multifluo (version 1.1)

pca: pca

Description

Calculates Principal Component Analysis with agreement ellipses

Usage

pca(data, zone = "zone", pixel = NULL)

Arguments

data

should contain a column named as zone, and another named pixel

zone

name of the column containing the zone

pixel

name of the column containing the pixel name (required when ellipses, individual projections or tests are asked in the PCA)

Value

A list containing

B

matrix of zone covariance

IndSup

supplementary individuals

EingenVectors

eigen vectors obtained by the PCA

EingenValues

eigen values obtained by the PCA

IndivCoord

coordinates of the individuals (here, zone means)

VarCoord

coordinates of the variables

NbdimSig

number of significant dimensions

References

Peltier, C., Visalli, M. and Schlich, P. (2015), Canonical Variate Analysis of Sensory Profiling Data. J Sens Stud, 30: 316 328. doi:10.1111/joss.12160

See Also

plotpca reshapimg

Examples

# NOT RUN {
data(df.scaled)
resPCA=pca(data=df.scaled[,-1], zone="zone",pixel="pixel")

# }