sr: Selectivity Ratio

In mvdalab: Multivariate Data Analysis Laboratory

Selectivity Ratio

Description

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

Usage

sr(object, ncomps = object$ncomp)

Arguments

object	an mvdareg or mvdapaca object, i.e. plsFit.
ncomps	the number of components to include in the model (see below).

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.

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.

Author(s)

Nelson Lee Afanador ([e-mail])

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))

## Not run: 
mod2 <- plsFit(Sepal.Length ~., scale = TRUE, data = iris,
               method = "wrtpls", validation = "none") #ncomp is ignored
plot(sr(mod2, ncomps = 2))

## End(Not run)

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