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Consider this call:
cv.plsr.res=cv.plsR(formula = y ~ ., data= my_data, scaleX = T, scaleY = T, nt=5, K=5, NK = 1)
summary.cv.plsRmodel(cv.plsr.res, verbose=F)
It works as expected.
Now consider:
nt_val = 5
And use nt_val instead of the value '5'
cv.plsr.res=cv.plsR(formula = y ~ ., data= my_data, scaleX = T, scaleY = T, nt=nt_val, K=5, NK = 1)
summary.cv.plsRmodel(cv.plsr.res, verbose=F)
gives an error. The 'offending' code is in:
kfolds2CVinfos_lm
nt <- as.numeric(as.character(pls_kfolds$call["nt"]))
The text was updated successfully, but these errors were encountered:
Consider this call:
cv.plsr.res=cv.plsR(formula = y ~ ., data= my_data, scaleX = T, scaleY = T, nt=5, K=5, NK = 1)
summary.cv.plsRmodel(cv.plsr.res, verbose=F)
It works as expected.
Now consider:
nt_val = 5
And use nt_val instead of the value '5'
cv.plsr.res=cv.plsR(formula = y ~ ., data= my_data, scaleX = T, scaleY = T, nt=nt_val, K=5, NK = 1)
summary.cv.plsRmodel(cv.plsr.res, verbose=F)
gives an error. The 'offending' code is in:
kfolds2CVinfos_lm
nt <- as.numeric(as.character(pls_kfolds$call["nt"]))
The text was updated successfully, but these errors were encountered: