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

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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

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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 (nelson.afanador@mvdalab.com)

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

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data(Penta)
## Number of bootstraps set to 500 to demonstrate flexibility
## Use a minimum of 1000 (default) for results that support bootstraping
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1], 
               ncomp = 2, validation = "oob", boots = 500)
smc.acfTest(mod1, ncomp = 2)

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