smc.acfTest: Test of the Residual Significant Multivariate Correlation...

In mvdalab: Multivariate Data Analysis Laboratory

Test of the Residual Significant Multivariate Correlation Matrix for the presence of Autocorrelation

Description

This function peforms a 1st order test of the Residual Significant Multivariate Correlation Matrix in order to help determine if the smc should be performed correcting for 1st order autocorrelation.

Usage

smc.acfTest(object, ncomp = object$ncomp)

Arguments

object	an object of class mvdareg, i.e. plsFit.
ncomp	the number of components to include in the acf assessment

Details

This function computes a test for 1st order auto correlation in the smc residual matrix.

Value

The output of smc.acfTest is a list detailing the following:

variable	variable for whom the test is being performed
ACF	value of the 1st lag of the ACF
Significant	Assessment of the statistical significance of the 1st order lag

Author(s)

Nelson Lee Afanador ([e-mail])

References

Thanh N. Tran, Nelson Lee Afanador, Lutgarde M.C. Buydens, Lionel Blanchet, Interpretation of variable importance in Partial Least Squares with Significance Multivariate Correlation (sMC). Chemom. Intell. Lab. Syst. 2014; 138: 153:160.

Nelson Lee Afanador, Thanh N. Tran, Lionel Blanchet, Lutgarde M.C. Buydens, Variable importance in PLS in the presence of autocorrelated data - Case studies in manufacturing processes. Chemom. Intell. Lab. Syst. 2014; 139: 139:145.

Examples

data(Penta)
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1], 
               ncomp = 2, validation = "loo")
smc.acfTest(mod1, ncomp = 2)

mvdalab documentation built on Oct. 6, 2022, 1:05 a.m.