RFlocalfdr (version 0.9)

run.it.importances: run.it.importances

Description

run.it.importances

Usage

run.it.importances(qq, imp, debug.flag = 0, temp.dir = NULL)

Value

return a list contining

• "C_0.95" estimate of the cutoff "C" such that there are only null values to the left of C. Based on the 95th quantile of the density

• "cc" estimate of the cutoff "C" based on the procedure of Gauran, et al., 2918, Biometrics,

• "estimates_C_0.95" estimate of the parameters of the SN using the data up to the C estimate

• "estimates_cc" estimate of the parameters of the SN using the data up to the C estimate

• "fdr_0.95" estimate of the fdr curve using the SN from "estimates_C_0.95"

• "fdr_cc" estimate of the fdr curve using the SN from "estimates_cc"

• "x" the x values from plotQ

• "temp.dir" the temp directory for dubuggin

• "p0" the estmate of the proportion of null values (can be 1)

Arguments

qq

object retuned by plotQ

imp

"reduction in impurity" importances from a random forest model

debug.flag

either 0 (no debugging information), 1 or 2

temp.dir

if debug flag is >0 then information is written to temp.dir

Examples

data(imp20000)
imp <- log(imp20000$importances)
t2 <- imp20000$counts
plot(density((imp)))
hist(imp,col=6,lwd=2,breaks=100,main="histogram of importances")
res.temp <- determine_cutoff(imp, t2 ,cutoff=c(0,1,2,3),plot=c(0,1,2,3),Q=0.75,try.counter=1)
plot(c(0,1,2,3),res.temp[,3])
imp<-imp[t2 > 1]
qq <- plotQ(imp,debug.flag = 0)                                                          
ppp<-run.it.importances(qq,imp,debug=0)                                                       
aa<-significant.genes(ppp,imp,cutoff=0.2,debug.flag=0,do.plot=2, use_95_q=TRUE)                           
length(aa$probabilities) #11#                                                          
names(aa$probabilities)

# \donttest{
library(RFlocalfdr.data)
data(ch22)                                                                                 
? ch22                                                                                     
#document how the data set is created                                                      
plot(density(log(ch22$imp)))                                                               
t2 <-ch22$C                                                                                
imp<-log(ch22$imp)                                                                         
#Detemine a cutoff to get a unimodal density.                                              
res.temp <- determine_cutoff(imp, t2 ,cutoff=c(1,10,20,30),plot=c(1,10,20,30),Q=0.75)      
plot(c(1,2,3,4),res.temp[,3])                                                              
                                                                                           
res.temp <- determine_cutoff(imp, t2 ,cutoff=c(25,30,35,40),plot=c(25,30,35,40),Q=0.75)    
plot(c(25,30,35,40),res.temp[,3])                                                          
imp<-imp[t2 > 30]
qq <- plotQ(imp,debug.flag = 0)                                                          
ppp<-run.it.importances(qq,imp,debug=0)                                                       
aa<-significant.genes(ppp,imp,cutoff=0.2,debug.flag=0,do.plot=2)                           
length(aa$probabilities) # 6650                                                            
aa<-significant.genes(ppp,imp,cutoff=0.05,debug.flag=0,do.plot=2)                          
length(aa$probabilities) # 3653
# }