plotROC: Plot ROC curves

In LoneWolf6/UJ-Analysis: Creation and Analysis of User Journey Data

Description

This function plots ROC curves and AUC values based on the results of analyzeUJ function.

Usage

plotROC(values, type, err)

Arguments

values	the values created in analyzeUJ function for a specific method. Input could be, for example, result$performance$Lasso.
type	an optional character string defining the type of ROC curve desired. Default is 'avg'. avg: ROC curve averaged over all folds of k-fold cross-validation. each: ROC curves for each fold of k-fold cross-validation.
err	an optional logical value True or False. Defining if error bar around averaged ROC curve is desired (only if type=avg). Default is True.

Value

Function plots the desired ROC curve including AUC value.

Examples

data = data.frame('id'=rep(c(1:5), each=600),
                  'type'=rep(c('Var1', 'Var2', 'Var3'), times=1000),
                  'value'=sample(c(0,1), 3000, T),
                  'date'=rep(seq(as.Date("2000/1/1"), by = "day", length.out=60), each=50))


# use function to create rectangle version of user journey
dat = reshapeData(data, parallel=T, cores=2, na.rm=F)

# use function to analyze data
res = analyzeUJ(dat, target="Var1", missing="median/mode", task="classification", ROC=T)

# use plot function
plotROC(res$performance$Lasso, type="avg", err=T)
{
  }

LoneWolf6/UJ-Analysis documentation built on Sept. 16, 2020, 4:59 a.m.