I have discovered on of my errors. The timematrix was unnecessary and an
unfortunate habit I brought from another package. The following provides
the same R2 values as it should, however, I still don't know how to
retrieve Q2 values. Any insight would again be appreciated:
library(caret)
library(pls)
data(iris)
#needed to convert to numeric in order to do regression
#I don't fully understand this but if I left as a factor I would get an
error following the summary function
iris$Species=as.numeric(iris$Species)
inTrain1=createDataPartition(y=iris$Species,
p=.75,
list=FALSE)
training1=iris[inTrain1,]
testing1=iris[-inTrain1,]
ctrl1=trainControl(method="cv",
number=10)
plsFit2=train(Species~.,
data=training1,
method="pls",
trControl=ctrl1,
metric="Rsquared",
preProc=c("scale"))
data(iris)
training1=iris[inTrain1,]
datvars=training1[,1:4]
dat.sc=scale(datvars)
pls.dat=plsr(as.numeric(training1$Species)~dat.sc,
ncomp=3, method="oscorespls", data=training1)
x=crossval(pls.dat, segments=10)
summary(x)
summary(plsFit2)
Regards,
Charles
On Sat, Mar 2, 2013 at 3:55 PM, Charles Determan Jr <deter...@umn.edu>wrote:
> Greetings,
>
> I have been exploring the use of the caret package to conduct some plsda
> modeling. Previously, I have come across methods that result in a R2 and
> Q2 for the model. Using the 'iris' data set, I wanted to see if I could
> accomplish this with the caret package. I use the following code:
>
> library(caret)
> data(iris)
>
> #needed to convert to numeric in order to do regression
> #I don't fully understand this but if I left as a factor I would get an
> error following the summary function
> iris$Species=as.numeric(iris$Species)
> inTrain1=createDataPartition(y=iris$Species,
> p=.75,
> list=FALSE)
>
> training1=iris[inTrain1,]
> testing1=iris[-inTrain1,]
>
> ctrl1=trainControl(method="cv",
> number=10)
>
> plsFit2=train(Species~.,
> data=training1,
> method="pls",
> trControl=ctrl1,
> metric="Rsquared",
> preProc=c("scale"))
>
> data(iris)
> training1=iris[inTrain1,]
> datvars=training1[,1:4]
> dat.sc=scale(datvars)
>
> n=nrow(dat.sc)
> dat.indices=seq(1,n)
>
> timematrix=with(training1,
> classvec2classmat(Species[dat.indices]))
>
> pls.dat=plsr(timematrix ~ dat.sc,
> ncomp=3, method="oscorespls", data=training1)
>
> x=crossval(pls.dat, segments=10)
>
> summary(x)
> summary(plsFit2)
>
> I see two different R2 values and I cannot figure out how to get the Q2
> value. Any insight as to what my errors may be would be appreciated.
>
> Regards,
>
> --
> Charles
>
--
Charles Determan
Integrated Biosciences PhD Student
University of Minnesota
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