discSurv (version 1.1.2)

fprUno: False Positive Rate Uno

Description

Estimates the predictive false positive rate (fpr) based on cross validation and generalized, linear models (see glm). The concept was suggested by Uno et al. (see references)

Usage

fprUno(timepoint, dataSet, trainIndices, survModelFormula, 
censModelFormula, linkFunc = "logit", idColumn = NULL)

Arguments

timepoint

Discrete time interval given that the false positive rate is evaluated (integer scalar)

dataSet

Original data in short format. Should be of class "data.frame".

trainIndices

List of Indices from original data used for training (list of integer vectors). The length of the list is equal to the number of cross validation samples.

survModelFormula

Formula of the discrete survival model. It is used in a generalized, linear model.

censModelFormula

Formula of the censoring model. It is used in a generalized, linear model. Usually this is done without covariates.

linkFunc

Link function of the generalized, linear model.

idColumn

Name of the column with identification numbers of persons. Default NULL means, that each row equals one person (no repeated measurements).

Value

• • Output:
  {List with objects:}
  • Output:
  {Data frame with two columns: "cutoff" gives the different marker values and "fpr" the false positive rates}
• Input:
• {A list of given argument input values (saved for reference)}

References

Hajime Uno and Tianxi Cai and Lu Tian and L. J. Wei, (2007), Evaluating Prediction Rules for t-Year Survivors With Censored Regression Models, Journal of the American Statistical Association Patrick J. Heagerty and Yingye Zheng, (2005), Survival Model Predictive Accuracy and ROC Curves, Biometrics 61, 92-105

See Also

tprUno, tprUnoShort, aucUno, concorIndex, createFolds, glm

Examples

# Example with cross validation and unemployment data 
library(Ecdat)
library(caret)
data(UnempDur)
summary(UnempDur$spell)

# Extract subset of data
set.seed(635)
IDsample <- sample(1:dim(UnempDur)[1], 100)
UnempDurSubset <- UnempDur [IDsample, ]
head(UnempDurSubset)
range(UnempDurSubset$spell)
set.seed(7550)
CVfolds <- createFolds (y=UnempDurSubset$spell, returnTrain=TRUE, k=2)

# Estimate false positive rate of time interval 7:
tryFPR <- fprUno (timepoint=7, dataSet=UnempDurSubset, trainIndices=CVfolds,  
survModelFormula=spell ~ age + logwage, censModelFormula=censor1 ~ 1, 
linkFunc="logit", idColumn=NULL)
tryFPR
plot(tryFPR)