mvdalab (version 1.4)

ScoreContrib: 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.

Value

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

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.

References

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

Examples

# NOT RUN {
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)
# }
# 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)
# }
# NOT RUN {
#PCA Model
pc1 <- pcaFit(Penta[, -1], ncomp = 2)
Score.Contributions1 <- ScoreContrib(pc1, obs1 = 1, obs2 = 3)
plot(Score.Contributions1)
# }