I am trying to apply a function I wrote that uses the 'pls' package to make a model and then use it to predict several test set(in this case 9), returning the R2,RMSEP and prediction bias of each test set for n number of subset selected from the data frame. the function is
cpo<-function(data,newdata1,newdata2,newdata3,newdata4,newdata5,newdata6,newdata7,newdata8,newdata9){
data.pls<-plsr(protein~.,8,data=data,validation="LOO")#making a pls model
newdata1.pred<-predict(data.pls,8,newdata=newdata1) #using the model to predict test sets
newdata2.pred<-predict(data.pls,8,newdata=newdata2)
newdata3.pred<-predict(data.pls,8,newdata=newdata3)
newdata4.pred<-predict(data.pls,8,newdata=newdata4)
newdata5.pred<-predict(data.pls,8,newdata=newdata5)
newdata6.pred<-predict(data.pls,8,newdata=newdata6)
newdata7.pred<-predict(data.pls,8,newdata=newdata7)
newdata8.pred<-predict(data.pls,8,newdata=newdata8)
newdata9.pred<-predict(data.pls,8,newdata=newdata9)
pred.bias1<-mean(newdata1.pred-newdata1[742]) #calculating the prediction bias
pred.bias2<-mean(newdata2.pred-newdata2[742])
pred.bias3<-mean(newdata3.pred-newdata3[742]) #[742] reference values in column742
pred.bias4<-mean(newdata4.pred-newdata4[742])
pred.bias5<-mean(newdata5.pred-newdata5[742])
pred.bias6<-mean(newdata6.pred-newdata6[742])
pred.bias7<-mean(newdata7.pred-newdata7[742])
pred.bias8<-mean(newdata8.pred-newdata8[742])
pred.bias9<-mean(newdata9.pred-newdata9[742])
r<-c(R2(data.pls,"train"),RMSEP(data.pls,"train"),pred.bias1,
pred.bias2,pred.bias3,pred.bias4,pred.bias5,pred.bias6,
pred.bias7,pred.bias8,pred.bias9)
return(r)
}
selecting n number of subsets (based on an answer from my question[1]: Select several subsets by taking different row interval and appy function to all subsets and applying cpo function to each subset I tried
Edited based on @Gavin advice
FO03 <- function(data, nSubsets, nSkip){
outList <- vector("list", 11)
names(outList) <- c("R2train","RMSEPtrain", paste("bias", 1:9, sep = ""))
sub <- vector("list", length = nSubsets) # sub is the n number subsets created by selecting rows
names(sub) <- c( paste("sub", 1:nSubsets, sep = ""))
totRow <- nrow(data)
for (i in seq_len(nSubsets)) {
rowsToGrab <- seq(i, totRow, nSkip)
sub[[i]] <- data[rowsToGrab ,]
}
for(i in sub) { #for every subset in sub i want to apply cpo
outList[[i]] <- cpo(data=sub,newdata1=gag11p,newdata2=gag12p,newdata3=gag13p,
newdata4=gag21p,newdata5=gag22p,newdata6=gag23p,
newdata7=gag31p,newdata8=gag32p,newdata9=gag33p) #new data are test sets loaded in the workspace
}
return(outlist)
}
FOO3(GAGp,10,10)
when I try this I keep getting 'Error in eval(expr, envir, enclos) : object 'protein' not found' not found. Protein is used in the plsr formula of cpo, and is in the data set. I then tried to use the plsr function directly as seen below
FOO4 <- function(data, nSubsets, nSkip){
outList <- vector("list", 11)
names(outList) <- c("R2train","RMSEPtrain", paste("bias", 1:9, sep = ""))
sub <- vector("list", length = nSubsets)
names(sub) <- c( paste("sub", 1:nSubsets, sep = ""))
totRow <- nrow(data)
for (i in seq_len(nSubsets)) {
rowsToGrab <- seq(i, totRow, nSkip)
sub[[i]] <- data[rowsToGrab ,]
}
cal<-vector("list", length=nSubsets) #for each subset in sub make a pls model for protein
names(cal)<-c(paste("cal",1:nSubsets, sep=""))
for(i in sub) {
cal[[i]] <- plsr(protein~.,8,data=sub,validation="LOO")
}
return(outlist) # return is just used to end script and check if error still occurs
}
FOO4(gagpm,10,10)
When I tried this I get the same error 'Error in eval(expr, envir, enclos) : object 'protein' not found'. Any advice on how to deal with this and make the function work will be much appreciated.
I suspect the problem is immediately at the start of
FOO3()
:Not sure what you are trying to do when creating
outList
, butvector()
has two arguments and you seem to be assigning tor
a vector of numerics that you want R to use as thelength
argument tovector()
.Here you are using the object
data.pls
and this doesn't exist yet - and never will in the frame ofFOO3()
- it is only ever created incpo()
.Your second loop looks totally wrong - you are not assigning the output from
cpo()
to anything. I suspect you wanted:But that depends on what
subset
is etc. You also haven't got the syntax for this loop right. You havewhen it should be
And
subset
anddata
aren't great names as these are common R functions and modelling arguments.There are lots of problems with your code. Try to start simple and build up from there.
I have managed to achieved what i wanted using this, if there is a better way of doing it (i'm sure there must be) I'm eager to learn.This function preforms the following task
1. select "n" number of subsets from a dataframe
2. For each subset created, a plsr model is made
3. Each plsr model is used to predict 9 test sets
4. For each prediction, the prediction bias is calculated
The first part -selecting the subsets is based on the answer to my question Select several subsets by taking different row interval and appy function to all subsets I was then able to apply the function cpr2 to the subsets created using "lapply" instead of the "for' loop as was previously done. cpr2 is a modification of cpo, for which only data is supplied, and the new data to be predicted is used directly in the function as shown below.
I was able to use this to solve my problem, however since the amount of new data to be predicted was only nine, it was possible to list them out as i did. If there is a more generalized way to do this I'm interested in learning.