Plot.PCA {MVar}R Documentation

Graphs of the Principal Components Analysis (PCA).

Description

Graphs of the Principal Components Analysis (PCA).

Usage

Plot.PCA(PC, titles = NA, xlabel = NA, ylabel = NA,
         size = 1.1, grid = TRUE, color = TRUE, linlab = NA,
         casc = TRUE)

Arguments

PC

Data of the PCA function.

titles

Titles of the graphics, if not set, assumes the default text.

xlabel

Names the X axis, if not set, assumes the default text.

ylabel

Names the Y axis, if not set, assumes the default text.

size

Size of the points in the graphs.

grid

Put grid on graphs (default = TRUE).

color

Colored graphics (default = TRUE).

linlab

Vector with the labels for the observations.

casc

Cascade effect in the presentation of the graphics (default = TRUE).

Value

Returns several graphs.

Author(s)

Paulo Cesar Ossani

Marcelo Angelo Cirillo

See Also

PCA

Examples

data(DataQuan) # set of quantitative data

Data <- DataQuan[,2:8]

rownames(Data) <- DataQuan[1:nrow(DataQuan),1]

PC <- PCA(Data, 2) # performs the PCA

Tit = c("Scree-plot","Graph of the Observations","Circle of Correlation")

Plot.PCA(PC, titles = Tit, xlabel = NA, ylabel = NA,
         color = TRUE, linlab = NA, casc = TRUE)

[Package MVar version 2.0.9 Index]