| Type: | Package |
| Title: | ROC for Cross Validation Results |
| Version: | 1.2 |
| Date: | 2019-05-10 |
| Author: | Ben Sherwood [aut, cre] |
| Depends: | R (≥ 3.0.0), glmnet, parallel, pROC |
| Maintainer: | Ben Sherwood <ben.sherwood@ku.edu> |
| Description: | Cross validate large genetic data while specifying clinical variables that should always be in the model using the function cv(). An ROC plot from the cross validation data with AUC can be obtained using rocplot(), which also can be used to compare different models. Framework was built to handle genetic data, but works for any data. |
| ByteCompile: | TRUE |
| License: | MIT + file LICENSE |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2019-05-10 15:51:48 UTC; bsherwoo |
| Repository: | CRAN |
| Date/Publication: | 2019-05-10 16:20:03 UTC |
roccv: A package for creating ROC plots on cross validated data
Description
roccv: A package for creating ROC plots on cross validated data
Cross validation results for a model
Description
Cross validation results for a model
Usage
cv(clinical_x = NULL, genomic_x = NULL, y = NULL, data = NULL,
clinical_formula = NULL, family = "binomial", folds = NULL, k = 10,
fit_method = "glm", method_name = NULL, n.cores = 1, ...)
Arguments
clinical_x |
clinical variables that will always be included in the model |
genomic_x |
genomic variables that will be penalized if a penalized model is used |
y |
response variables |
data |
dataframe if clinical formula is used |
clinical_formula |
formula for clinical variables |
family |
gaussian, binomial or poisson |
folds |
predefined partions for cross validation |
k |
number of cross validation folds. A value of k=n is leave one out cross validation. |
fit_method |
glm or glmnet used to fit the model |
method_name |
tracking variable to include in return dataframe |
n.cores |
Number of cores to be used |
... |
additional commmands to glm or cv.glmnet |
Value
returns a dataframe of predicted values and observed values. In addition, method_name is recorded if that variable is defined.
Author(s)
Ben Sherwood <ben.sherwood@ku.edu>
Examples
x <- matrix(rnorm(800),ncol=8)
y <- runif(100) < exp(1 + x[,1] + x[,5])/(1+exp(1 + x[,1] + x[,5]))
cv_results <- cv(x,y=y,method_name="without_formula")
combined_data <- data.frame(y=y,x1=x[,1],x5=x[,5])
gx <- x[,c(2,3,4,6,7,8)]
cvf <- cv(genomic_x=gx,clinical_formula=y~x1+x5,data=combined_data,method_name="with_form")
Cross validation on fold i
Description
Cross validation on fold i
Usage
fit_pred_fold(i, x, y, folds, fit_method, family, non_pen_vars = NULL, ...)
Arguments
i |
target partition |
x |
matrix of predictors |
y |
vector of responses |
folds |
defines how data is seperated into folds for cross validation |
fit_method |
model being used to fit the data |
family |
family used to fit the data |
non_pen_vars |
index of variables that will not be penalized if glmnet is used |
... |
additional commmands to glm or cv.glmnet |
Value
returns predictions for partition i
Author(s)
Ben Sherwood <ben.sherwood@ku.edu>
Examples
folds_10 <- randomly_assign(100,10)
x <- matrix(rnorm(800),ncol=8)
y <- runif(100) < exp(1 + x[,1] + x[,5])/(1+exp(1 + x[,1] + x[,5]))
fold_1_results <- fit_pred_fold(1,x,y,folds_10,"glm","binomial")
fold_2_results <- fit_pred_fold(2,x,y,folds_10,"glm","binomial")
Assigns n samples into k groups
Description
Assigns n samples into k groups
Usage
randomly_assign(n, k)
Arguments
n |
sample size |
k |
number of groups |
Value
returns a vector of length n with a random assignment of entries from 1 to k
Author(s)
Ben Sherwood <ben.sherwood@ku.edu>
Examples
n <- 100
folds_10 <- randomly_assign(n,10)
folds_5 <- randomly_assign(n,5)
Create ROC plot from cross validation results
Description
Create ROC plot from cross validation results
Usage
rocplot(plot_data, ...)
Arguments
plot_data |
dataframe with columns: response, prediction and method |
... |
additional commmands plot.roc such as main |
Value
returns ROC plot
Author(s)
Ben Sherwood <ben.sherwood@ku.edu>
Examples
x <- matrix(rnorm(800),ncol=8)
y <- runif(100) < exp(1 + x[,1] + x[,5])/(1+exp(1 + x[,1] + x[,5]))
cv_results <- cv(x,y=y,method_name="without_formula")
combined_data <- data.frame(y=y,x1=x[,1],x5=x[,5])
gx <- x[,c(2,3,4,6,7,8)]
cvf <- cv(genomic_x=gx,clinical_formula=y~x1+x5,
data=combined_data,method_name="with_form")
total_results <- rbind(cv_results,cvf)
rocplot(total_results,main="rocplot test")