auc_var {R2ROC}

auc_var function

Description

This function estimates var(AUC(y~x[,v1])) where AUC is the Area Under ROC curve of the model, y is N by 1 matrix having the dependent variable, and x is N by M matrix having M explanatory variables. v1 indicates the ith column in the x matrix (v1 can be multiple values between 1 - M, see Arguments below)

Usage

auc_var(dat, v1, nv, kv)

Arguments

dat

N by (M+1) matrix having variables in the order of cbind(y,x)

v1

This can be set as v1=c(1), v1=c(1,2) or possibly with more values

nv

Sample size

kv

Population prevalence

Value

This function will test the null hypothesis for AUC. To get the test statistics for AUC(y~x[,v1]). The outputs are listed as follows.

auc

AUC

var

Variance of AUC

upper_auc

Upper limit of 95% CI for AUC

lower_auc

Lower limit of 95% CI for AUC

p

two tailed p-value

p_one_tail

one tailed p-value

Examples

#To get the AUC for AUC(y=x[,1]) 

dat=dat1 #(this example embedded within the package)
nv=length(dat$V1)
kv=sum(dat$V1)/length(dat$V1)# pop. prevalence estimated from data
#R2ROC also allows users to estimate AUC using pre-adjusted phenotype
#In that case, users need to specify kv
#eg. kv=0.10 for dat2 (dat2 embedded within the package) 
v1=c(1)
output=auc_var(dat,v1,nv,kv)

#R2ROC output
#output$auc (AUC)
#0.7390354

#output$var (variance of AUC)
#7.193337e-05

#output$upper_auc (upper limit of 95% CI for AUC)
#0.7556589

#output$lower_auc (lower limit of 95% CI for AUC)
#0.7224119

#output$p
#9.28062e-175 (two tailed P-value for the AUC is significantly
#different from 0.5)

#output$p_one_tail (one tailed P-value for the AUC is significantly 
#different from 0.5)
#4.64031e-175