mvdalab (version 1.7)

smc: Significant Multivariate Correlation

Description

This function calculates the significant multivariate correlation (smc) metric for an mvdareg object

Usage

smc(object, ncomps = object$ncomp, corrected = F)

Value

The output of smc is an smc summary detailing the following:

smc

significant multivariate correlation statistic (smc).

p.value

p-value of the smc statistic.

f.value

f-value of the smc statistic.

Significant

Assessment of statistical significance.

Arguments

object

an mvdareg or mvdapaca object, i.e. plsFit.

ncomps

the number of components to include in the model (see below).

corrected

whether there should be a correction of 1st order auto-correlation in the residuals.

Note that hidden objects include the smc modeled matrix and error matrices

Author

Nelson Lee Afanador ()

Details

smc is used to extract a summary of the significant multivariae correlation of a PLS model.

If comps is missing (or is NULL), summaries for all smc estimates are returned. Otherwise, if comps are given parameters for a model with only the requested component comps is returned.

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.

See Also

smc.acfTest, sr

Examples

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

###  PLS MODEL FIT WITH method = 'wrtpls' and validation = 'none', i.e. WRT-PLS is performed ###
if (FALSE) {
mod2 <- plsFit(Sepal.Length ~., scale = TRUE, data = iris,
               method = "wrtpls", validation = "none") #ncomp is ignored
plot(smc(mod2, ncomps = 2))
}