c060 (version 0.3-0)

aggregation.auc: Determine the area under the ROC curve for a fitted model

Description

Evaluate the area under the ROC curve for a fitted model on new data. To be used as argument aggregation.fun in peperr call.

Usage

aggregation.auc(full.data=NULL, response, x, model, cplx=NULL,  
type=c("apparent", "noinf"), fullsample.attr = NULL, ...)

Value

Scalar, indicating the area under the ROC curve.

Arguments

full.data

passed from peperr, but not used for calculation.

response

vector of binary response.

x

n*p matrix of covariates.

model

model fitted as returned by a fit.fun, as used in a call to peperr.

cplx

passed from peperr, but not necessary for calculation.

type

character.

fullsample.attr

passed from peperr, but not necessary for calculation.

...

additional arguments, passed to predict function.

Author

Thomas Hielscher \

Details

Area under the ROC curve is calculated based on internal glmnet:::auc function from package glmnet.

See Also

peperr

Examples

if (FALSE) {
# binomial model - classification

library(c060)
library(gridExtra)
library(ggplot2)

set.seed(0815)
x <- matrix(rnorm(100*20),100,20)
y <- sample(0:1,100,replace=TRUE)

peperr_obj <- peperr(response=y, x=x, fit.fun=fit.glmnet, args.fit=list(family="binomial"),
           complexity=complexity.glmnet, args.complexity=list(nfolds=10, family="binomial"),
           trace=F, RNG="fixed",seed=0815,
#           aggregation.fun=c060:::aggregation.misclass,                  
#           aggregation.fun=c060:::aggregation.brier,                  
           aggregation.fun=c060:::aggregation.auc,                  
           indices=resample.indices(n=nrow(x), sample.n = 100, method = "sub632"))

tmp   <- data.frame(grp="",error=unlist(peperr_obj$sample.error)) 
errs  <- data.frame(error=c(perr(peperr_obj,"resample"),
         perr(peperr_obj,"632p"),perr(peperr_obj,"app"),
         perr(peperr_obj,"nullmodel")), col  = c("red","blue","green","brown"),
         row.names=c("mean\nout-of-bag",".632plus","apparent","null model"))
                 
p     <- ggplot(tmp, aes(grp,error))
pg    <- p + geom_boxplot(outlier.colour = rgb(0,0,0,0), outlier.size=0) +
         geom_jitter(position=position_jitter(width=.1)) + 
         theme_bw() + scale_y_continuous("AUC") +  scale_x_discrete("") +
         geom_hline(aes(yintercept=error, colour=col), data=errs, show_guide=T) + 
         scale_colour_identity("error type", guide = "legend", breaks=errs$col,
         labels=rownames(errs)) +
         ggtitle("AUC \n in bootstrap samples ")                       

p2     <- ggplot(data.frame(complx=peperr_obj$sample.complexity), aes(x=complx))
pg2    <- p2 + geom_histogram(binwidth = 0.02, fill = "white", colour="black") +
          theme_bw()+  xlab(expression(lambda)) +
          ylab("frequency") + 
          geom_vline(xintercept=peperr_obj$selected.complexity, colour="red") + 
          ggtitle("Selected complexity \n in bootstrap samples") +
          ggplot2::annotate("text", x = 0.12, y = -0.5,
          label = "full data", colour="red", size=4)

grid.arrange(pg2, pg, ncol=2)

}