Addi Wei writes:
> Hello,
>I am having some trouble using a model I created from plsr (of train) to
> analyze each invididual R^2 of the 10 components against the test data. For
> example:
>
> mice1 <- plsr(response ~factors, ncomp=10 data=MiceTrain)
> R2(mice1)##this provides the corr
I figured out the answer. Since I have both the predicted and actual values,
I simply need to call the lm function, and summary to see R^2 which matches
what MOE provided.
myline.fit <-lm( y~x)
summary(myline.fit)
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Hello,
I am having some trouble using a model I created from plsr (of train) to
analyze each invididual R^2 of the 10 components against the test data. For
example:
mice1 <- plsr(response ~factors, ncomp=10 data=MiceTrain)
R2(mice1)##this provides the correct R2 for the Train data for 1
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