Re: [R] help: pls package
wu sz writes:
trainSet = as.data.frame(scale(trainSet, center = T, scale = T))
trainSet.plsr = mvr(formula, ncomp = 14, data = trainSet, method =
kernelpls,
CV = TRUE, validation = LOO, model = TRUE, x = TRUE,
y = TRUE)
[Two side notes here:
1) scaling of the data (with its sd) should be performed inside the
cross-validation. In the current version of 'pls', one can use
cvplsr - crossval(plsr(y ~ scale(X), ncomp = 14, data = mydata),
length.seg = 1)
(However, 'crossval' is slower than the built-in cross-validation on
'mvr'/'plsr'. In the development version of the package, scaling
within the cross-validation has been implemented in the built-in
cross-validation. This will hopefully be published shortly.)
2) The 'CV' argument is from the earlier 'pls.pcr' package, and is no
longer used. It is silently ignored.]
i = 1; msep_element = c()
while(i = length(p)){
msep_element[,i] = (p[i]-y)^2
i = i + 1
}
Hmm... I don't see how you got that code to run. This should work, though:
msep_element - (p - y)^2
msep = colMeans(msep_element)
msep_sd = sd(msep_element)
You will get much closer to the true value with
sd(msep_element) / sqrt(length(y))
However, this will not produce an unbiased estimate of the sd of the
estimated MSEP, because it ignores the depencies between the
residuals. E.g., the residual when sample 1 is predicted is not
independent of the residual when sample 2 is predicted. In general, I
think, it will produce underestimated sds. The effect should be
largest for small data sets.
This is the reason the pls package currently doesn't estimate se of
cross-validated MSEPs. There is also the question of what the
estimated should be conditioned on: for leave-one-out
cross-validation, sd(MSEP | trainData) = 0.
[If someone knows how to calculate unbiased estimates of
cross-validated MSEPs, please let me know. :-)]
--
Bjørn-Helge Mevik
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[R] help: pls package
Hello,
I have a data set with 15 variables (first one is the response) and
1200 observations. Now I use pls package to do the plsr with cross
validation as below.
trainSet = as.data.frame(scale(trainSet, center = T, scale = T))
trainSet.plsr = mvr(formula, ncomp = 14, data = trainSet, method = kernelpls,
CV = TRUE, validation = LOO, model =
TRUE, x = TRUE,
y = TRUE)
after that I wish to obtain the value of se, estimated standard
errors of the estimates(cross validation), mentioned in the function
of MSEP, but not implemented yet, so I made the program by myself to
calculate it. The results I got seem not right, and I wonder which
step below is wrong.
y = trainSet.plsr$y
p = as.data.frame(trainSet.plsr$validation$pred)
i = 1; msep_element = c()
while(i = length(p)){
msep_element[,i] = (p[i]-y)^2
i = i + 1
}
msep = colMeans(msep_element)
msep_sd = sd(msep_element)
Then I compare msep with trainSet.plsr$validation$MSEP, which are
the same, but the values of msep_sd seem much larger than I
expected, is it the same as se?
Thank you,
Shengzhe
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] help: pls package
Hello,
I have a data set with 15 variables (first one is the response) and
1200 observations. Now I use pls package to do the plsr with cross
validation as below.
trainSet = as.data.frame(scale(trainSet, center = T, scale = T))
trainSet.plsr = mvr(formula, ncomp = 14, data = trainSet, method = kernelpls,
CV = TRUE, validation = LOO, model = TRUE, x = TRUE,
y = TRUE)
after that I wish to obtain the value of se, estimated standard
errors of the estimates(cross validation), mentioned in the function
of MSEP, but not implemented yet, so I made the program by myself to
calculate it. The results I got seem not right, and I wonder which
step below is wrong.
y = trainSet.plsr$y
p = as.data.frame(trainSet.plsr$validation$pred)
i = 1; msep_element = c()
while(i = length(p)){
msep_element[,i] = (p[i]-y)^2
i = i + 1
}
msep = colMeans(msep_element)
msep_sd = sd(msep_element)
Then I compare msep with trainSet.plsr$validation$MSEP, which are
the same, but the values of msep_sd seem much larger than I
expected, is it the same as se? If not, how to calculate se of
cross validation?
Thank you,
Shengzhe
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
