in the "Subpopulation" column of cdfest.

Indicator for the label utilized for the left side y-axis and the values used for the left side y-axis tick marks, where "Percent" means the label and values are in terms of percent of the population, and "Units" means the label and values are in terms of units (count, length, or area) of the population. The default is "Percent".

Character string consisting of the value "Continuous" or "Ordinal" that controls the type of CDF plot. The default is "Continuous".

Character string consisting of the value "" or "x" that controls whether the x axis uses the original scale ("") or the base 10 logarithmic scale ("x"). The default is "".

Character string providing the x-axis label. If this argument equals NULL, then the indicator name is used as the label. The default is NULL.

Character string providing the left side y-axis label. If argument units_cdf equals "Units", a value should be provided for this argument. Otherwise, the label will be "Percent". The default is "Percent".

Character string providing the label for the right side y-axis (and, hence, determining the values used for the right side y-axis tick marks), where NULL means a right side y-axis is not created. If this argument equals "Same", the right side y-axis will have the same label and tick mark values as the left side y-axis. If this argument equals a character string other than "Same", the right side y-axis label will be the value provided for argument ylab_r, and the right side y-axis tick mark values will be determined by the choice not utilized for argument units_cdf, which means that the default value of argument units_cdf (i.e., "Percent") will result in the right side y-axis tick mark values being expressed in terms of units of the population (i.e., count, length, or area). The default is NULL.

Character string providing the plot title. The default is NULL.

Indicator for location of the plot legend, where "BR" means bottom right, "BL" means bottom left, "TR" means top right, "TL" means top left, and NULL means no legend. The default is NULL.

Numeric value that controls plotting confidence limits at the CDF extremes. Confidence limits for CDF values (percent scale) less than confcut or greater than 100 minus confcut are not plotted. A value of zero means confidence limits are plotted for the complete range of the CDF. The default is 0.

Numeric value of the confidence level used for confidence limits. The default is 95.

Expansion factor for the plot title. The default is 1.2.

Expansion factor for the legend title. The default is 1.

Additional arguments passed to the plot.default function (aside from those already used and ylim).

Data to be analyzed (analysis data). A data frame or sf object containing survey design variables, response variables, and subpopulation (domain) variables.

Vector composed of character values that identify the names of categorical response variables in dframe. The default is NULL.

Vector composed of character values that identify the names of continuous response variables in dframe. The default is NULL.

Character string or character vector providing the location measure(s) to use for change estimation for continuous variables. The choices are "mean", "total", "median", or some combination of the three options (e.g., c("mean", "total")). The default is "mean".

Vector composed of character values that identify the names of subpopulation (domain) variables in dframe. If a value is not provided, the value "All_Sites" is assigned to the subpops argument and a factor variable named "All_Sites" that takes the value "All Sites" is added to dframe. The default value is NULL.

Character value providing name of the survey ID variable in dframe. The default value is "surveyID".

Character vector of length two that provides the survey names contained in the surveyID variable in the dframe data frame. The two values in the vector identify the first survey and second survey, respectively. If a value is not provided, unique values of the surveyID variable are assigned to the survey_names argument. The default is NULL.

Character value providing name of the site ID variable in dframe. For a two-stage sample, the site ID variable identifies stage two site IDs. The default value is "siteID". If a unique site is visited in both surveys, the corresponding siteID should be the same for both entries.

Character value providing name of the design weight variable in dframe. For a two-stage sample, the weight variable identifies stage two weights. The default value is "weight".

Logical value that indicates whether each repeat visit site has the same design weight in the two surveys, where TRUE = the weight for each repeat visit site is the same and FALSE = the weight for each repeat visit site is not the same. When this argument is FALSE, all of the repeat visit sites are assigned equal weights when calculating the covariance component of the change estimate standard error. The default is FALSE.

Character value providing name of the x-coordinate variable in dframe. For a two-stage sample, the x-coordinate variable identifies stage two x-coordinates. Note that x-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the x-coordinate). The default value is NULL.

Character value providing name of the y-coordinate variable in dframe. For a two-stage sample, the y-coordinate variable identifies stage two y-coordinates. Note that y-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the y-coordinate). The default value is NULL.

Character value providing name of the stratum ID variable in dframe. The default value is NULL.

Character value providing the name of the cluster (stage one) ID variable in dframe. Note that cluster IDs are required for a two-stage sample. The default value is NULL.

Character value providing name of the stage one weight variable in dframe. The default value is NULL.

Character value providing the name of the stage one x-coordinate variable in dframe. Note that x coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing the name of the stage one y-coordinate variable in dframe. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Logical value that indicates whether size weights should be used during estimation, where TRUE uses size weights and FALSE does not use size weights. To employ size weights for a single-stage sample, a value must be supplied for argument weight. To employ size weights for a two-stage sample, values must be supplied for arguments weight and weight1. The default value is FALSE.

Character value providing the name of the size weight variable in dframe. For a two-stage sample, the size weight variable identifies stage two size weights. The default value is NULL.

Character value providing name of the stage one size weight variable in dframe. The default value is NULL.

Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator.

Example fpc for a single-stage unstratified survey design:

⁠fpc <- 15000⁠

Example fpc for a single-stage stratified survey design:

⁠fpc <- list( Stratum_1 = 9000, Stratum_2 = 6000) ⁠

Example fpc for a two-stage unstratified survey design:

⁠fpc <- c( Ncluster = 150, Cluster_1 = 150, Cluster_2 = 75, Cluster_3 = 75, Cluster_4 = 125, Cluster_5 = 75) ⁠

Example fpc for a two-stage stratified survey design:

⁠fpc <- list( Stratum_1 = c( Ncluster_1 = 100, Cluster_1 = 125, Cluster_2 = 100, Cluster_3 = 100, Cluster_4 = 125, Cluster_5 = 50), Stratum_2 = c( Ncluster_2 = 50, Cluster_1 = 75, Cluster_2 = 150, Cluster_3 = 75, Cluster_4 = 75, Cluster_5 = 125)) ⁠

Object that provides values for the population argument of the calibrate or postStratify functions in the survey package. If a value is provided for popsize, then either the calibrate or postStratify function is used to modify the survey design object that is required by functions in the survey package. Whether to use the calibrate or postStratify function is dictated by the format of popsize, which is discussed below. Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Calibration, generalized raking, or GREG estimators generalize post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. For the calibrate function, the object is a named list, where the names identify factor variables in dframe. Each element of the list is a named vector containing the population total for each level of the associated factor variable. For the postStratify function, the object is either a data frame, table, or xtabs object that provides the population total for all combinations of selected factor variables in the dframe data frame. If a data frame is used for popsize, the variable containing population totals must be the last variable in the data frame. If a table is used for popsize, the table must have named dimnames where the names identify factor variables in the dframe data frame. If the popsize argument is equal to NULL, then neither calibration nor post-stratification is performed. The default value is NULL.

Example popsize for calibration:

⁠popsize <- list( Ecoregion = c( East = 750, Central = 500, West = 250), Type = c( Streams = 1150, Rivers = 350)) ⁠

Example popsize for post-stratification using a data frame:

⁠popsize <- data.frame( Ecoregion = rep(c("East", "Central", "West"), rep(2, 3)), Type = rep(c("Streams", "Rivers"), 3), Total = c(575, 175, 400, 100, 175, 75)) ⁠

Example popsize for post-stratification using a table:

⁠popsize <- with(MySurveyFrame, table(Ecoregion, Type))⁠

Example popsize for post-stratification using an xtabs object:

⁠popsize <- xtabs(~Ecoregion + Type, data = MySurveyFrame)⁠

Character value providing the choice of the variance estimator, where "Local" indicates the local mean estimator and "SRS" indicates the simple random sampling estimator. The default value is "Local".

Character value providing the choice of joint inclusion probability approximation for use with Horvitz-Thompson and Yates-Grundy variance estimators, where "overton" indicates the Overton approximation, "hr" indicates the Hartley-Rao approximation, and "brewer" equals the Brewer approximation. The default value is "overton".

Numeric value providing the Gaussian-based confidence level. The default value is 95.

A logical variable used when subpops is not NULL. If All_Sites is TRUE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is returned for each variable in vars. If All_Sites is FALSE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is not returned for each variable in vars. The default is FALSE.

Data to be analyzed (analysis data). A data frame or sf object containing survey design variables, response variables, and subpopulation (domain) variables.

Vector composed of character values that identify the names of response variables in dframe.

Vector composed of character values that identify the names of subpopulation (domain) variables in dframe. If a value is not provided, the value "All_Sites" is assigned to the subpops argument and a factor variable named "All_Sites" that takes the value "All Sites" is added to the dframe data frame. The default value is NULL.

Character value providing name of the site ID variable in the dframe data frame. For a two-stage sample, the site ID variable identifies stage two site IDs. The default value is NULL, which assumes that each row in dframe represents a unique site.

Character value providing name of the design weight variable in dframe. For a two-stage sample, the weight variable identifies stage two weights. The default value is "weight".

Character value providing name of the x-coordinate variable in the dframe data frame. For a two-stage sample, the x-coordinate variable identifies stage two x-coordinates. Note that x-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the x-coordinate). The default value is NULL.

Character value providing name of the y-coordinate variable in the dframe data frame. For a two-stage sample, the y-coordinate variable identifies stage two y-coordinates. Note that y-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the y-coordinate). The default value is NULL.

Character value providing name of the stratum ID variable in the dframe data frame. The default value is NULL.

Character value providing the name of the cluster (stage one) ID variable in dframe. Note that cluster IDs are required for a two-stage sample. The default value is NULL.

Character value providing name of the stage one weight variable in dframe. The default value is NULL.

Character value providing the name of the stage one x-coordinate variable in dframe. Note that x coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing the name of the stage one y-coordinate variable in dframe. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Logical value that indicates whether size weights should be used during estimation, where TRUE uses size weights and FALSE does not use size weights. To employ size weights for a single-stage sample, a value must be supplied for argument weight. To employ size weights for a two-stage sample, values must be supplied for arguments weight and weight1. The default value is FALSE.

Character value providing the name of the size weight variable in dframe. For a two-stage sample, the size weight variable identifies stage two size weights. The default value is NULL.

Character value providing name of the stage one size weight variable in dframe. The default value is NULL.

Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator.

Example fpc for a single-stage unstratified survey design:

⁠fpc <- 15000⁠

Example fpc for a single-stage stratified survey design:

⁠fpc <- list( Stratum_1 = 9000, Stratum_2 = 6000) ⁠

Example fpc for a two-stage unstratified survey design:

⁠fpc <- c( Ncluster = 150, Cluster_1 = 150, Cluster_2 = 75, Cluster_3 = 75, Cluster_4 = 125, Cluster_5 = 75) ⁠

Example fpc for a two-stage stratified survey design:

⁠fpc <- list( Stratum_1 = c( Ncluster_1 = 100, Cluster_1 = 125, Cluster_2 = 100, Cluster_3 = 100, Cluster_4 = 125, Cluster_5 = 50), Stratum_2 = c( Ncluster_2 = 50, Cluster_1 = 75, Cluster_2 = 150, Cluster_3 = 75, Cluster_4 = 75, Cluster_5 = 125)) ⁠

Object that provides values for the population argument of the calibrate or postStratify functions in the survey package. If a value is provided for popsize, then either the calibrate or postStratify function is used to modify the survey design object that is required by functions in the survey package. Whether to use the calibrate or postStratify function is dictated by the format of popsize, which is discussed below. Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Calibration, generalized raking, or GREG estimators generalize post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. For the calibrate function, the object is a named list, where the names identify factor variables in dframe. Each element of the list is a named vector containing the population total for each level of the associated factor variable. For the postStratify function, the object is either a data frame, table, or xtabs object that provides the population total for all combinations of selected factor variables in the dframe data frame. If a data frame is used for popsize, the variable containing population totals must be the last variable in the data frame. If a table is used for popsize, the table must have named dimnames where the names identify factor variables in the dframe data frame. If the popsize argument is equal to NULL, then neither calibration nor post-stratification is performed. The default value is NULL.

Example popsize for calibration:

⁠popsize <- list( Ecoregion = c( East = 750, Central = 500, West = 250), Type = c( Streams = 1150, Rivers = 350)) ⁠

Example popsize for post-stratification using a data frame:

⁠popsize <- data.frame( Ecoregion = rep(c("East", "Central", "West"), rep(2, 3)), Type = rep(c("Streams", "Rivers"), 3), Total = c(575, 175, 400, 100, 175, 75)) ⁠

Example popsize for post-stratification using a table:

⁠popsize <- with(MySurveyFrame, table(Ecoregion, Type))⁠

Example popsize for post-stratification using an xtabs object:

⁠popsize <- xtabs(~Ecoregion + Type, data = MySurveyFrame)⁠

Character value providing the choice of the variance estimator, where "Local" indicates the local mean estimator, "SRS" indicates the simple random sampling estimator, "HT" indicates the Horvitz-Thompson estimator, and "YG" indicates the Yates-Grundy estimator. The default value is "Local".

Character value providing the choice of joint inclusion probability approximation for use with Horvitz-Thompson and Yates-Grundy variance estimators, where "overton" indicates the Overton approximation, "hr" indicates the Hartley-Rao approximation, and "brewer" equals the Brewer approximation. The default value is "overton".

Numeric value providing the Gaussian-based confidence level. The default value is 95.

Vector of the set of values at which percentiles are estimated. The default set is: c(5, 10, 25, 50, 75, 90, 95).

Character vector specifying desired estimates, where "CDF" specifies CDF estimates, "Pct" specifies percentile estimates, "Mean" specifies mean estimates, and "Total" specifies total estimates. Any combination of the four choices may be provided by the user. The default value is c("CDF", "Pct", "Mean", "Total").

A logical variable used when subpops is not NULL. If All_Sites is TRUE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is returned for each variable in vars. If All_Sites is FALSE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is not returned for each variable in vars. The default is FALSE.

Name of the PDF file. The default is "cdf2x2.pdf".

Data frame with the same structure as the "CDF" output from cont_analysis.

Indicator for the label utilized for the left side y-axis and the values used for the left side y-axis tick marks, where "Percent" means the label and values are in terms of percent of the population, and "Units" means the label and values are in terms of units (count, length, or area) of the population. The default is "Percent".

Character vector consisting of the values "Continuous" or "Ordinal" that controls the type of CDF plot for each indicator. The default is "Continuous" for every indicator.

Character vector consisting of the values "" or "x" that controls whether the x axis uses the original scale ("") or the base 10 logarithmic scale ("x") for each indicator. The default is "" for every indicator.

Character vector consisting of the x-axis label for each indicator. If this argument equals NULL, then indicator names are used as the labels. The default is NULL.

Character string providing the left side y-axis label. If argument units_cdf equals "Units", a value should be provided for this argument. Otherwise, the label will be "Percent". The default is "Percent".

Character string providing the label for the right side y-axis (and, hence, determining the values used for the right side y-axis tick marks), where NULL means a right side y-axis is not created. If this argument equals "Same", the right side y-axis will have the same label and tick mark values as the left side y-axis. If this argument equals a character string other than "Same", the right side y-axis label will be the value provided for argument ylab_r, and the right side y-axis tick mark values will be determined by the choice not utilized for argument units_cdf, which means that the default value of argument units_cdf (i.e., "Percent") will result in the right side y-axis tick mark values being expressed in terms of units of the population (i.e., count, length, or area). The default is NULL.

Indicator for location of the plot legend, where "BR" means bottom right, "BL" means bottom left, "TR" means top right, "TL" means top left, and NULL means no legend. The default is NULL.

Number of CDF plots on each page, which must be chosen from the values: 1, 2, 4, or 6. The default is 4.

Width of the graphic region in inches. The default is 10.

Height of the graphic region in inches. The default is 8.

Numeric value that controls plotting confidence limits at the CDF extremes. Confidence limits for CDF values (percent scale) less than confcut or greater than 100 minus confcut are not plotted. A value of zero means confidence limits are plotted for the complete range of the CDF. The default is 0.

Expansion factor for the plot title. The default is 1.2.

Expansion factor for the legend title. The default is 1.

Additional arguments passed to the cdf_plot function.

Data frame containing survey design variables, response variables, and subpopulation (domain) variables.

Vector composed of character values that identify the names of response variables in the dframe data frame.

Vector composed of character values that identify the names of subpopulation (domain) variables in the dframe data frame. If a value is not provided, the value "All_Sites" is assigned to the subpops argument and a factor variable named "All_Sites" that takes the value "All Sites" is added to the dframe data frame. The default value is NULL.

Character value providing name of the survey ID variable in the dframe data frame. If this argument equals NULL, then the dframe data frame contains data for a single survey. The default value is NULL.

Character value providing name of the site ID variable in the dframe data frame. For a two-stage sample, the site ID variable identifies stage two site IDs. The default value is "siteID".

Character value providing name of the survey design weight variable in the dframe data frame. For a two-stage sample, the weight variable identifies stage two weights. The default value is "weight".

Character value providing name of the x-coordinate variable in the dframe data frame. For a two-stage sample, the x-coordinate variable identifies stage two x-coordinates. Note that x-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing name of the y-coordinate variable in the dframe data frame. For a two-stage sample, the y-coordinate variable identifies stage two y-coordinates. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing name of the stratum ID variable in the dframe data frame. The default value is NULL.

Character value providing the name of the cluster (stage one) ID variable in the dframe data frame. Note that cluster IDs are required for a two-stage sample. The default value is NULL.

Character value providing name of the stage one weight variable in the dframe data frame. The default value is NULL.

Character value providing the name of the stage one x-coordinate variable in the dframe data frame. Note that x coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing the name of the stage one y-coordinate variable in the dframe data frame. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Logical value that indicates whether size weights should be used during estimation, where TRUE uses size weights and FALSE does not use size weights. To employ size weights for a single-stage sample, a value must be supplied for argument weight. To employ size weights for a two-stage sample, values must be supplied for arguments weight and weight1. The default value is FALSE.

Character value providing the name of the size weight variable in the dframe data frame. For a two-stage sample, the size weight variable identifies stage two size weights. The default value is NULL.

Character value providing name of the stage one size weight variable in the dframe data frame. The default value is NULL.

Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator.

Example fpc for a single-stage unstratified survey design:

⁠fpc <- 15000⁠

Example fpc for a single-stage stratified survey design:

⁠fpc <- list( Stratum_1 = 9000, Stratum_2 = 6000) ⁠

Example fpc for a two-stage unstratified survey design:

⁠fpc <- c( Ncluster = 150, Cluster_1 = 150, Cluster_2 = 75, Cluster_3 = 75, Cluster_4 = 125, Cluster_5 = 75) ⁠

Example fpc for a two-stage stratified survey design:

⁠fpc <- list( Stratum_1 = c( Ncluster_1 = 100, Cluster_1 = 125, Cluster_2 = 100, Cluster_3 = 100, Cluster_4 = 125, Cluster_5 = 50), Stratum_2 = c( Ncluster_2 = 50, Cluster_1 = 75, Cluster_2 = 150, Cluster_3 = 75, Cluster_4 = 75, Cluster_5 = 125)) ⁠

Object that provides values for the population argument of the calibrate or postStratify functions in the survey package. If a value is provided for popsize, then either the calibrate or postStratify function is used to modify the survey design object that is required by functions in the survey package. Whether to use the calibrate or postStratify function is dictated by the format of popsize, which is discussed below. Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Calibration, generalized raking, or GREG estimators generalize post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. For the calibrate function, the object is a named list, where the names identify factor variables in the dframe data frame. Each element of the list is a named vector containing the population total for each level of the associated factor variable. For the postStratify function, the object is either a data frame, table, or xtabs object that provides the population total for all combinations of selected factor variables in the dframe data frame. If a data frame is used for popsize, the variable containing population totals must be the last variable in the data frame. If a table is used for popsize, the table must have named dimnames where the names identify factor variables in the dframe data frame. If the popsize argument is equal to NULL, then neither calibration nor post-stratification is performed. The default value is NULL.

Example popsize for calibration:

⁠popsize <- list( Ecoregion = c( East = 750, Central = 500, West = 250), Type = c( Streams = 1150, Rivers = 350)) ⁠

Example popsize for post-stratification using a data frame:

⁠popsize <- data.frame( Ecoregion = rep(c("East", "Central", "West"), rep(2, 3)), Type = rep(c("Streams", "Rivers"), 3), Total = c(575, 175, 400, 100, 175, 75)) ⁠

Example popsize for post-stratification using a table:

⁠popsize <- with(MySurveyFrame, table(Ecoregion, Type))⁠

Example popsize for post-stratification using an xtabs object:

⁠popsize <- xtabs(~Ecoregion + Type, data = MySurveyFrame)⁠

Character value providing the choice of the variance estimator, where "Local" indicates the local mean estimator, "SRS" indicates the simple random sampling estimator, "HT" indicates the Horvitz-Thompson estimator, and "YG" indicates the Yates-Grundy estimator. The default value is "Local".

Character value providing the choice of joint inclusion probability approximation for use with Horvitz-Thompson and Yates-Grundy variance estimators, where "overton" indicates the Overton approximation, "hr" indicates the Hartley-Rao approximation, and "brewer" equals the Brewer approximation. The default value is "overton".

Name of the test statistic to be reported in the output data frame. Choices for the name are: "Wald", "adjWald", "RaoScott_First", and "RaoScott_Second", which correspond to the Wald statistic, adjusted Wald statistic, Rao-Scott first-order corrected statistic, and Rao-Scott second-order corrected statistic, respectively. The default is "adjWald".

Number of classes into which the CDFs will be divided (binned), which must equal at least 2. The default is 3.

A matrix (dimensions: number of panels (rows) by number of periods (columns)) containing the number of units visited for each combination of panel and period. Default is matrix(50, 1, 10) which is a single panel of 50 units visited 10 times, typical time is a period.

Either NULL or a list of matrices the same length as paneldsgn specifying the number of revisits made to units in a panel in the same period for each design. Specifying NULL indicates that number of revisits to units is the same for all panels and for all periods and for all panel designs. The default is NULL, a single visit. Names must match list names in paneldsgn.

The variance component estimate for unit. The default is NULL.

The variance component estimate for period The default is NULL.

The variance component estimate for unit by period interaction. The default is NULL.

The variance component estimate for index error. The default is NULL.

Unit correlation across periods. The default is 1.

Period autocorrelation. The default is 0.

Data to be analyzed (analysis data). A data frame or sf object containing survey design variables, response variables, stressor variables, and subpopulation (domain) variables.

Vector composed of character values that identify the names of response variables in dframe. Each response variable must have two category values (levels), where one level is associated with poor condition and the other level is associated with good condition.

Vector composed of character values that identify the names of stressor variables in dframe. Each stressor variable must have two category values (levels), where one level is associated with poor condition and the other level is associated with good condition.

List providing the category values (levels) for each element in the vars_response argument. Each element in the list must contain two values, where the first value identifies poor condition, and the second value identifies good condition. This argument must be named and must be the same length as argument vars_response. Names for this argument must match the values in the vars_response argument. If this argument equals NULL, then a named list is created that contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_response argument and that uses values in the vars_response argument as names for the list. If response_levels is provided without names, then the names of response_levels are set to vars_response. The default value is NULL.

List providing the category values (levels) for each element in the vars_stressor argument. Each element in the list must contain two values, where the first value identifies poor condition, and the second value identifies good condition. This argument must be named and must be the same length as argument vars_stressor. Names for this argument must match the values in the vars_stressor argument. If this argument equals NULL, then a named list is created that contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_stressor argument and that uses values in the vars_stressor argument as names for the list. If stressor_levels is provided without names, then the names of stressor_levels are set to vars_stressor. The default value is NULL.

Vector composed of character values that identify the names of subpopulation (domain) variables in dframe. If a value is not provided, the value "All_Sites" is assigned to the subpops argument and a factor variable named "All_Sites" that takes the value "All Sites" is added to dframe. The default value is NULL.

Character value providing the name of the site ID variable in dframe. For a two-stage sample, the site ID variable identifies stage two site IDs. The default value is NULL, which assumes that each row in dframe represents a unique site.

Character value providing the name of the design weight variable in dframe. For a two-stage sample, the weight variable identifies stage two weights. The default value is "weight".

Character value providing name of the x-coordinate variable in dframe. For a two-stage sample, the x-coordinate variable identifies stage two x-coordinates. Note that x-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the x-coordinate). The default value is NULL.

Character value providing name of the y-coordinate variable in dframe. For a two-stage sample, the y-coordinate variable identifies stage two y-coordinates. Note that y-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the t-coordinate). The default value is NULL.

Character value providing the name of the stratum ID variable in dframe. The default value is NULL.

Character value providing the name of the cluster (stage one) ID variable in dframe. Note that cluster IDs are required for a two-stage sample. The default value is NULL.

Character value providing the name of the stage one weight variable in dframe. The default value is NULL.

Character value providing the name of the stage one x-coordinate variable in dframe. Note that x coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing the name of the stage one y-coordinate variable in dframe. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Logical value that indicates whether size weights should be used during estimation, where TRUE uses size weights and FALSE does not use size weights. To employ size weights for a single-stage sample, a value must be supplied for argument weight. To employ size weights for a two-stage sample, values must be supplied for arguments weight and weight1. The default value is FALSE.

Character value providing the name of the size weight variable in dframe. For a two-stage sample, the size weight variable identifies stage two size weights. The default value is NULL.

Character value providing the name of the stage one size weight variable in dframe. The default value is NULL.

Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator.

Example fpc for a single-stage unstratified survey design:

⁠fpc <- 15000⁠

Example fpc for a single-stage stratified survey design:

⁠fpc <- list( Stratum_1 = 9000, Stratum_2 = 6000) ⁠

Example fpc for a two-stage unstratified survey design:

⁠fpc <- c( Ncluster = 150, Cluster_1 = 150, Cluster_2 = 75, Cluster_3 = 75, Cluster_4 = 125, Cluster_5 = 75) ⁠

Example fpc for a two-stage stratified survey design:

⁠fpc <- list( Stratum_1 = c( Ncluster_1 = 100, Cluster_1 = 125, Cluster_2 = 100, Cluster_3 = 100, Cluster_4 = 125, Cluster_5 = 50), Stratum_2 = c( Ncluster_2 = 50, Cluster_1 = 75, Cluster_2 = 150, Cluster_3 = 75, Cluster_4 = 75, Cluster_5 = 125)) ⁠

Object that provides values for the population argument of the calibrate or postStratify functions in the survey package. If a value is provided for popsize, then either the calibrate or postStratify function is used to modify the survey design object that is required by functions in the survey package. Whether to use the calibrate or postStratify function is dictated by the format of popsize, which is discussed below. Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Calibration, generalized raking, or GREG estimators generalize post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. For the calibrate function, the object is a named list, where the names identify factor variables in dframe. Each element of the list is a named vector containing the population total for each level of the associated factor variable. For the postStratify function, the object is either a data frame, table, or xtabs object that provides the population total for all combinations of selected factor variables in the dframe data frame. If a data frame is used for popsize, the variable containing population totals must be the last variable in the data frame. If a table is used for popsize, the table must have named dimnames where the names identify factor variables in the dframe data frame. If the popsize argument is equal to NULL, then neither calibration nor post-stratification is performed. The default value is NULL.

Example popsize for calibration:

⁠popsize <- list( Ecoregion = c( East = 750, Central = 500, West = 250), Type = c( Streams = 1150, Rivers = 350)) ⁠

Example popsize for post-stratification using a data frame:

⁠popsize <- data.frame( Ecoregion = rep(c("East", "Central", "West"), rep(2, 3)), Type = rep(c("Streams", "Rivers"), 3), Total = c(575, 175, 400, 100, 175, 75)) ⁠

Example popsize for post-stratification using a table:

⁠popsize <- with(MySurveyFrame, table(Ecoregion, Type))⁠

Example popsize for post-stratification using an xtabs object:

⁠popsize <- xtabs(~Ecoregion + Type, data = MySurveyFrame)⁠

Character value providing the choice of the variance estimator, where "Local" indicates the local mean estimator and "SRS" indicates the simple random sampling estimator. The default value is "Local".

Numeric value providing the Gaussian-based confidence level. The default value is 95.

A logical variable used when subpops is not NULL. If All_Sites is TRUE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is returned for each variable in vars. If All_Sites is FALSE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is not returned for each variable in vars. The default is FALSE.

Data frame that contains error messages. The default is "error_vec", which is the name given to the error messages vector created by functions in the spsurvey package.

A sampling frame as an sf object. The coordinate system for sframe must projected (not geographic). If m or z values are in sframe's geometry, they are silently dropped (i.e., only x-coordinates and y-coordinates are preserved).

The base sample size required. If the sampling design is unstratified, this is a single numeric value. If the sampling design is stratified, this is a named vector or list whose names represent each stratum and whose values represent each stratum's sample size. These names must match the values of the stratification variable represented by stratum_var. Legacy sites are considered part of the base sample, so the value for n_base should be equal to the number of legacy sites plus the number of desired non-legacy sites.

A character string containing the name of the column from sframe that identifies stratum membership for each element in sframe. If stratum equals NULL, the sampling design is unstratified and all elements in sframe are eligible to be selected in the sample. The default is NULL.

A character string or vector indicating the inclusion probability type, which must be one of following: "equal" for equal inclusion probabilities; "unequal" for unequal inclusion probabilities according to a categorical variable specified by caty_var; and "proportional" for inclusion probabilities proportional to a positive auxiliary variable specified by aux_var. If the sampling design is unstratified, seltype is a single character vector. If the sampling design is stratified, seltype is a named vector whose names represent each stratum and whose values represent each stratum's inclusion probability type. seltype's default value tries to match the intended inclusion probability type: If caty_var and aux_var are not specified, seltype is "equal"; if caty_var is specified, seltype is "unequal"; and if aux_var is specified, seltype is "proportional".

A character string containing the name of the column from sframe that represents the unequal probability variable.

A character vector indicating the expected sample size for each level of caty_var, the unequal probability variable. If the sampling design is unstratified, caty_n is a named vector whose names represent each level of caty_var and whose values represent each level's expected sample size. The sum of caty_n must equal n_base. If the sampling design is stratified and the expected sample sizes are the same among strata, caty_n is a named vector whose names represent represent each level of caty_var and whose values represent each level's expected sample size – these expected sample sizes are applied to all strata. The sum of caty_n must equal each stratum's value in n_base. If the sampling design is stratified and the expected sample sizes differ among strata, caty_n is a list where each element is named as a stratum in n_base. Each stratum's list element is a named vector whose names represent each level of caty_var and whose values represent each level's expected sample size (within the stratum). The sum of the values in each stratum's list element must equal that stratum's value in n_base.

A character string containing the name of the column from sframe that represents the proportional (to size) inclusion probability variable (auxiliary variable). This auxiliary variable must be positive, and the resulting inclusion probabilities are proportional to the values of the auxiliary variable. Larger values of the auxiliary variable result in higher inclusion probabilities.

This argument can be used instead of legacy_sites when sframe is a POINT or MULTIPOINT geometry (i.e. a finite sampling frame), When legacy_var is used, it is a character string containing the name of the column from sframe that represents whether each site is a legacy site. For legacy sites, the values of the legacy_var must contain character strings that act as a legacy site identifier. For non-legacy sites, the values of the legacy_var column must be NA. Using this approach, legacy_stratum_var, legacy_caty_var, and legacy_aux_var are not required and should not be used (because legacy_var represents a column in sframe). spsurvey assumes that the legacy sites were selected from a previous sampling design that incorporated randomness into site selection and that the legacy sites are elements of the current sampling frame.

An sf object with a POINT or MULTIPOINT geometry representing the legacy sites. spsurvey assumes that the legacy sites were selected from a previous sampling design that incorporated randomness into site selection and that the legacy sites are elements of the current sampling frame. If sframe has a POINT or MULTIPOINT geometry, the observations in legacy_sites should not also be in sframe (i.e., duplicates are not removed). Thus, sframe and legacy_sites together compose the current sampling frame. If m or z values are in legacy_sites' geometry, they are silently dropped (i.e., only x-coordinates and y-coordinates are preserved).

A character string containing the name of the column from legacy_sites that identifies stratum membership for each element of legacy_sites. This argument is required when the sampling design is stratified and its levels must be contained in the levels of the stratum_var variable. The default value of legacy_stratum_var is stratum_var, so legacy_stratum_var need only be specified explicitly when the name of the stratification variable in legacy_sites differs from stratum_var.

A character string containing the name of the column from legacy_sites that identifies the unequal probability variable for each element of legacy_sites. This argument is required when the sampling design uses unequal selection probabilities and its categories must be contained in the levels of the caty_var variable. The default value of legacy_caty_var is caty_var, so legacy_caty_var need only be specified explicitly when the name of the unequal probability variable in legacy_sites differs from caty_var.

A character string containing the name of the column from legacy_sites that identifies the proportional probability variable for each element of legacy_sites. This argument is required when the sampling design uses proportional selection probabilities and the values of the legacy_aux_var variable must be positive. The default value of legacy_aux_var is aux_var, so legacy_aux_var need only be specified explicitly when the name of the proportional probability variable in legacy_sites differs from aux_var.

A numeric value indicating the desired minimum distance between sampled sites. If the sampling design is stratified and mindis is an numeric value, the minimum distance is applied to all strata. If the sampling design is stratified and different minimum distances are desired among strata, then mindis is a list whose names match the names of n_base and whose and values are the minimum distance for the corresponding stratum. If a minimum distance is not desired for a particular stratum, then the corresponding value in mindis should be 0 or NULL (which is equivalent to 0). The units of mindis must represent the units in sframe. A warning is returned if the minimum distance could not be reached after maxtry attempts. If legacy sites are used, the minimum distance requirement (and subsequent warning if maxtry attempts are reached) is enforced for all base sites that are not legacy sites (i.e., the minimum distance is enforced for these sites by comparing distances against all base sites (legacy and non-legacy)).

The number of maximum attempts to apply the minimum distance algorithm to obtain the desired minimum distance between sites. Each iteration takes roughly as long as the standard GRTS algorithm. Successive iterations will always contain at least as many sites satisfying the minimum distance requirement as the previous iteration. The algorithm stops when the minimum distance requirement is met or there are maxtry iterations. The default number of maximum iterations is 10.

The number of reverse hierarchically ordered (rho) replacement sites. If the sampling design is unstratified, then n_over is an integer specifying the number of rho replacement sites desired. If the sampling design is stratified, then n_over is a vector (or list) whose names match the names of n_base and whose values indicate the number of rho replacement sites for each stratum. If replacement sites are not desired for a particular stratum, then the corresponding value in n_over should be 0 or NULL (which is equivalent to 0). If the sampling design is stratified but the number of n_over sites is the same in each stratum, n_over can be a vector which is used for each stratum. If n_over is an unnamed, length-one vector, it's value is recycled and used for each stratum. Note that if the sampling design has unequal selection probabilities (seltype = "unequal"), then n_over sites are given the same proportion of caty_n values as n_base.

The number of nearest neighbor (nn) replacement sites. If the sampling design is unstratified, n_near is integer from 1 to 10 specifying the number of nn replacement sites to be selected for each base site. If the sampling design is stratified but the same number of nn replacement sites is desired for each stratum, n_near is integer from 1 to 10 specifying the number of nn replacement sites to be selected for each base site. If the sampling design is unstratified and a different number of nn replacement sites is desired for each stratum, n_near is a vector (or list) whose names represent strata and whose values is integer from 1 to 10 specifying the number of nn replacement sites to be selected for each base site in the stratum. If replacement sites are not desired for a particular stratum, then the corresponding value in n_over should be 0 or NULL (which is equivalent to 0). For infinite sampling frames, the distance between a site and its nn depends on pt_density. The larger pt_density, the closer the nn neighbors.

The units used to compute the design weights. These units must be standard units as defined by the set_units() function in the units package. The default units match the units of the sf object.

A positive integer controlling the density of the GRTS approximation for infinite sampling frames. The GRTS approximation for infinite sample frames vastly improves computational efficiency by generating many finite points and selecting a sample from the points. pt_density represents the density of finite points per unit to use in the approximation. More specifically, for each stratum, the number of points used in the approximation equals pt_density * (n_base + n_over). A larger value of pt_density means a closer approximation to the infinite sampling frame but less computational efficiency. The default value of pt_density is 10. Note that when used with caty_n, the unequal inclusion probabilities generated from this approach are also approximations.

A character string indicating the naming structure for each site's identifier selected in the sample, which is matched with SiteBegin and included as a variable in the sf object in the function's output. Default is "Site".

A character string indicating the first number to use to match with DesignID while creating each site's identifier selected in the sample. Successive sites are given successive integers. The default starting number is 1 and the number of digits is equal to number of digits in nbase + nover. For example, if nbase is 50 and nover is 0, then the default site identifiers are Site-01 to Site-50

A character string that acts as a separator between DesignID and SiteBegin. The default is "-".

A check for whether the coordinates are projected. If TRUE, an error is returned if coordinates are not projected (i.e., they are geographic or NA). If FALSE, the check is not performed, which means that the crs in sframe (and legacy_sites if provided) can be projected, geographic, or NA.

A sampling frame as an sf object. The coordinate system for sframe must projected (not geographic). If m or z values are in sframe's geometry, they are silently dropped (i.e., only x-coordinates and y-coordinates are preserved).

The base sample size required. If the sampling design is unstratified, this is a single numeric value. If the sampling design is stratified, this is a named vector or list whose names represent each stratum and whose values represent each stratum's sample size. These names must match the values of the stratification variable represented by stratum_var. Legacy sites are considered part of the base sample, so the value for n_base should be equal to the number of legacy sites plus the number of desired non-legacy sites.

A character string containing the name of the column from sframe that identifies stratum membership for each element in sframe. If stratum equals NULL, the sampling design is unstratified and all elements in sframe are eligible to be selected in the sample. The default is NULL.

A character string or vector indicating the inclusion probability type, which must be one of following: "equal" for equal inclusion probabilities; "unequal" for unequal inclusion probabilities according to a categorical variable specified by caty_var; and "proportional" for inclusion probabilities proportional to a positive auxiliary variable specified by aux_var. If the sampling design is unstratified, seltype is a single character vector. If the sampling design is stratified, seltype is a named vector whose names represent each stratum and whose values represent each stratum's inclusion probability type. seltype's default value tries to match the intended inclusion probability type: If caty_var and aux_var are not specified, seltype is "equal"; if caty_var is specified, seltype is "unequal"; and if aux_var is specified, seltype is "proportional".

A character string containing the name of the column from sframe that represents the unequal probability variable.

A character vector indicating the expected sample size for each level of caty_var, the unequal probability variable. If the sampling design is unstratified, caty_n is a named vector whose names represent each level of caty_var and whose values represent each level's expected sample size. The sum of caty_n must equal n_base. If the sampling design is stratified and the expected sample sizes are the same among strata, caty_n is a named vector whose names represent represent each level of caty_var and whose values represent each level's expected sample size – these expected sample sizes are applied to all strata. The sum of caty_n must equal each stratum's value in n_base. If the sampling design is stratified and the expected sample sizes differ among strata, caty_n is a list where each element is named as a stratum in n_base. Each stratum's list element is a named vector whose names represent each level of caty_var and whose values represent each level's expected sample size (within the stratum). The sum of the values in each stratum's list element must equal that stratum's value in n_base.

A character string containing the name of the column from sframe that represents the proportional (to size) inclusion probability variable (auxiliary variable). This auxiliary variable must be positive, and the resulting inclusion probabilities are proportional to the values of the auxiliary variable. Larger values of the auxiliary variable result in higher inclusion probabilities.

This argument can be used instead of legacy_sites when sframe is a POINT or MULTIPOINT geometry (i.e. a finite sampling frame), When legacy_var is used, it is a character string containing the name of the column from sframe that represents whether each site is a legacy site. For legacy sites, the values of the legacy_var must contain character strings that act as a legacy site identifier. For non-legacy sites, the values of the legacy_var column must be NA. Using this approach, legacy_stratum_var, legacy_caty_var, and legacy_aux_var are not required and should not be used (because legacy_var represents a column in sframe). spsurvey assumes that the legacy sites were selected from a previous sampling design that incorporated randomness into site selection and that the legacy sites are elements of the current sampling frame.

An sf object with a POINT or MULTIPOINT geometry representing the legacy sites. spsurvey assumes that the legacy sites were selected from a previous sampling design that incorporated randomness into site selection and that the legacy sites are elements of the current sampling frame. If sframe has a POINT or MULTIPOINT geometry, the observations in legacy_sites should not also be in sframe (i.e., duplicates are not removed). Thus, sframe and legacy_sites together compose the current sampling frame. If m or z values are in legacy_sites' geometry, they are silently dropped (i.e., only x-coordinates and y-coordinates are preserved).

A character string containing the name of the column from legacy_sites that identifies stratum membership for each element of legacy_sites. This argument is required when the sampling design is stratified and its levels must be contained in the levels of the stratum_var variable. The default value of legacy_stratum_var is stratum_var, so legacy_stratum_var need only be specified explicitly when the name of the stratification variable in legacy_sites differs from stratum_var.

A character string containing the name of the column from legacy_sites that identifies the unequal probability variable for each element of legacy_sites. This argument is required when the sampling design uses unequal selection probabilities and its categories must be contained in the levels of the caty_var variable. The default value of legacy_caty_var is caty_var, so legacy_caty_var need only be specified explicitly when the name of the unequal probability variable in legacy_sites differs from caty_var.

A character string containing the name of the column from legacy_sites that identifies the proportional probability variable for each element of legacy_sites. This argument is required when the sampling design uses proportional selection probabilities and the values of the legacy_aux_var variable must be positive. The default value of legacy_aux_var is aux_var, so legacy_aux_var need only be specified explicitly when the name of the proportional probability variable in legacy_sites differs from aux_var.

A numeric value indicating the desired minimum distance between sampled sites. If the sampling design is stratified and mindis is an numeric value, the minimum distance is applied to all strata. If the sampling design is stratified and different minimum distances are desired among strata, then mindis is a list whose names match the names of n_base and whose and values are the minimum distance for the corresponding stratum. If a minimum distance is not desired for a particular stratum, then the corresponding value in mindis should be 0 or NULL (which is equivalent to 0). The units of mindis must represent the units in sframe. A warning is returned if the minimum distance could not be reached after maxtry attempts. If legacy sites are used, the minimum distance requirement (and subsequent warning if maxtry attempts are reached) is enforced for all base sites that are not legacy sites (i.e., the minimum distance is enforced for these sites by comparing distances against all base sites (legacy and non-legacy)).

The number of maximum attempts to apply the minimum distance algorithm to obtain the desired minimum distance between sites. Each iteration takes roughly as long as the standard GRTS algorithm. Successive iterations will always contain at least as many sites satisfying the minimum distance requirement as the previous iteration. The algorithm stops when the minimum distance requirement is met or there are maxtry iterations. The default number of maximum iterations is 10.

The number of reverse hierarchically ordered (rho) replacement sites. If the sampling design is unstratified, then n_over is an integer specifying the number of rho replacement sites desired. If the sampling design is stratified, then n_over is a vector (or list) whose names match the names of n_base and whose values indicate the number of rho replacement sites for each stratum. If replacement sites are not desired for a particular stratum, then the corresponding value in n_over should be 0 or NULL (which is equivalent to 0). If the sampling design is stratified but the number of n_over sites is the same in each stratum, n_over can be a vector which is used for each stratum. If n_over is an unnamed, length-one vector, it's value is recycled and used for each stratum. Note that if the sampling design has unequal selection probabilities (seltype = "unequal"), then n_over sites are given the same proportion of caty_n values as n_base.

The number of nearest neighbor (nn) replacement sites. If the sampling design is unstratified, n_near is integer from 1 to 10 specifying the number of nn replacement sites to be selected for each base site. If the sampling design is stratified but the same number of nn replacement sites is desired for each stratum, n_near is integer from 1 to 10 specifying the number of nn replacement sites to be selected for each base site. If the sampling design is unstratified and a different number of nn replacement sites is desired for each stratum, n_near is a vector (or list) whose names represent strata and whose values is integer from 1 to 10 specifying the number of nn replacement sites to be selected for each base site in the stratum. If replacement sites are not desired for a particular stratum, then the corresponding value in n_over should be 0 or NULL (which is equivalent to 0). For infinite sampling frames, the distance between a site and its nn depends on pt_density. The larger pt_density, the closer the nn neighbors.

The units used to compute the design weights. These units must be standard units as defined by the set_units() function in the units package. The default units match the units of the sf object.

A positive integer controlling the density of the GRTS approximation for infinite sampling frames. The GRTS approximation for infinite sample frames vastly improves computational efficiency by generating many finite points and selecting a sample from the points. pt_density represents the density of finite points per unit to use in the approximation. More specifically, for each stratum, the number of points used in the approximation equals pt_density * (n_base + n_over). A larger value of pt_density means a closer approximation to the infinite sampling frame but less computational efficiency. The default value of pt_density is 10. Note that when used with caty_n, the unequal inclusion probabilities generated from this approach are also approximations.

A character string indicating the naming structure for each site's identifier selected in the sample, which is matched with SiteBegin and included as a variable in the sf object in the function's output. Default is "Site".

A character string indicating the first number to use to match with DesignID while creating each site's identifier selected in the sample. Successive sites are given successive integers. The default starting number is 1 and the number of digits is equal to number of digits in nbase + nover. For example, if nbase is 50 and nover is 0, then the default site identifiers are Site-01 to Site-50

A character string that acts as a separator between DesignID and SiteBegin. The default is "-".

A check for whether the coordinates are projected. If TRUE, an error is returned if coordinates are not projected (i.e., they are geographic or NA). If FALSE, the check is not performed, which means that the crs in sframe (and legacy_sites if provided) can be projected, geographic, or NA.

Matrix of weighted response values or weighted residual values for the sample points.

List from the local mean weight function containing two elements: a matrix named ij composed of the index values of neighboring points and a vector named gwt composed of weights.

Vector of weighted response values or weighted residual values for the sample points.

List from the local mean weight function containing two elements: a matrix named ij composed of the index values of neighboring points and a vector named gwt composed of weights.

Vector of x-coordinates for location of the sample points.

Vector of y-coordinates for location of the sample points.

Vector of inclusion probabilities for the sample points.

Number of neighboring points to use in the calculations.

Two-dimensional array from panel_design and dimnames specifying revisit panel design. Typically, array is output from revisit_dsgn, revisit_bibd or revisit_rand functions.

Two-dimensional array with same dimensions as paneldsgn specifying the number of times a sample unit is sampled at each time period. Default is visitdsgn=NULL, where default assumes that a sample unit will be sampled only once at each time period.

Additional arguments (S3 consistency)

An object to plot. When plotting sampling frames an sf object given the appropriate class using sp_frame. When plotting design sites, an object created by grts() or irs() (which has class sp_design). When plotting analysis data, a data frame or an sf object given the appropriate class using sp_frame.

A formula. One-sided formulas are used to summarize the distribution of numeric or categorical variables. For one-sided formulas, variable names are placed to the right of ~ (a right-hand side variable). Two sided formulas are used to summarize the distribution of a left-hand side variable for each level of each right-hand side categorical variable in the formula. Note that only for two-sided formulas are numeric right-hand side variables coerced to a categorical variables. If an intercept is included as a right-hand side variable (whether the formula is one-sided or two-sided), the total will also be summarized. When plotting sampling frames or analysis data, the default formula is ~ 1. When plotting design sites, siteuse should be used in the formula, and the default formula is ~ siteuse.

Name of the x-coordinate (east-west) in object (only required if object is not an sf object).

Name of y (north-south)-coordinate in object (only required if object is not an sf object).

Projection code for xcoord and ycoord (only required if object is not an sf object).

A named list. The name of each list element corresponds to a right-hand side variable in formula. Values in the list are composed of graphical arguments that are to be passed to every level of the variable. To see all graphical arguments available, run ?plot.sf.

A named list. The name of each list element corresponds to a right-hand side variable in formula. The first element in this list should be "levels" and contain all levels of the particular right-hand side variable. Subsequent names correspond to graphical arguments that are to be passed to the specified levels (in order) of the right-hand side variable. Values for each graphical argument must be specified for each level of the right-hand side variable, but applicable sf defaults will be matched by inputting the value NA. To see all graphical arguments available, run ?plot.sf

Should separate geometries for each level of the right-hand side formula variables be plotted? Defaults to FALSE.

A string indicating the single level of the single right-hand side variable for which a summary is requested. This argument is only used when a single right-hand side variable is provided.

Should the geometry bounding box be fixed across plots? If a length-four vector with names "xmin", "ymin", "xmax", and "ymax" and values indicating bounding box edges, the bounding box will be fixed as fix_bbox across plots. If TRUE, the bounding box will be fixed across plots as the bounding box of object. If FALSE, the bounding box will vary across plots according to the unique geometry for each plot. Defaults to TRUE.

Additional arguments to pass to plot.sf().

The sampling frame (an sf object) to plot alongside design sites. This argument is only used when object corresponds to the design sites.

A character vector of site types to include when plotting design sites. It can only take on values "sframe" (sampling frame), "Legacy" (for legacy sites), "Base" (for base sites), "Over" (for n_over replacement sites), and "Near" (for n_near replacement sites). The order of sites represents the layering in the plot (e.g. siteuse = c("Base", "Legacy") will plot legacy sites on top of base sites. Defaults to all non-NULL elements in x and y with plot order "sframe", "Legacy", "Base", "Over", "Near".

Data frame from the "CDF" output given by cont_analysis.

If cdfest has multiple variables in the "Indicator" column, then var is the single variable to be plotted. The default is NULL, which assumes that only one variable is in the "Indicator" column of cdfest.

If cdfest has multiple variables in the "Type" column, then subpop is the single variable to be plotted. The default is NULL, which assumes that only one variable is in the "Type" column of cdfest.

If cdfest has multiple levels of subpop in the "Subpopulation" column, then subpop_level is the single level to be plotted. The default is NULL, which assumes that only one level is in the "Subpopulation" column of cdfest.

Indicator for the label utilized for the left side y-axis and the values used for the left side y-axis tick marks, where "Percent" means the label and values are in terms of percent of the population, and "Units" means the label and values are in terms of units (count, length, or area) of the population. The default is "Percent".

Character string consisting of the value "Continuous" or "Ordinal" that controls the type of CDF plot. The default is "Continuous".

Character string consisting of the value "" or "x" that controls whether the x axis uses the original scale ("") or the base 10 logarithmic scale ("x"). The default is "".

Character string providing the x-axis label. If this argument equals NULL, then the indicator name is used as the label. The default is NULL.

Character string providing the left side y-axis label. If argument units_cdf equals "Units", a value should be provided for this argument. Otherwise, the label will be "Percent". The default is "Percent".

Character string providing the label for the right side y-axis (and, hence, determining the values used for the right side y-axis tick marks), where NULL means a right side y-axis is not created. If this argument equals "Same", the right side y-axis will have the same label and tick mark values as the left side y-axis. If this argument equals a character string other than "Same", the right side y-axis label will be the value provided for argument ylab_r, and the right side y-axis tick mark values will be determined by the choice not utilized for argument units_cdf, which means that the default value of argument units_cdf (i.e., "Percent") will result in the right side y-axis tick mark values being expressed in terms of units of the population (i.e., count, length, or area). The default is NULL.

Character string providing the plot title. The default is NULL.

Indicator for location of the plot legend, where "BR" means bottom right, "BL" means bottom left, "TR" means top right, "TL" means top left, and NULL means no legend. The default is NULL.

Numeric value that controls plotting confidence limits at the CDF extremes. Confidence limits for CDF values (percent scale) less than confcut or greater than 100 minus confcut are not plotted. A value of zero means confidence limits are plotted for the complete range of the CDF. The default is 0.

Numeric value of the confidence level used for confidence limits. The default is 95.

Expansion factor for the plot title. The default is 1.2.

Expansion factor for the legend title. The default is 1.

Additional arguments passed to the plot.default function (aside from those already used and ylim).

Vector of indicator names

Vector of indicator mean values

Vector of variance component estimates for unit variability for the indicators

Vector of variance component estimates for period variability for the indicators

Vector of variance component estimates for unit by period interaction variability for the indicators

Vector of variance component estimates for index (residual) error for the indicators

Correlation across units. Default is 1.

Correlation across periods. Default is 0.

A list of panel designs each as a matrix. Each element of the list is a matrix with dimnames (dimensions: number of panels (rows) by number of periods (columns)) containing the number of units visited for each combination of panel and period. Dimnames for columns must be able to be coerced into an integer (e.g., 2016). All designs must span the same number of periods. Typically, the panel designs are the output of the function revisit_dsgn.

Either NULL or a list of matrices the same length as paneldsgn specifying the number of revisits made to units in a panel in the same period for each design. Specifying NULL indicates that number of revisits to units is the same for all panels and for all periods and for all panel designs. The default is NULL, a single visit. Names must match list names in paneldsgn.

Trend type is either "mean" where trend is applied as percent trend in the indicator mean or "percent" where the trend is applied as percent trend in the proportion (percent) of the distribution that is below or above a fixed value. Default is trend_type="mean"

When trend_type is equal to "percent", a vector of the values of the indicator fixed value that defines the percent. Default is NULL

When trend_type is equal to "percent", a character vector with values of either "lower" or "upper" for each indicator. "lower" states that the percent is associated with the lower tail of the distribution and "upper" states that the percent is associated with the upper tail of the distribution. Default is NULL.

Single value or vector of assumed percent change from initial value in the indicator for each period. Assumes the trend is expressed as percent per period. Note that the trend may be either positive or negative. The default is 2.

Single value or vector of significance level for linear trend test, alpha, Type I error, level. The default is 0.05.

List object of class powerpaneldesign. Object provides power calculated for a set of panel designs, set of indicators, set of trend values, and set of alpha values. Expect input as list as output from function power_dsgn.

Default is "standard" which plots standard power curve. If equal to "relative", then plot power of one panel design compared to one or more other panel designs.

Character value for trend in mean ("mean") or or percent change in proportion ("percent") of cumulative distribution function above or below a fixed cut point. Default is "mean".

Character value equal to "period" or "change" which designates the type of x-axis for power plot where power is plotted on y-axis. For xaxis_type = "period", x-axis is periods in dsgnpower. If xaxis_type = "change", then x-axis is percent per period with secondary x-axes for total percent per period and associated change in mean. Default is "period". Note that xaxis_type controls how the input for "period" and "trend" parameters is used.

Character value equal to "design" or "indicator" which designates the type of power curve comparison that will occur on a single plot. If comp_type = "design", then on a single plot of power curves all panel designs specified in "dsgns" are plotted for a single indicator, single trend value and single alpha. If comp_type = "indicator", then on a single plot of power curves all indicators specified in "indicator" are plotted for a single panel design, single trend value and single alpha. Default is "design".

Vector of names of panel designs that are to be plotted. Names must be all, or a subset of, names of designs in dsgnpower. Default is NULL which results in only the first panel design in dsgnpower being used.

Vector of indicator names contained in dsgnpower that are to be plotted. Indicator names must be all, or a subset of, indicator names in dsgnpower. Default is NULL which results in only the first indicator in dsgnpower being used.

NULL. A single value or vector of values contained in dsgnpower that will be plotted. Values must be all, or a subset of, trend values in dsgnpower. If xaxis_type is equal to "period", then NULL results in maximum trend value being used and a single value or vector of values results in a separate plot for each value specified. If xaxis_type is equal to "change", then NULL results in all trend values in dsgnpower being plotted on x-axis and a vector of values results in all trend values in dsgnpower from minimum value to maximum value specified being plotted on x-axis.

NULL, a single value or vector of values contained in dsgnpower that will be plotted. Values must be all, or a subset of, period values in dsgnpower. If xaxis_type is equal to "period", then NULL results in all time periods in dsgnpower being plotted on x-axis and a vector of values results in all period values in dsgnpower from minimum value to maximum value specified being plotted on x-axis. If xaxis_type is equal to "change", then NULL results in all time periods in dsgnpower being plotted in separate plots and a vector of values results in time periods specified being plotted in separate plots.

A single value or vector of significance levels (as proportion, e.g. 0.05) contained in dsgnpower to used for power plots. Specifying more than a single value results in multiple plots. Default is NULL which results in the minimum significance level in dsgnpower being used.

Additional arguments (S3 consistency)

Data to be analyzed (analysis data). A data frame or sf object containing survey design variables, response variables, stressor variables, and subpopulation (domain) variables.

Vector composed of character values that identify the names of response variables in dframe. Each response variable must have two category values (levels), where one level is associated with poor condition and the other level is associated with good condition.

Vector composed of character values that identify the names of stressor variables in dframe. Each stressor variable must have two category values (levels), where one level is associated with poor condition and the other level is associated with good condition.

List providing the category values (levels) for each element in the vars_response argument. Each element in the list must contain two values, where the first value identifies poor condition, and the second value identifies good condition. This argument must be named and must be the same length as argument vars_response. Names for this argument must match the values in the vars_response argument. If this argument equals NULL, then a named list is created that contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_response argument and that uses values in the vars_response argument as names for the list. If response_levels is provided without names, then the names of response_levels are set to vars_response. The default value is NULL.

List providing the category values (levels) for each element in the vars_stressor argument. Each element in the list must contain two values, where the first value identifies poor condition, and the second value identifies good condition. This argument must be named and must be the same length as argument vars_stressor. Names for this argument must match the values in the vars_stressor argument. If this argument equals NULL, then a named list is created that contains the values "Poor" and "Good" for the first and second levels, respectively, of each element in the vars_stressor argument and that uses values in the vars_stressor argument as names for the list. If stressor_levels is provided without names, then the names of stressor_levels are set to vars_stressor. The default value is NULL.

Vector composed of character values that identify the names of subpopulation (domain) variables in dframe. If a value is not provided, the value "All_Sites" is assigned to the subpops argument and a factor variable named "All_Sites" that takes the value "All Sites" is added to dframe. The default value is NULL.

Character value providing the name of the site ID variable in dframe. For a two-stage sample, the site ID variable identifies stage two site IDs. The default value is NULL, which assumes that each row in dframe represents a unique site.

Character value providing the name of the design weight variable in dframe. For a two-stage sample, the weight variable identifies stage two weights. The default value is "weight".

Character value providing name of the x-coordinate variable in dframe. For a two-stage sample, the x-coordinate variable identifies stage two x-coordinates. Note that x-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the x-coordinate). The default value is NULL.

Character value providing name of the y-coordinate variable in dframe. For a two-stage sample, the y-coordinate variable identifies stage two y-coordinates. Note that y-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the t-coordinate). The default value is NULL.

Character value providing the name of the stratum ID variable in dframe. The default value is NULL.

Character value providing the name of the cluster (stage one) ID variable in dframe. Note that cluster IDs are required for a two-stage sample. The default value is NULL.

Character value providing the name of the stage one weight variable in dframe. The default value is NULL.

Character value providing the name of the stage one x-coordinate variable in dframe. Note that x coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing the name of the stage one y-coordinate variable in dframe. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Logical value that indicates whether size weights should be used during estimation, where TRUE uses size weights and FALSE does not use size weights. To employ size weights for a single-stage sample, a value must be supplied for argument weight. To employ size weights for a two-stage sample, values must be supplied for arguments weight and weight1. The default value is FALSE.

Character value providing the name of the size weight variable in dframe. For a two-stage sample, the size weight variable identifies stage two size weights. The default value is NULL.

Character value providing the name of the stage one size weight variable in dframe. The default value is NULL.

Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator.

Example fpc for a single-stage unstratified survey design:

⁠fpc <- 15000⁠

Example fpc for a single-stage stratified survey design:

⁠fpc <- list( Stratum_1 = 9000, Stratum_2 = 6000) ⁠

Example fpc for a two-stage unstratified survey design:

⁠fpc <- c( Ncluster = 150, Cluster_1 = 150, Cluster_2 = 75, Cluster_3 = 75, Cluster_4 = 125, Cluster_5 = 75) ⁠

Example fpc for a two-stage stratified survey design:

⁠fpc <- list( Stratum_1 = c( Ncluster_1 = 100, Cluster_1 = 125, Cluster_2 = 100, Cluster_3 = 100, Cluster_4 = 125, Cluster_5 = 50), Stratum_2 = c( Ncluster_2 = 50, Cluster_1 = 75, Cluster_2 = 150, Cluster_3 = 75, Cluster_4 = 75, Cluster_5 = 125)) ⁠

Object that provides values for the population argument of the calibrate or postStratify functions in the survey package. If a value is provided for popsize, then either the calibrate or postStratify function is used to modify the survey design object that is required by functions in the survey package. Whether to use the calibrate or postStratify function is dictated by the format of popsize, which is discussed below. Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Calibration, generalized raking, or GREG estimators generalize post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. For the calibrate function, the object is a named list, where the names identify factor variables in dframe. Each element of the list is a named vector containing the population total for each level of the associated factor variable. For the postStratify function, the object is either a data frame, table, or xtabs object that provides the population total for all combinations of selected factor variables in the dframe data frame. If a data frame is used for popsize, the variable containing population totals must be the last variable in the data frame. If a table is used for popsize, the table must have named dimnames where the names identify factor variables in the dframe data frame. If the popsize argument is equal to NULL, then neither calibration nor post-stratification is performed. The default value is NULL.

Example popsize for calibration:

⁠popsize <- list( Ecoregion = c( East = 750, Central = 500, West = 250), Type = c( Streams = 1150, Rivers = 350)) ⁠

Example popsize for post-stratification using a data frame:

⁠popsize <- data.frame( Ecoregion = rep(c("East", "Central", "West"), rep(2, 3)), Type = rep(c("Streams", "Rivers"), 3), Total = c(575, 175, 400, 100, 175, 75)) ⁠

Example popsize for post-stratification using a table:

⁠popsize <- with(MySurveyFrame, table(Ecoregion, Type))⁠

Example popsize for post-stratification using an xtabs object:

⁠popsize <- xtabs(~Ecoregion + Type, data = MySurveyFrame)⁠

Character value providing the choice of the variance estimator, where "Local" indicates the local mean estimator and "SRS" indicates the simple random sampling estimator. The default value is "Local".

Numeric value providing the Gaussian-based confidence level. The default value is 95.

A logical variable used when subpops is not NULL. If All_Sites is TRUE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is returned for each variable in vars. If All_Sites is FALSE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is not returned for each variable in vars. The default is FALSE.

Number of time periods for the survey design. Typically, number of periods if sampling occurs once per period or number of months if sampling occurs once per month. (v, number of varieties/treatments in BIBD terms)

Number of panels (b, number of blocks in BIBD terms)

Number of time periods to be visited in a panel (k, block size in BIBD terms)

Number of samples in each panel.

Prefix for name of each panel

Numeric name of first sampling occasion, e.g. a specific period.

Number of sampling occasions to skip between planned sampling periods, e.g., sampling will occur only every 5 periods if skip = 5.

Maximum number of iterations in search for D-optimal Generalized Youden Design.

Number of time periods for the panel design. For example, number of periods if sampling occurs once per period or number of months if sampling occurs once per month.

List of lists where each list specifies a revisit panel structure. Each sublist consists of four components: n - sample size for each panel in the sublist, pnl_dsgn - a vector with an even number of elements specifying the panel revisit schedule in terms of the number of consecutive time periods sample units will be sampled, followed by number of consecutive time periods skipped, and then repeated as necessary. pnl_n - number of panels in the sublist, and start_option - option for starting the revisit_dsgn (None, Partial_Begin, or Partial_End) which must be the same a pnl_dsgn. Three basic panel structures are possible: a) if pnl_dsgn ends in 0, then the sample units are visited on all subsequent time periods, b) if pnl_dsgn ends in NA, then panel follows a rotating panel structure, and c) if pnl_dsgn ends in any number > 0, then panel follows a serially alternating panel structure. See details for further information.

Numeric name of first sampling occasion, e.g. a specific period.

Number of time periods to skip between planned sampling periods, e.g., sampling will occur only every 5 periods if skip = 5.

Number of time periods for the survey design. Typically, number of periods if sampling occurs once per period or number of months if sampling occurs once per month. (v, number of varieties (or treatments) in BIBD terms)

Number of panels

Character value must be "none", "panel", or "period". Specifies whether the number of sample events will be fixed for each panel ("panel"), for each sample occasion ("occasion"), or for total panel-period combinations ("none"). Default is "panel".

If rand_control is "panel", this is the number of panels that will be sampled in each time period. If rand_control is "period", this is the number of time periods to be sampled in each panel. If rand_control is "none", this is the total number of panel-period combinations that will have units sampled in the revisit design.

Number of samples in each panel.

Prefix for name of each panel

Numeric name of first sampling occasion, e.g. a specific period.

Number of sampling occasions to skip between planned sampling periods, e.g., sampling will occur only every 5 periods if skip = 5.

An sf object containing some design sites.

The sampling frame as an sf object. The coordinate system for sframe must be one where distance for coordinates is meaningful.

The name of the stratum variable in object and sframe. If NULL (the default), no strata is assumed. If a single character vector is provided, it is assumed this is the name of the stratum variable in object and sframe. If a two-dimensional character vector is provided, one element must be named "object" and corresponds to the name of the stratum variable in object, while the other element must be named "sframe" and corresponds to the name of the stratum variable in sframe.

Inclusion probabilities associated with each row of sframe. If these are not provided, an equal probability design is assumed (within strata).

A character vector of spatial balance metrics:

pielou

Pielou's Evenness Index (the default). This statistic can take on a value between zero and one.

simpsons

Simpsons Evenness Index. This statistic can take on a value between zero and logarithm of the sample size.

rmse

Root-Mean-Squared Error. This statistic can take on a value between zero and infinity.

mse

Mean-Squared Error. This statistic can take on a value between zero and infinity.

mae

Median-Absolute Error. This statistic can take on a value between zero and infinity.

medae

Mean-Absolute Error. This statistic can take on a value between zero and infinity.

chisq

Chi-Squared Loss. This statistic can take on a value between zero and infinity.

All spatial balance metrics have a lower bound of zero, which indicates perfect spatial balance. As the metric value increases, the spatial balance decreases.

Should the extent (total units) within each Voronoi polygon be returned? Defaults to FALSE.

A sampling frame or analysis data

An sp_frame object.

An object to plot. When plotting sampling frames or analysis data, a data frame or sf object. When plotting design sites, an object created by grts() or irs() (which has class sp_design).

Additional arguments to pass to plot.sf().

A formula. One-sided formulas are used to summarize the distribution of numeric or categorical variables. For one-sided formulas, variable names are placed to the right of ~ (a right-hand side variable). Two sided formulas are used to summarize the distribution of a left-hand side variable for each level of each right-hand side categorical variable in the formula. Note that only for two-sided formulas are numeric right-hand side variables coerced to a categorical variables. If an intercept is included as a right-hand side variable (whether the formula is one-sided or two-sided), the total will also be summarized. When plotting sampling frames or analysis data, the default formula is ~ 1. When plotting design sites, siteuse should be used in the formula, and the default formula is ~ siteuse.

Name of the x-coordinate (east-west) in object (only required if object is not an sf object).

Name of y (north-south)-coordinate in object (only required if object is not an sf object).

Projection code for xcoord and ycoord (only required if object is not an sf object).

A named list. The name of each list element corresponds to a right-hand side variable in formula. Values in the list are composed of graphical arguments that are to be passed to every level of the variable. To see all graphical arguments available, run ?plot.sf.

A named list. The name of each list element corresponds to a right-hand side variable in formula. The first element in this list should be "levels" and contain all levels of the particular right-hand side variable. Subsequent names correspond to graphical arguments that are to be passed to the specified levels (in order) of the right-hand side variable. Values for each graphical argument must be specified for each level of the right-hand side variable, but applicable sf defaults will be matched by inputting the value NA. To see all graphical arguments available, run ?plot.sf

Should separate geometries for each level of the right-hand side formula variables be plotted? Defaults to FALSE.

A string indicating the single level of the single right-hand side variable for which a summary is requested. This argument is only used when a single right-hand side variable is provided.

Should the geometry bounding box be fixed across plots? If a length-four vector with names "xmin", "ymin", "xmax", and "ymax" and values indicating bounding box edges, the bounding box will be fixed as fix_bbox across plots. If TRUE, the bounding box will be fixed across plots as the bounding box of object. If FALSE, the bounding box will vary across plots according to the unique geometry for each plot. Defaults to TRUE.

The sampling frame (an sf object) to plot alongside design sites. This argument is only used when object corresponds to the design sites.

A character vector of site types to include when plotting design sites. It can only take on values "sframe" (sampling frame), "Legacy" (for legacy sites), "Base" (for base sites), "Over" (for n_over replacement sites), and "Near" (for n_near replacement sites). The order of sites represents the layering in the plot (e.g. siteuse = c("Base", "Legacy") will plot legacy sites on top of base sites. Defaults to all non-NULL elements in x and y with plot order "sframe", "Legacy", "Base", "Over", "Near".

The design sites (output from grts() or irs()).

A character vector of site types to return. Can contain "Legacy" (for legacy sites), "Base" (for base sites), "Over" (for n_over replacement sites), and "Near" (for n_near replacement sites). The default is NULL, which returns all non-NULL output from object$sites_legacy, object$sites_base, object$sites_over, and object$sites_near.

An object to summarize. When summarizing sampling frames, an sf object. When summarizing design sites, an object created by grts() or irs() (which has class sp_design). When summarizing analysis data, a data frame or an sf object.

Additional arguments to pass to sp_summary(). If the left-hand side of the formula is empty, the appropriate generic arguments are passed to summary.data.frame. If the left-hand side of the formula is provided, the appropriate generic arguments are passed to summary.default.

A formula. One-sided formulas are used to summarize the distribution of numeric or categorical variables. For one-sided formulas, variable names are placed to the right of ~ (a right-hand side variable). Two sided formulas are used to summarize the distribution of a left-hand side variable for each level of each right-hand side categorical variable in the formula. Note that only for two-sided formulas are numeric right-hand side variables coerced to a categorical variables. If an intercept is included as a right-hand side variable (whether the formula is one-sided or two-sided), the total will also be summarized. When summarizing sampling frames or analysis data, the default formula is ~ 1. When summarizing design sites, siteuse should be used in the formula, and the default formula is ~ siteuse.

A string indicating the single level of the single right-hand side variable for which a summary is requested. This argument is only used when a single right-hand side variable is provided.

A character vector indicating the design sites for which summaries are requested in object. Defaults to computing summaries for each non-NULL sites_* list in object.

Data frame that contains stop messages. The default is stop_df, which is the name given to the stop data frame created by functions in the spsurvey package.

Vector of indices for stop messages that are to be printed. The default is a vector containing the integers from 1 through the number of rows in stop_df, which will print all stop messages in the data frame.

An object to summarize. When summarizing sampling frames, an sf object given the appropriate class using sp_frame. When summarizing design sites, an object created by grts() or irs() (which has class sp_design). When summarizing analysis data, a data frame or an sf object given the appropriate class using sp_frame.

A formula. One-sided formulas are used to summarize the distribution of numeric or categorical variables. For one-sided formulas, variable names are placed to the right of ~ (a right-hand side variable). Two sided formulas are used to summarize the distribution of a left-hand side variable for each level of each right-hand side categorical variable in the formula. Note that only for two-sided formulas are numeric right-hand side variables coerced to a categorical variables. If an intercept is included as a right-hand side variable (whether the formula is one-sided or two-sided), the total will also be summarized. When summarizing sampling frames or analysis data, the default formula is ~ 1. When summarizing design sites, siteuse should be used in the formula, and the default formula is ~ siteuse.

A string indicating the single level of the single right-hand side variable for which a summary is requested. This argument is only used when a single right-hand side variable is provided.

Additional arguments to pass to sp_summary(). If the left-hand side of the formula is empty, the appropriate generic arguments are passed to summary.data.frame. If the left-hand side of the formula is provided, the appropriate generic arguments are passed to summary.default.

A character vector indicating the design sites for which summaries are requested in object. Defaults to computing summaries for each non-NULL sites_* list in object.

Data to be analyzed (analysis data). A data frame or sf object containing survey design variables, response variables, and subpopulation (domain) variables.

Vector composed of character values that identify the names of categorical response variables in dframe. If argument model_cat equals "GLMM", the categorical variables in the dframe data frame must be factors each of which has two levels, where the second level will be assumed to specify "success". The default value is NULL.

Vector composed of character values that identify the names of continuous response variables in dframe. The default value is NULL.

Vector composed of character values that identify the names of subpopulation (domain) variables in dframe. If a value is not provided, the value "All_Sites" is assigned to the subpops argument and a factor variable named "All_Sites" that takes the value "All Sites" is added to dframe. The default value is NULL.

Character value identifying the analytical procedure used for trend estimation for categorical variables. The choices are: "SLR" (simple linear regression), "WLR" (weighted linear regression), and "GLMM" (generalized linear mixed-effects model). The default value is "SLR".

Character value specifying the right hand side of the formula for a generalized linear mixed-effects model. If a value is not provided, the argument is assigned a value that specifies the Piepho and Ogutu (2002) model. The default value is NULL.

Character value identifying the analytical procedure used for trend estimation for continuous variables. The choices are: "SLR" (simple linear regression), "WLR" (weighted linear regression), and "LMM" (linear mixed-effects model). The default value is "LMM".

Character value specifying the right hand side of the formula for a linear mixed-effects model. If a value is not provided, the argument is assigned a value that specifies the Piepho and Ogutu (2002) model. The default value is NULL.

Character value providing name of the site ID variable in dframe. If repeat visit sites are present, the site ID value for each revisit site will be the same for each survey. For a two-stage sample, the site ID variable identifies stage two site IDs. The default value is "siteID".

Character value providing name of the time period variable in dframe, which must be numeric and will be forced to numeric if it is not. The default assumption is that the time period variable is years. The default value is "year".

Character value providing name of the design weight variable in dframe. For a two-stage sample, the weight variable identifies stage two weights. The default value is "weight".

Character value providing name of the x-coordinate variable in dframe. For a two-stage sample, the x-coordinate variable identifies stage two x-coordinates. Note that x-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the x-coordinate). The default value is NULL.

Character value providing name of the y-coordinate variable in dframe. For a two-stage sample, the y-coordinate variable identifies stage two y-coordinates. Note that y-coordinates are required for calculation of the local mean variance estimator. If dframe is an sf object, this argument is not required (as the geometry column in dframe is used to find the y-coordinate). The default value is NULL.

Character value providing name of the stratum ID variable in dframe. The default value is NULL.

Character value providing name of the cluster (stage one) ID variable in dframe. Note that cluster IDs are required for a two-stage sample. The default value is NULL.

Character value providing name of the stage one weight variable in dframe. The default value is NULL.

Character value providing name of the stage one x-coordinate variable in dframe. Note that x-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Character value providing name of the stage one y-coordinate variable in dframe. Note that y-coordinates are required for calculation of the local mean variance estimator. The default value is NULL.

Logical value that indicates whether size weights should be used during estimation, where TRUE = use size weights and FALSE = do not use size weights. To employ size weights for a single-stage sample, a value must be supplied for argument weight. To employ size weights for a two-stage sample, values must be supplied for arguments weight and weight1. The default value is FALSE.

Character value providing name of the size weight variable in dframe. For a two-stage sample, the size weight variable identifies stage two size weights. The default value is NULL.

Character value providing name of the stage one size weight variable in dframe. The default value is NULL.

Object that specifies values required for calculation of the finite population correction factor used during variance estimation. The object must match the survey design in terms of stratification and whether the design is single-stage or two-stage. For an unstratified design, the object is a vector. The vector is composed of a single numeric value for a single-stage design. For a two-stage unstratified design, the object is a named vector containing one more than the number of clusters in the sample, where the first item in the vector specifies the number of clusters in the population and each subsequent item specifies the number of stage two units for the cluster. The name for the first item in the vector is arbitrary. Subsequent names in the vector identify clusters and must match the cluster IDs. For a stratified design, the object is a named list of vectors, where names must match the strata IDs. For each stratum, the format of the vector is identical to the format described for unstratified single-stage and two-stage designs. Note that the finite population correction factor is not used with the local mean variance estimator.

Example fpc for a single-stage unstratified survey design:

⁠fpc <- 15000⁠

Example fpc for a single-stage stratified survey design:

⁠fpc <- list( Stratum_1 = 9000, Stratum_2 = 6000) ⁠

Example fpc for a two-stage unstratified survey design:

⁠fpc <- c( Ncluster = 150, Cluster_1 = 150, Cluster_2 = 75, Cluster_3 = 75, Cluster_4 = 125, Cluster_5 = 75) ⁠

Example fpc for a two-stage stratified survey design:

⁠fpc <- list( Stratum_1 = c( Ncluster_1 = 100, Cluster_1 = 125, Cluster_2 = 100, Cluster_3 = 100, Cluster_4 = 125, Cluster_5 = 50), Stratum_2 = c( Ncluster_2 = 50, Cluster_1 = 75, Cluster_2 = 150, Cluster_3 = 75, Cluster_4 = 75, Cluster_5 = 125)) ⁠

Object that provides values for the population argument of the calibrate or postStratify functions in the survey package. If a value is provided for popsize, then either the calibrate or postStratify function is used to modify the survey design object that is required by functions in the survey package. Whether to use the calibrate or postStratify function is dictated by the format of popsize, which is discussed below. Post-stratification adjusts the sampling and replicate weights so that the joint distribution of a set of post-stratifying variables matches the known population joint distribution. Calibration, generalized raking, or GREG estimators generalize post-stratification and raking by calibrating a sample to the marginal totals of variables in a linear regression model. For the calibrate function, the object is a named list, where the names identify factor variables in dframe. Each element of the list is a named vector containing the population total for each level of the associated factor variable. For the postStratify function, the object is either a data frame, table, or xtabs object that provides the population total for all combinations of selected factor variables in the dframe data frame. If a data frame is used for popsize, the variable containing population totals must be the last variable in the data frame. If a table is used for popsize, the table must have named dimnames where the names identify factor variables in the dframe data frame. If the popsize argument is equal to NULL, then neither calibration nor post-stratification is performed. The default value is NULL.

Example popsize for calibration:

⁠popsize <- list( Ecoregion = c( East = 750, Central = 500, West = 250), Type = c( Streams = 1150, Rivers = 350)) ⁠

Example popsize for post-stratification using a data frame:

⁠popsize <- data.frame( Ecoregion = rep(c("East", "Central", "West"), rep(2, 3)), Type = rep(c("Streams", "Rivers"), 3), Total = c(575, 175, 400, 100, 175, 75)) ⁠

Example popsize for post-stratification using a table:

⁠popsize <- with(MySurveyFrame, table(Ecoregion, Type))⁠

Example popsize for post-stratification using an xtabs object:

⁠popsize <- xtabs(~Ecoregion + Type, data = MySurveyFrame)⁠

Logical value that indicates whether the inverse probability bootstrap procedure is used to calculate trend parameter estimates. This bootstrap procedure is only available for the "LMM" option for continuous variables. Inverse probability references the design weights, which are the inverse of the sample inclusion probabilities. The default value is TRUE.

Numeric value for the number of bootstrap iterations. The default is 1000.

Character value providing choice of the variance estimator, where "Local" = the local mean estimator, "SRS" = the simple random sampling estimator, "HT" = the Horvitz-Thompson estimator, and "YG" = the Yates-Grundy estimator. The default value is "Local".

Character value providing choice of joint inclusion probability approximation for use with Horvitz-Thompson and Yates-Grundy variance estimators, where "overton" indicates the Overton approximation, "hr" indicates the Hartley_Rao approximation, and "brewer" equals the Brewer approximation. The default value is "overton".

Numeric value for the Gaussian-based confidence level. The default is 95.

A logical variable used when subpops is not NULL. If All_Sites is TRUE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is returned for each variable in vars. If All_Sites is FALSE, then alongside the subpopulation output, output for all sites (ignoring subpopulations) is not returned for each variable in vars. The default is FALSE.

Data frame that contains warning messages. The default is "warn_df", which is the name given to the warnings data frame created by functions in the spsurvey package.

Vector of indices for warning messages that are to be printed. The default is a vector containing the integers from 1 through the number of rows in warn_df, which will print all warning messages in the data frame.