Momocs (version 1.1.6)

PCA: Principal component analysis on Coe objects

Description

Performs a PCA on Coe objects, using prcomp.

Usage

PCA(x, scale., center, fac)
# S3 method for OutCoe
PCA(x, scale. = FALSE, center = TRUE, fac)
# S3 method for OpnCoe
PCA(x, scale. = FALSE, center = TRUE, fac)
# S3 method for LdkCoe
PCA(x, scale. = FALSE, center = TRUE, fac)
# S3 method for TraCoe
PCA(x, scale. = TRUE, center = TRUE, fac)
# S3 method for default
PCA(x, scale. = TRUE, center = TRUE, fac = data.frame())
as.PCA(x, fac)

Arguments

x

a Coe object or an appropriate object (eg prcomp) for as.PCA

scale.

logical whether to scale the input data

center

logical whether to center the input data

fac

any factor or data.frame to be passed to as.PCA and for use with plot.PCA

Value

a 'PCA' object on which to apply plot.PCA

Details

By default, methods on Coe object do not scale the input data but center them. There is also a generic method (eg for traditional morphometrics) that centers and scales data.

See Also

Other multivariate: CLUST, KMEANS, LDA, MANOVA_PW, MANOVA

Examples

data(bot)
bot.f <- efourier(bot, 12)
bot.p <- PCA(bot.f)
bot.p
plot(bot.p, morpho=FALSE)
plot(bot.p, 'type')

data(olea)
op <- npoly(olea, 5)
op.p <- PCA(op)
op.p
plot(op.p, 1, morpho=TRUE)

data(wings)
wp <- fgProcrustes(wings, tol=1e-4)
wpp <- PCA(wp)
wpp
plot(wpp, 1)

# "foreign prcomp"
head(iris)
iris.p <- prcomp(iris[, 1:4])
iris.p <- as.PCA(iris.p, iris[, 5])
class(iris.p)
plot(iris.p, 1)