ScoreContrib: Generates a score contribution plot

In mvdalab: Multivariate Data Analysis Laboratory

Generates a score contribution plot

Description

Generates a the Score Contribution Graph both mvdareg and mvdapca objects.

Usage

ScoreContrib(object, ncomp = 1:object$ncomp, obs1 = 1, obs2 = NULL)

Arguments

object	an object of class mvdareg or mvdapca.
ncomp	the number of components to include in the model (see below).
obs1	the first observaion(s) in the score(s) comparison.
obs2	the second observaion(s) in the score(s) comparison.

Details

ScoreContrib is used to generates the score contributions for both PLS and PCA models. Up to two groups of score(s) can be selected. If only one group is selected, the contribution is measured to the model average. For PLS models the PCA loadings are replaced with the PLS weights.

Value

The output of ScoreContrib is a matrix of score contributions for the specified observation(s).

Author(s)

Nelson Lee Afanador ([e-mail])

References

MacGregor, Process Monitoring and Diagnosis by Multiblock PLS Methods, May 1994 Vol. 40, No. 5 AIChE Journal.

Examples

data(Penta)
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1],
               ncomp = 2, validation = "none")
Score.Contributions1 <- ScoreContrib(mod1, ncomp = 1:2, obs1 = 1, obs2 = 3)
plot(Score.Contributions1, ncomp = 2)

## Not run: 
data(Penta)
mod2 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1],
               ncomp = 2, validation = "none")
Score.Contributions2 <- ScoreContrib(mod2, obs1 = 1, obs2 = 3)
plot(Score.Contributions2)
Score.Contributions3 <- ScoreContrib(mod1, obs1 = c(1, 3), obs2 = c(5:10))
plot(Score.Contributions3)

## End(Not run)

###  PLS MODEL FIT WITH method = 'wrtpls' and validation = 'none', i.e. WRT-PLS is performed ###
## Not run: 
mod3 <- plsFit(Sepal.Length ~., scale = TRUE, data = iris,
               method = "wrtpls", validation = "none") #ncomp is ignored
Score.Contributions4 <- ScoreContrib(mod3, ncomp = 1:5, obs1 = 1, obs2 = 3)
plot(Score.Contributions4, ncomp = 5)

## End(Not run)

#PCA Model
pc1 <- pcaFit(Penta[, -1], ncomp = 2)
Score.Contributions1 <- ScoreContrib(pc1, obs1 = 1, obs2 = 3)
plot(Score.Contributions1)

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