corrected.wauc {svyROC} | R Documentation |
Corrected estimate of the AUC based on replicate weights.
Description
Optimism correction of the AUC of logistic regression models with complex survey data based on replicate weights methods.
Usage
corrected.wauc(
data = NULL,
formula,
tag.event = NULL,
tag.nonevent = NULL,
weights.var = NULL,
strata.var = NULL,
cluster.var = NULL,
design = NULL,
method = c("dCV", "JKn", "RB"),
dCV.method = c("average", "pooling"),
RB.method = c("subbootstrap", "bootstrap"),
k = 10,
R = 1,
B = 200
)
Arguments
data |
A data frame which, at least, must incorporate information on the columns
|
formula |
Formula of the model for which the AUC needs to be corrected.
The models are fitted by means of |
tag.event |
A character string indicating the label used to indicate the event of interest in |
tag.nonevent |
A character string indicating the label used for non-event in |
weights.var |
A character string indicating the name of the column with sampling weights.
It could be |
strata.var |
A character string indicating the name of the column with strata identifiers.
It could be |
cluster.var |
A character string indicating the name of the column with cluster identifiers.
It could be |
design |
An object of class |
method |
A character string indicating the method to be applied to define replicate weights and correct the AUC.
Choose between: |
dCV.method |
Only applies for the |
RB.method |
Only applies for the |
k |
A numeric value indicating the number of folds to be defined.
Default is |
R |
A numeric value indicating the number of times the sample is partitioned. Default is |
B |
A numeric value indicating the number of bootstrap resamples. Default is |
Details
See Iparragirre and Barrio (2024) for more information on the AUC correction methods and their performance.
Value
The output object of this function is a list of 5 elements containing the following information:
-
corrected.AUCw
: the corrected estimate of the weighted AUC. -
correction.method
: the selected correction method. -
formula
: formula of the model that has been fitted. -
tags
: a list containing two elements with the following information:-
tag.event
: a character string indicating the event of interest. -
tag.nonevent
: a character string indicating the non-event.
-
-
call
: an object saving the information about the way in which the function has been run.
References
Iparragirre, A., Barrio, I. (2024). Optimism Correction of the AUC with Complex Survey Data. In: Einbeck, J., Maeng, H., Ogundimu, E., Perrakis, K. (eds) Developments in Statistical Modelling. IWSM 2024. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-031-65723-8_7
Examples
data(example_variables_wroc)
mydesign <- survey::svydesign(ids = ~cluster, strata = ~strata,
weights = ~weights, nest = TRUE,
data = example_variables_wroc)
m <- survey::svyglm(y ~ x1 + x2 + x3 + x4 + x5 + x6, design = mydesign,
family = quasibinomial())
phat <- predict(m, newdata = example_variables_wroc, type = "response")
myaucw <- wauc(response.var = example_variables_wroc$y, phat.var = phat,
weights.var = example_variables_wroc$weights)
# Correction of the AUCw:
set.seed(1)
res <- corrected.wauc(data = example_variables_wroc,
formula = y ~ x1 + x2 + x3 + x4 + x5 + x6,
tag.event = 1, tag.nonevent = 0,
weights.var = "weights", strata.var = "strata", cluster.var = "cluster",
method = "dCV", dCV.method = "pooling", k = 10, R = 20)
# Or equivalently:
set.seed(1)
res <- corrected.wauc(design = mydesign,
formula = y ~ x1 + x2 + x3 + x4 + x5 + x6,
tag.event = 1, tag.nonevent = 0,
method = "dCV", dCV.method = "pooling", k = 10, R = 20)