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
[[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.
