pcares: Results of PCA decomposition

In svkucheryavski/mdatools: Multivariate Data Analysis for Chemometrics

Results of PCA decomposition

Description

pcares is used to store and visualise results for PCA decomposition.

Usage

pcares(...)

Arguments

...	all arguments supported by ldecomp.

Details

In fact pcares is a wrapper for ldecomp - general class for storing results for linear decomposition X = TP' + E. So, most of the methods, arguments and returned values are inherited from ldecomp.

There is no need to create a pcares object manually, it is created automatically when build a PCA model (see pca) or apply the model to a new data (see predict.pca). The object can be used to show summary and plots for the results.

It is assumed that data is a matrix or data frame with I rows and J columns.

Value

Returns an object (list) of class pcares and ldecomp with following fields:

scores	matrix with score values (I x A).
residuals	matrix with data residuals (I x J).
T2	matrix with score distances (I x A).
Q	matrix with orthogonal distances (I x A).
ncomp.selected	selected number of components.
expvar	explained variance for each component.
cumexpvar	cumulative explained variance.

See Also

Methods for pcares objects:

print.pcares	shows information about the object.
summary.pcares	shows statistics for the PCA results.

Methods, inherited from ldecomp class:

plotScores.ldecomp	makes scores plot.
plotVariance.ldecomp	makes explained variance plot.
plotCumVariance.ldecomp	makes cumulative explained variance plot.
plotResiduals.ldecomp	makes Q vs. T2 distance plot.

Check also pca and ldecomp.

Examples

### Examples for PCA results class

library(mdatools)

## 1. Make a model for every odd row of People data
## and apply it to the objects from every even row

data(people)
x = people[seq(1, 32, 2), ]
x.new = people[seq(1, 32, 2), ]

model = pca(people, scale = TRUE, info = "Simple PCA model")
model = selectCompNum(model, 4)

res = predict(model, x.new)
summary(res)
plot(res)

## 1. Make PCA model for People data with autoscaling
## and full cross-validation and get calibration results

data(people)
model = pca(people, scale = TRUE, info = "Simple PCA model")
model = selectCompNum(model, 4)

res = model$calres
summary(res)
plot(res)

## 2. Show scores plots for the results
par(mfrow = c(2, 2))
plotScores(res)
plotScores(res, cgroup = people[, "Beer"], show.labels = TRUE)
plotScores(res, comp = c(1, 3), show.labels = TRUE)
plotScores(res, comp = 2, type = "h", show.labels = TRUE)
par(mfrow = c(1, 1))

## 3. Show residuals and variance plots for the results
par(mfrow = c(2, 2))
plotVariance(res, type = "h")
plotCumVariance(res, show.labels = TRUE)
plotResiduals(res, show.labels = TRUE, cgroup = people[, "Sex"])
plotResiduals(res, ncomp = 2, show.labels = TRUE)
par(mfrow = c(1, 1))

svkucheryavski/mdatools documentation built on Aug. 25, 2023, 12:27 p.m.