Significant Multivariate Correlation

Description

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

Usage

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

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.

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.

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.

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.

See Also

smc.acfTest, sr, vip

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(mod1)
plot(smc(mod1))

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