| Title: | Doubly-Robust Estimators for Ordinal Outcomes |
| Version: | 1.0.1 |
| Description: | Efficient covariate-adjusted estimators of quantities that are useful for establishing the effects of treatments on ordinal outcomes. |
| Depends: | R (≥ 3.5.0) |
| Imports: | MASS, VGAM, ordinal, ggplot2 |
| Suggests: | testthat, knitr, rmarkdown, ggsci |
| License: | MIT + file LICENSE |
| URL: | https://github.com/benkeser/drord |
| BugReports: | https://github.com/benkeser/drord/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.1.1 |
| NeedsCompilation: | no |
| Packaged: | 2021-05-20 20:32:29 UTC; dbenkes |
| Author: | David Benkeser 
[aut, cre, cph] |
| Maintainer: | David Benkeser <benkeser@emory.edu> |
| Repository: | CRAN |
| Date/Publication: | 2021-05-20 22:30:03 UTC |
Fits a proportional odds model via pooled logistic regression.
Description
The outcome in data (indicated in the form object) should be an ordered factor.
Usage
POplugin(form, data, weights = 1)
Arguments
form |
The model formula |
data |
The data set used to fit the model |
weights |
Either equal to 1 (no weights) or a vector of length equal to nrow(data) |
Value
A list with the fitted glm, the original data, levels of the outcome, and the outcome name
Compute a BCa confidence interval
Description
Compute a BCa confidence interval
Usage
bca_interval(pt_est, boot_samples, jack_samples, alpha = 0.05)
Arguments
pt_est |
The point estimate of the parameter of interest |
boot_samples |
A collection of bootstrap realizations of the estimator of the parameter of interest |
jack_samples |
A vector of jackknife estimates of the parameter of interest. |
alpha |
Confidence intervals have nominal level 1- |
Value
2-length vector containing BCa confidence interval limits.
Compute a BCa bootstrap confidence interval for the weighted mean. The code is based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
Description
Compute a BCa bootstrap confidence interval for the weighted mean. The code is based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
Usage
bca_logodds(
treat,
covar,
out,
nboot,
treat_form,
out_levels,
out_form,
out_model,
logodds_est,
alpha = 0.05
)
Arguments
treat |
A |
covar |
A |
out |
A |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
logodds_est |
The estimated log-odds. |
alpha |
Level of confidence interval. |
Value
matrix with treatment-specific log-odds CIs and CI for difference.
Compute a BCa bootstrap confidence interval for the Mann-Whitney parameter. The code is based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
Description
Compute a BCa bootstrap confidence interval for the Mann-Whitney parameter. The code is based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
Usage
bca_mannwhitney(
treat,
covar,
out,
nboot,
treat_form,
out_levels,
out_form,
mannwhitney_est,
out_model,
alpha = 0.05
)
Arguments
treat |
A |
covar |
A |
out |
A |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
mannwhitney_est |
The point estimate of the Mann-Whitney parameter. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
alpha |
Level of confidence interval. |
Value
Confidence interval for the Mann-Whitney parameter
Compute a BCa bootstrap confidence interval for the weighted mean. The code is based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
Description
Compute a BCa bootstrap confidence interval for the weighted mean. The code is based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
Usage
bca_marg_dist(
treat,
covar,
out,
nboot,
treat_form,
out_levels,
out_form,
out_model,
marg_cdf_est,
marg_pmf_est,
alpha = 0.05
)
Arguments
treat |
A |
covar |
A |
out |
A |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
marg_cdf_est |
Point estimate of treatment-specific CDF. |
marg_pmf_est |
Point estimate of treatment-specific PMF. |
alpha |
Level of confidence interval. |
Value
List (cdf, pmf) of lists (treat=1, treat=0) of
confidence intervals for distributions.
Compute a BCa bootstrap confidence interval for the weighted mean. The code is based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
Description
Compute a BCa bootstrap confidence interval for the weighted mean. The code is based on the slides found here: http://users.stat.umn.edu/~helwig/notes/bootci-Notes.pdf
Usage
bca_wmean(
treat,
covar,
out,
nboot,
treat_form,
out_levels,
out_form,
out_weights,
out_model,
wmean_est,
alpha = 0.05
)
Arguments
treat |
A |
covar |
A |
out |
A |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_weights |
A vector of |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
wmean_est |
The estimated weighted means + estimated covariance matrix. |
alpha |
Level of confidence interval. |
Value
matrix with treatment-specific weighted mean CIs and CI for difference.
Used to compute treatment-specific BCa intervals for the CDF and PMF
Description
Used to compute treatment-specific BCa intervals for the CDF and PMF
Usage
compute_trt_spec_bca_intervals(
dist = c("cdf", "pmf"),
trt = c(1, 0),
marg_est,
boot_samples,
jack_samples,
alpha
)
Arguments
dist |
Which one? CDF or PMF? |
trt |
Which treatment? |
marg_est |
The point estimate |
boot_samples |
A collection of bootstrap realizations of the estimator of the parameter of interest |
jack_samples |
A vector of jackknife estimates of the parameter of interest. |
alpha |
Confidence intervals have nominal level 1- |
Value
List of pointwise and simultaneous confidence intervals for dist.
Compute simultaneous confidence interval for treatment-specific marginal distribution
Description
Compute simultaneous confidence interval for treatment-specific marginal distribution
Usage
compute_trt_spec_marg_dist_ptwise_ci(pt_est, cov_est, alpha, cdf = TRUE)
Arguments
pt_est |
The point estimate of the treatment-specific marginal CDF/PMF |
cov_est |
Covariance matrix estimates. |
alpha |
Confidence intervals have nominal level 1- |
cdf |
Is this for CDF or PMF? |
Value
Confidence interval
Compute simultaneous confidence interval for treatment-specific marginal distribution
Description
Compute simultaneous confidence interval for treatment-specific marginal distribution
Usage
compute_trt_spec_marg_dist_simul_ci(
pt_est,
trt_spec_marg_dist_eif,
remove_last = TRUE,
alpha
)
Arguments
pt_est |
The point estimate of the treatment-specific marginal CDF/PMF |
trt_spec_marg_dist_eif |
The EIF estimates for the treatment-specific marginal CDF/PMF estimates |
remove_last |
Should the last level be removed? Should be set equal to
|
alpha |
Confidence intervals have nominal level 1- |
Value
Confidence interval
Simulated COVID-19 outcomes for hospitalized patients.
Description
A simulated dataset containing outcomes, (hypothetical) treatment, and age group
Usage
covid19
Format
A data frame with 500 rows and 3 variables:
- out
study outcome, here 1 represents death, 2 intubation, 3 no adverse outcome
- age_grp
age category with 1 the youngest and 7 the oldest
- treat
hypothetical treatment, here 1 represents an (effective) active treatment and 0 a control
Doubly robust estimates of for evaluating effects of treatments on ordinal outcomes.
Description
The available parameters for evaluating treatment efficacy are:
Difference in (weighted) means: The outcome levels are treated numerically, with each level possibly assigned a weight. The difference in average outcomes is computed.
Log odds ratio: The comparison describes the average log-odds (treatment level 1 versus 0) of the cumulative probability for each level of the outcome.
Mann-Whitney: The probability that a randomly-selected individual receiving treatment 1 will have a larger outcome value than a randomly selected individual receiving treatment 0 (with ties assigned weight 1/2).
Usage
drord(
out,
treat,
covar,
out_levels = sort(unique(out)),
out_form = paste0(colnames(covar), collapse = "+"),
out_weights = rep(1, length(out_levels)),
out_model = "pooled-logistic",
treat_form = "1",
param = c("weighted_mean", "log_odds", "mann_whitney"),
ci = "wald",
alpha = 0.05,
nboot = 1000,
return_models = TRUE,
est_dist = TRUE,
stratify = FALSE,
...
)
Arguments
out |
A |
treat |
A |
covar |
A |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_weights |
A vector of |
out_model |
Which R function should be used to fit the proportional odds
model. The recommended option is |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
param |
A vector of |
ci |
A vector of |
alpha |
Confidence intervals have nominal level 1- |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
return_models |
If |
est_dist |
A |
stratify |
If |
... |
Other options (not currently used). |
Details
In each case, estimates are constructed by obtaining a doubly robust estimate of the cumulative distribution function (CDF) for each treatment group. This is achieved by fitting a (working) proportional odds model that includes inverse probability of treatment weights. The inclusion of these weights ensures that, so long as the working model includes intercept terms, the resultant estimate of the CDF is an augmented inverse probability of treatment weighted estimate. This implies that the estimate is nonparametric efficient if the working model contains the truth; however, even if the working model does not contain the truth, the CDF estimates are consistent and asymptotically normal with variance expected to dominate that of an unadjusted estimate of the same treatment effect.
The CDF estimates are subsequently mapped into estimates of each requested
parameter for evaluating treatment effects. The double robustness and efficiency
properties of the CDF estimates extend to these quantities as well. Confidence
intervals and hypothesis tests can be carried out in closed form using Wald-style
intervals and tests or using a nonparametric corrected and accelerated bootstrap
(BCa). Inference for the CDF and probability mass function is also returned and
can be used for subsequent visualizations (see plot.drord).
Value
An object of class drord. In addition to information related to
how drord was called, the output contains the following:
- log_odds
inference pertaining to the log-odds parameter.
NULLif this parameter not requested in call todrord.- mann_whitney
inference pertaining to the Mann-Whitney parameter.
NULLif this parameter not requested in call todrord.- weighted_mean
inference pertaining to weighted mean parameter.
NULLif this parameter not requested in call todrord.- cdf
inference pertaining to the treatment-specific CDFs. See the
plotmethod for a convenient way of visualizing this information.NULLifest_dist = FALSEin call todrord.- pmf
inference pertaining to the treatment-specific PMFs. See the
plotmethod for a convenient way of visualizing this information.NULLifest_dist = FALSEin call todrord.- treat_mod
the fitted model for the probability of treatment as a function of covariates.
NULLifreturn_models = FALSE- out_mod
the proportional odds model fit in each treatment arm. named entries in list indicate the corresponding treatment arm.
NULLifreturn_models = FALSEorstratify = TRUE.
Examples
data(covid19)
# get estimates of all parameters based on main-effects
# proportional odds model and intercept-only propensity model
fit <- drord(out = covid19$out, treat = covid19$treat,
covar = covid19[, "age_grp", drop = FALSE])
# get estimates of all parameters based on proportional odds and
# propensity model that treats age_grp as categorical
fit2 <- drord(out = covid19$out, treat = covid19$treat,
covar = covid19[, "age_grp", drop = FALSE],
out_form = "factor(age_grp)",
treat_form = "factor(age_grp)")
# obtain estimator stratified by age group
fit3 <- drord(out = covid19$out, treat = covid19$treat,
covar = covid19[, "age_grp", drop = FALSE],
stratify = TRUE)
# demonstration with missing outcome data
covid19$out[1:5] <- NA
# propensity model should now adjust for covariates to address
# the potential for informative missingness
fit4 <- drord(out = covid19$out, treat = covid19$treat,
covar = covid19[, "age_grp", drop = FALSE],
treat_form = "age_grp")
Get EIF estimates for treatment-specific PMF at a particular level of the outcome
Description
Get EIF estimates for treatment-specific PMF at a particular level of the outcome
Usage
eif_pmf_k(k, out, treat, trt_level, trt_spec_prob_est, trt_k_spec_pmf_est)
Arguments
k |
The level of the outcome. |
out |
A |
treat |
A |
trt_level |
Treatment level |
trt_spec_prob_est |
Estimated propensity for |
trt_k_spec_pmf_est |
Estimated conditional PMF for |
Get EIF estimates for treatment-specific CDF at a particular level of the outcome
Description
Get EIF estimates for treatment-specific CDF at a particular level of the outcome
Usage
eif_theta_k(k, out, treat, trt_level, trt_spec_prob_est, trt_k_spec_cdf_est)
Arguments
k |
The level of the outcome. |
out |
A |
treat |
A |
trt_level |
Treatment level |
trt_spec_prob_est |
Estimated propensity for |
trt_k_spec_cdf_est |
Estimated conditional CDF for |
Map an estimate of the conditional PMF into an estimate of the conditional CDF
Description
Map an estimate of the conditional PMF into an estimate of the conditional CDF
Usage
estimate_cdf(pmf_est)
Arguments
pmf_est |
A list of the treatment-specific PMF estimates |
Value
A list of treatment-specific CDF estimates
Compute confidence interval/s for the log-odds parameters
Description
Compute confidence interval/s for the log-odds parameters
Usage
estimate_ci_logodds(
logodds_est,
cdf_est,
out_form,
covar,
treat_prob_est,
treat,
treat_form,
out,
ci,
alpha = 0.05,
nboot,
out_levels,
out_model,
...
)
Arguments
logodds_est |
The point estimates for log-odds. |
cdf_est |
A list of treatment-specific CDF estimates. |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
covar |
A |
treat_prob_est |
Estimated probability of treatments, output from call
to |
treat |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out |
A |
ci |
A vector of |
alpha |
Confidence intervals have nominal level 1- |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
out_levels |
A |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
... |
Other options (not currently used). |
Value
List with wald and bca-estimated confidence intervals
for the weighted mean parameters.
Compute confidence interval/s for the Mann-Whitney parameter
Description
Compute confidence interval/s for the Mann-Whitney parameter
Usage
estimate_ci_mannwhitney(
mannwhitney_est,
cdf_est,
pmf_est,
treat_prob_est,
treat_form,
out_form,
treat,
ci,
out,
alpha,
nboot,
out_levels,
covar,
out_model
)
Arguments
mannwhitney_est |
The point estimates for log-odds. |
cdf_est |
The estimated conditional CDF. |
pmf_est |
The estimated conditional PMF. |
treat_prob_est |
Estimated probability of treatments, output from call
to |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
treat |
A |
ci |
A vector of |
out |
A |
alpha |
Confidence intervals have nominal level 1- |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
out_levels |
A |
covar |
A |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
List with wald and bca-estimated confidence intervals
for the Mann-Whitney parameter.
Compute confidence interval/s for the treatment specific PMF and CDF.
Description
Compute confidence interval/s for the treatment specific PMF and CDF.
Usage
estimate_ci_marg_dist(
marg_cdf_est,
marg_pmf_est,
cdf_est,
pmf_est,
covar,
treat_prob_est,
treat_form,
out_form,
treat,
ci,
out_levels,
out_model,
out,
alpha,
nboot
)
Arguments
marg_cdf_est |
Point estimate of treatment-specific CDF. |
marg_pmf_est |
Point estimate of treatment-specific PMF. |
cdf_est |
Estimates of treatment-specific conditional CDF. |
pmf_est |
Estimates of treatment-specific conditional PMF. |
covar |
A |
treat_prob_est |
Estimated probability of treatments, output from call
to |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
treat |
A |
ci |
A vector of |
out_levels |
A |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
out |
A |
alpha |
Confidence intervals have nominal level 1- |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
Value
List of lists (cdf and pmf) with wald and bca-estimated confidence
intervals for the marginal treatment-specific distribution functions.
Compute confidence interval/s for the weight mean parameters
Description
Compute confidence interval/s for the weight mean parameters
Usage
estimate_ci_wmean(
out,
treat,
covar,
wmean_est,
alpha = 0.05,
out_levels = order(unique(out)),
out_form = NULL,
out_weights = rep(1, length(out_levels)),
out_model,
treat_form = "1",
ci = c("bca", "wald"),
nboot = 10000
)
Arguments
out |
A |
treat |
A |
covar |
A |
wmean_est |
The point estimates for weighted means |
alpha |
Confidence intervals have nominal level 1- |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_weights |
A vector of |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
ci |
A vector of |
nboot |
Number of bootstrap replicates used to compute bootstrap confidence intervals. |
Value
List with wald and bca-estimated confidence intervals
for the weighted mean parameters.
Map an estimate of treatment-specific PMF into an estimate of treatment specific conditional mean for each observation.
Description
Map an estimate of treatment-specific PMF into an estimate of treatment specific conditional mean for each observation.
Usage
estimate_cond_mean(trt_spec_pmf_est, ordered_out_levels, ordered_out_weights)
Arguments
trt_spec_pmf_est |
The treatment-specific PMF estimates |
ordered_out_levels |
Self explanatory |
ordered_out_weights |
Self explanatory |
Value
Vector of estimated conditional means
Obtain an estimate of the efficient influence function for the treatment-specific weighted mean parameter
Description
Obtain an estimate of the efficient influence function for the treatment-specific weighted mean parameter
Usage
estimate_eif_wmean(
trt_spec_cond_mean_est,
trt_spec_prob_est,
trt_level,
out,
treat
)
Arguments
trt_spec_cond_mean_est |
Conditional mean for |
trt_spec_prob_est |
Propensity for |
trt_level |
Treatment level |
out |
A |
treat |
A |
implements a plug-in estimator of equation (2) in Diaz et al
Description
implements a plug-in estimator of equation (2) in Diaz et al
Usage
estimate_logodds(cdf_est)
Arguments
cdf_est |
A list of treatment-specific CDF estimates |
Value
Log odds of treatment = 1, = 0, and the difference.
Compute the estimate of Mann-Whitney based on conditional CDF and PMF
Description
Compute the estimate of Mann-Whitney based on conditional CDF and PMF
Usage
estimate_mannwhitney(cdf_est, pmf_est)
Arguments
cdf_est |
Conditional CDF estimates |
pmf_est |
Conditional PMF estimates |
Value
Mann-Whitney point estimate
Get a treatment-specific estimate of the conditional PMF.
Essentially this is a wrapper function for fit_trt_spec_reg, which
fits the proportion odds model in a given treatment arm.
Description
Get a treatment-specific estimate of the conditional PMF.
Essentially this is a wrapper function for fit_trt_spec_reg, which
fits the proportion odds model in a given treatment arm.
Usage
estimate_pmf(
out,
treat,
covar,
out_levels,
out_form = NULL,
out_model,
treat_prob_est,
stratify = FALSE,
return_models = TRUE,
...
)
Arguments
out |
A |
treat |
A |
covar |
A |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
treat_prob_est |
Estimated probability of treatments, output from call
to |
stratify |
Boolean indicating whether to use nonparametric maximum likelihood
(i.e., a stratified estimator). If |
return_models |
If |
... |
Other options (not used). |
Value
A list with fm the fitted model for treatment 1 and 0 (or, if
!return_models then NULL) and pmf the estimated PMF
under treatment 1 and 0 evaluated on each observation.
Estimate probability of receiving each level of treatment
Description
Estimate probability of receiving each level of treatment
Usage
estimate_treat_prob(treat, covar, treat_form, return_models)
Arguments
treat |
A |
covar |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
return_models |
If |
Value
A list where the first element is estimate of Pr(treat = 1 | covar)
for covar equal to inputted values of covar
and second element is estimate of Pr(treat = 0 | covar)
for covar equal to inputted values of covar
Compute the estimate of the weighted mean parameter based on estimated PMF in each treatment arm.
Description
Compute the estimate of the weighted mean parameter based on estimated PMF in each treatment arm.
Usage
estimate_wmean(
pmf_est,
treat,
out,
out_levels,
out_weights,
treat_prob_est,
return_cov = TRUE
)
Arguments
pmf_est |
List of treatment-specific PMF estimates. |
treat |
A |
out |
A |
out_levels |
A |
out_weights |
A vector of |
treat_prob_est |
Estimated probability of treatments, output from call
to |
return_cov |
If |
Value
List with estimates of treatment-specific means and difference in means.
If return_cov = TRUE, also includes covariance matrix estimates.
Get the covariance matrix for beta
Description
Get the covariance matrix for beta
Usage
evaluate_beta_cov(cdf_est, theta_cov)
Arguments
cdf_est |
Estimated CDFs |
theta_cov |
Covariance matrix for CDF estimates |
Value
Estimated covariance matrix for log-odds ratio parameters
Compute the estimated gradient of the Mann-Whitney parameter. Needed to derive standard error for Wald confidence intervals.
Description
Compute the estimated gradient of the Mann-Whitney parameter. Needed to derive standard error for Wald confidence intervals.
Usage
evaluate_mannwhitney_gradient(cdf_est, pmf_est)
Arguments
cdf_est |
Conditional CDF estimates |
pmf_est |
Conditional PMF estimates |
Value
3-length vector for delta method calculus
Get eif estimates for treatment-specific CDF
Description
Get eif estimates for treatment-specific CDF
Usage
evaluate_marg_cdf_eif(cdf_est, treat_prob_est, treat, out, out_levels)
Arguments
cdf_est |
Estimated conditional CDF for |
treat_prob_est |
Estimated propensity for |
treat |
A |
out |
A |
out_levels |
A |
Value
a list of eif estimates
Evaluate pointwise confidence interval for marginal CDF.
Description
Evaluate pointwise confidence interval for marginal CDF.
Usage
evaluate_marg_cdf_ptwise_ci(marg_cdf_est, marg_cdf_eif, alpha)
Arguments
marg_cdf_est |
The point estimate of the marginal CDF distribution |
marg_cdf_eif |
The EIF estimates for the marginal CDF estimates |
alpha |
Confidence intervals have nominal level 1- |
Value
List by treatment of simultaneous confidence intervals
Evaluate simultaneous confidence interval for marginal PMF or CDF.
Description
Evaluate simultaneous confidence interval for marginal PMF or CDF.
Usage
evaluate_marg_dist_simul_ci(
marg_dist_est,
marg_dist_eif,
alpha,
remove_last = FALSE
)
Arguments
marg_dist_est |
The point estimate of the marginal CDF/PMF distribution |
marg_dist_eif |
The EIF estimates for the marginal CDF/PMF estimates |
alpha |
Confidence intervals have nominal level 1- |
remove_last |
Should the last level be removed? Should be set equal to
|
Value
List by treatment of simultaneous confidence intervals
Get eif estimates for treatment-specific PMF
Description
Get eif estimates for treatment-specific PMF
Usage
evaluate_marg_pmf_eif(pmf_est, treat_prob_est, treat, out, out_levels)
Arguments
pmf_est |
Estimated conditional PMF for |
treat_prob_est |
Estimated propensity for |
treat |
A |
out |
A |
out_levels |
A |
Value
a list of eif estimates
Evaluate pointwise confidence interval for marginal PMF.
Description
Evaluate pointwise confidence interval for marginal PMF.
Usage
evaluate_marg_pmf_ptwise_ci(marg_pmf_est, marg_pmf_eif, alpha)
Arguments
marg_pmf_est |
The point estimate of the marginal PMF distribution |
marg_pmf_eif |
The EIF estimates for the marginal PMF estimates |
alpha |
Confidence intervals have nominal level 1- |
Value
List by treatment of simultaneous confidence intervals
get a covariance matrix for the estimated CDF
Description
get a covariance matrix for the estimated CDF
Usage
evaluate_theta_cov(cdf_est, treat_prob_est, treat, out, out_levels)
Arguments
cdf_est |
The estimates of the treatment-specific CDFs |
treat_prob_est |
List of estimated probability of treatments, output from call
to |
treat |
A |
out |
A |
out_levels |
A |
Value
Estimated covariance matrix for CDF estimates
Get a matrix of eif estimates for treatment-specific PMF
Description
Get a matrix of eif estimates for treatment-specific PMF
Usage
evaluate_trt_spec_pmf_eif(
trt_spec_pmf_est,
trt_spec_prob_est,
trt_level,
treat,
out,
out_levels
)
Arguments
trt_spec_pmf_est |
Estimated conditional PMF for |
trt_spec_prob_est |
Estimated propensity for |
trt_level |
Treatment level |
treat |
A |
out |
A |
out_levels |
A |
Value
a matrix of EIF estimates
get a matrix of eif estimates for the treatment-specific CDF estimates
Description
get a matrix of eif estimates for the treatment-specific CDF estimates
Usage
evaluate_trt_spec_theta_eif(
trt_spec_cdf_est,
trt_spec_prob_est,
trt_level,
treat,
out,
out_levels
)
Arguments
trt_spec_cdf_est |
Estimated conditional CDF for |
trt_spec_prob_est |
Estimated propensity for |
trt_level |
Treatment level |
treat |
A |
out |
A |
out_levels |
A |
Value
matrix of EIF estimates for CDF.
Helper function to fit a treatment specific outcome regression.
If there are more than 2 observed levels of the outcome for the
specified treatment arm, then polr is used from the MASS
package. Otherwise logistic regression is used. In both cases,
inverse probability of treatment weights are included in the regression.
If there are levels of the outcome that are not observed in this treatment
group, then 0's are added in. The function returns a matrix with named columns
corresponding to each outcome (ordered numerically). The entries
represent the estimated covariate-conditional treatment-specific PMF.
Description
Helper function to fit a treatment specific outcome regression.
If there are more than 2 observed levels of the outcome for the
specified treatment arm, then polr is used from the MASS
package. Otherwise logistic regression is used. In both cases,
inverse probability of treatment weights are included in the regression.
If there are levels of the outcome that are not observed in this treatment
group, then 0's are added in. The function returns a matrix with named columns
corresponding to each outcome (ordered numerically). The entries
represent the estimated covariate-conditional treatment-specific PMF.
Usage
fit_trt_spec_reg(
trt_level,
trt_spec_prob_est,
out,
treat,
covar,
out_levels,
out_form = NULL,
out_model,
stratify,
...
)
Arguments
trt_level |
Which level of treatment to fit the proportional odds model for |
trt_spec_prob_est |
A vector of estimates of Pr( |
out |
A |
treat |
A |
covar |
A |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
stratify |
Boolean indicating whether to use nonparametric maximum likelihood
(i.e., a stratified estimator). If |
... |
Other options (not used). |
Get a response from model formula
Description
Get a response from model formula
Usage
getResponseFromFormula(formula, data)
Arguments
formula |
The model formula |
data |
The data frame associated with the model |
Compute one log odds based on a given data set.
Description
Compute one log odds based on a given data set.
Usage
get_one_logodds(treat, covar, treat_form, out_model, out, out_levels, out_form)
Arguments
treat |
A |
covar |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
out |
A |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
Value
Estimated log odds for these input data.
Compute one estimate of Mann-Whitney parameter based on a given data set.
Description
Compute one estimate of Mann-Whitney parameter based on a given data set.
Usage
get_one_mannwhitney(
treat,
covar,
treat_form,
out,
out_levels,
out_form,
out_model
)
Arguments
treat |
A |
covar |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out |
A |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
Estimate of Mann-Whitney parameter for these input data.
Compute one estimate of the marginal CDF/PMF on a given data set.
Description
Compute one estimate of the marginal CDF/PMF on a given data set.
Usage
get_one_marg_dist(
treat,
covar,
treat_form,
out_model,
out,
out_levels,
out_form
)
Arguments
treat |
A |
covar |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
out |
A |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
Value
List of estimated cdf/pmf for these input data.
Compute one weighted mean based on a given data set.
Description
Compute one weighted mean based on a given data set.
Usage
get_one_wmean(
treat,
covar,
treat_form,
out,
out_levels,
out_form,
out_model,
out_weights
)
Arguments
treat |
A |
covar |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out |
A |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
out_weights |
A vector of |
Compute jackknife log-odds estimates.
Description
Compute jackknife log-odds estimates.
Usage
jack_logodds(treat, covar, out, treat_form, out_model, out_levels, out_form)
Arguments
treat |
A |
covar |
A |
out |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
Value
Jackknife estimated log-odds
Compute Mann-Whitney log-odds estimates.
Description
Compute Mann-Whitney log-odds estimates.
Usage
jack_mannwhitney(
treat,
covar,
out,
treat_form,
out_levels,
out_form,
out_model
)
Arguments
treat |
A |
covar |
A |
out |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
Jackknife estimate of Mann-Whitney parameter
Compute jackknife distribution estimates.
Description
Compute jackknife distribution estimates.
Usage
jack_marg_cdf(treat, covar, out, treat_form, out_levels, out_form, out_model)
Arguments
treat |
A |
covar |
A |
out |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
Jackknife estimated distributions
Compute jackknife weighted mean estimates.
Description
Compute jackknife weighted mean estimates.
Usage
jack_wmean(
treat,
covar,
out,
treat_form,
out_levels,
out_form,
out_weights,
out_model
)
Arguments
treat |
A |
covar |
A |
out |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_weights |
A vector of |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
Jackknife-estimated weighted mean
Marginalize over empirical distribution to obtain marginal treatment-specific CDF estimate.
Description
Marginalize over empirical distribution to obtain marginal treatment-specific CDF estimate.
Usage
marginalize_cdf(cdf_est)
Arguments
cdf_est |
Estimates of treatment-specific conditional CDF. |
Marginalize over empirical distribution to obtain marginal treatment-specific PMF estimate.
Description
Marginalize over empirical distribution to obtain marginal treatment-specific PMF estimate.
Usage
marginalize_pmf(pmf_est)
Arguments
pmf_est |
Estimates of treatment-specific conditional PMF. |
Get one bootstrap computation of the log odds parameters.
Description
Get one bootstrap computation of the log odds parameters.
Usage
one_boot_logodds(
treat,
covar,
out,
treat_form,
out_levels,
out_form,
out_model
)
Arguments
treat |
A |
covar |
A |
out |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
Estimates of log odds for a particular bootstrap sample.
Get one bootstrap computation of the Mann-Whitney parameter.
Description
Get one bootstrap computation of the Mann-Whitney parameter.
Usage
one_boot_mannwhitney(
treat,
covar,
out,
treat_form,
out_levels,
out_form,
out_model
)
Arguments
treat |
A |
covar |
A |
out |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
Estimates of Mann-Whitney parameter for a particular bootstrap sample.
Get one bootstrap computation of the CDF and PMF estimates
Description
Get one bootstrap computation of the CDF and PMF estimates
Usage
one_boot_marg_dist(
treat,
covar,
out,
treat_form,
out_levels,
out_form,
out_model
)
Arguments
treat |
A |
covar |
A |
out |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
Estimates of CDF and PMF for a particular bootstrap sample.
Get one bootstrap computation of the weighted mean parameters.
Description
Get one bootstrap computation of the weighted mean parameters.
Usage
one_boot_wmean(
treat,
covar,
out,
treat_form,
out_levels,
out_form,
out_weights,
out_model
)
Arguments
treat |
A |
covar |
A |
out |
A |
treat_form |
The right-hand side of a regression formula for the working model of treatment probability as a function of covariates |
out_levels |
A |
out_form |
The right-hand side of a regression formula for the working proportional odds model. NOTE: THIS FORMULA MUST NOT SUPPRESS THE INTERCEPT. |
out_weights |
A vector of |
out_model |
Which R function should be used to fit the proportional odds
model. Options are |
Value
Estimates of weighted mean for a particular bootstrap sample.
Print the output of a "drord" object.
Description
Print the output of a "drord" object.
Usage
## S3 method for class 'drord'
plot(
x,
treat_labels = c(1, 0),
dist = "pmf",
out_labels = if (dist == "pmf") { x$out_levels } else {
x$out_levels[-length(x$out_levels)] },
...
)
Arguments
x |
A |
treat_labels |
Labels for the treatment variables (treat = 1 followed by treat = 0). |
dist |
Which distribution to plot. Valid options are |
out_labels |
Labels for the ordered outcome levels. If |
... |
Other arguments (not used) |
Value
A list with named entries plot (a ggplot2 object) and plot_data,
the data.frame from which the plot is made. The latter is included for additional
modifications to the plot that are desired.
Predict method for a POplugin object
Description
Predict method for a POplugin object
Usage
## S3 method for class 'POplugin'
predict(object, newdata = NULL)
Arguments
object |
An object of class |
newdata |
A |
Value
A data frame with nrow = number of rows in newdata (or the orignal data frame) and with the number of columns equal to the number of levels of the outcome observed in the original data frame
Print the output of a "drord" object.
Description
Print the output of a "drord" object.
Usage
## S3 method for class 'drord'
print(x, ci = "bca", ...)
Arguments
x |
A |
ci |
Which confidence interval should be printed. Defaults to BCa,
but it BCa was not computed in call to |
... |
Other arguments (not used) |
Trimmed logistic function
Description
Trimmed logistic function
Usage
trimmed_logit(x)
Arguments
x |
A numeric between 0 and 1 |
Compute a Wald confidence interval for the weighted mean
Description
Compute a Wald confidence interval for the weighted mean
Usage
wald_ci_wmean(wmean_est, alpha)
Arguments
wmean_est |
The estimated weighted means + estimated covariance matrix. |
alpha |
Level of confidence interval. |
Value
matrix with treatment-specific weighted mean CIs and CI for difference.