arunkumar1111 <akpbond...@gmail.com> writes: > How to get p-value and the standard error in PLS
There is (to my knowledge) no theory able to calculate p-values for the regression coefficients in PLS regression. Most practicioners use cross-validation to estimate the Root Mean Squared Error (RMSEP) and use that as a measure of the quality of the fit. PLS regression is typically used when you have many (hundreds, thousands, tens of thousands) of predictors, where individual p-values are not very useful. The pls package does implement the jackknife to estimate the variance/standard error of the regression coefficients. There is even a function to calculate p-values from that, but please _do_ read the warning in the documentation: the distribution of the "t values" used in the test is _unknown_. See the example in ?jack.test for how to use the jackknife. > I have used the following function to calculate PLS > > fit1 <- mvr(formula=Y~X1+X2+X3+X4, data=Dataset, comp=4) >From a previous message on this list, I see that each of these predictor terms (X1, ...) is a vector. Thus you have only 4 predictor variables, so it would probably be better to use Ordinary Least Squares (OLS) regression (the lm() function in R). There you get p-values automatically. Furthermore, a PLS regression with the same number of components as predictor variables is equivalent to OLS, so there seems no reason to use PLS at all in your case. -- Cheers, Bjørn-Helge Mevik ______________________________________________ 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.