pcares function

Results of PCA decomposition

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

pcares(...)

Arguments

• ...: all arguments supported by ldecomp.

Returns

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.

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.

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))

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.