Thank you for your response Max. Is there some literature that you make
that statement? I am confused as I have seen many publications that
contain R^2 and Q^2 following PLSDA analysis. The analysis usually is to
discriminate groups (ie. classification). Are these papers incorrect in
using these statistics?
Regards,
Charles
On Sat, Mar 2, 2013 at 10:39 PM, Max Kuhn <mxk...@gmail.com> wrote:
> Charles,
>
> You should not be treating the classes as numeric (is virginica really
> three times setosa?). Q^2 and/or R^2 are not appropriate for classification.
>
> Max
>
>
> On Sat, Mar 2, 2013 at 5:21 PM, Charles Determan Jr <deter...@umn.edu>wrote:
>
>> 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
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide
>> http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
>
>
> --
>
> Max
>
--
Charles Determan
Integrated Biosciences PhD Student
University of Minnesota
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