wroc.plot: Estimation of the ROC curve of logistic regression models...

In svyROC: Estimation of the ROC Curve and the AUC for Complex Survey Data

Estimation of the ROC curve of logistic regression models with complex survey data

Description

Plot the ROC curve of a logistic regression model considering sampling weights with complex survey data.

Usage

wroc.plot(
  x,
  print.auc = TRUE,
  print.cutoff = FALSE,
  col.cutoff = "red",
  cex.text = 0.75,
  round.digits = 4
)

Arguments

x	An object of class wroc obtained by means of the function wroc().
print.auc	A logical value. If TRUE, the value of the area under the ROCw curve (AUCw) is printed (default print.auc = TRUE).
print.cutoff	A logical value. If TRUE, the value of the optimal cut-off point, and the corresponding weighted estimates of the sensitivity and specificity parameters are printed (default print.cutoff = TRUE).
col.cutoff	A character string indicating the color in which the cut-off point is depicted. The default option is col.cutoff = "red".
cex.text	A numeric value indicating the size with which the information of the AUCw and optimal cut-off point is printed. The default option is cex.text = 0.75.
round.digits	A numeric value indicating the number of digits that will be employed when printing the information about the AUCw and optimal cut-off point. The default option is round.digits = 4.

Details

More information is given in the documentation of the wroc(), wauc{} and wocp() functions.

Value

a graph

Examples

data(example_data_wroc)

mycurve <- wroc(response.var = "y", phat.var = "phat", weights.var = "weights",
                data = example_data_wroc,
                tag.event = 1, tag.nonevent = 0,
                cutoff.method = "Youden")
wroc.plot(x = mycurve, print.auc = TRUE, print.cutoff = TRUE)

svyROC documentation built on Oct. 25, 2024, 9:07 a.m.