mvdalab (version 1.7)

sr: Selectivity Ratio

Description

This function calculates the Selectivity Ratio (sr) metric for an mvdareg object

Usage

sr(object, ncomps = object$ncomp)

Value

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

sr

selectivity ratio statistic (sr).

p.value

p-value of the sr statistic.

f.value

f-value of the sr statistic.

Significant

Assessment of statistical significance.

Note that hidden objects include the SR modeled matrix and error matrices.

Arguments

object

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

ncomps

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

Author

Nelson Lee Afanador ()

Details

sr 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 sr estimates are returned. Otherwise, if comps are given parameters for a model with only the requested component comps is returned.

References

O.M. Kvalheim, T.V. Karstang, Interpretation of latent-variable regression models. Chemom. Intell. Lab. Syst., 7 (1989), pp. 39:51

O.M. Kvalheim, Interpretation of partial least squares regression models by means of target projection and selectivity ratio plots. J. Chemom., 24 (2010), pp. 496:504

See Also

smc

Examples

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

if (FALSE) {
mod2 <- plsFit(Sepal.Length ~., scale = TRUE, data = iris,
               method = "wrtpls", validation = "none") #ncomp is ignored
plot(sr(mod2, ncomps = 2))
}