| plotQ {RFlocalfdr} | R Documentation |
plotQ
Description
produces a plot showing the q values
q_95, the 95th quantile of the data
q using the penalized selection method of Gauran et.al 2018
Usage
plotQ(imp, debug.flag = 0, temp.dir = NULL, try.counter = 3)
Arguments
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 |
try.counter |
where to explain this? |
Details
We estiamte a value "q" such that: to the left of "q", the density is composed solely of NULL importance values to the right of "q" we have a density that is a mixture of null and non-null importance values. The method of Gauran et.al 2018 may not work in cases where the data distribution is not well modelled by a skew-normal. The q_95 value can be uses as a workaround in these case. In many cases they will be very similar
Value
df, contains x and y, midpoints and counts from a histogram of imp
final.estimates_C_0.95, the output from the fitting routine nlsLM in minpack.lm, where df has been truncated at the value C_0.95 (the 0.95 quantile of the skew-Normal distribution fitted to the imp histogram
final.estimates_cc, as for final.estimates_C_0.95 but with cc determined by the procedure of Gauran et.al 2018
temp.dir, the directory where debugging information may be written
C_0.95, the 0.95 quantile of the skew-Normal distribution fitted to the imp histogram
cc, determined by the procedure of Gauran et.al 2018
fileConn, a file connectin for writing debugging information
f_fit, a spline fit to the histogram
ww the minimum value of the local fdr
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)
library(RFlocalfdr.data)
data(ch22)
?ch22
#document how the data set is created
plot(density(log(ch22$imp)))
t2 <-ch22$C
imp<-log(ch22$imp)
#Detemine a cutoff to get a unimodal density.
# This was calculated previously. See determine_cutoff
imp<-imp[t2 > 30]
qq <- plotQ(imp,debug.flag = 0)
data(smoking)
?smoking
y<-smoking$y
smoking_data<-smoking$rma
y.numeric <-ifelse((y=="never-smoked"),0,1)
library(ranger)
rf1 <-ranger::ranger(y=y.numeric ,x=smoking_data,importance="impurity",seed=123, num.trees = 10000,
classification=TRUE)
t2 <-count_variables(rf1)
imp<-log(rf1$variable.importance)
plot(density(imp),xlab="log importances",main="")
cutoffs <- c(2,3,4,5)
res.con<- determine_cutoff(imp,t2,cutoff=cutoffs,plot=c(2,3,4,5))
plot(cutoffs,res.con[,3],pch=15,col="red",cex=1.5,ylab="max(abs(y - t1))")
cutoffs[which.min(res.con[,3])]
temp<-imp[t2 > 3]
temp <- temp - min(temp) + .Machine$double.eps
qq <- plotQ(temp)
ppp<-run.it.importances(qq,temp,debug.flag = 0)
aa<-significant.genes(ppp,temp,cutoff=0.05,debug.flag=0,do.plot=TRUE,use_95_q=TRUE)
length(aa$probabilities) # 17