Plot.PCA_English: Graphs of the Principal Components Analysis (PCA).

In MVar: Multivariate Analysis

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, axes = TRUE, class = NA, 
         classcolor = NA, posleg = 2, boxleg = TRUE, savptc = FALSE,
         width = 3236, height = 2000, res = 300, 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.
axes	Plots the X and Y axes (default = TRUE).
class	Vector with names of data classes.
classcolor	Vector with the colors of the classes.
posleg	0 with no caption, 1 for caption in the left upper corner, 2 for caption in the right upper corner (default), 3 for caption in the right lower corner, 4 for caption in the left lower corner.
boxleg	Puts the frame in the caption (default = TRUE).
savptc	Saves graphics images to files (default = FALSE).
width	Graphics images width when savptc = TRUE (defaul = 3236).
height	Graphics images height when savptc = TRUE (default = 2000).
res	Nominal resolution in ppi of the graphics images when savptc = TRUE (default = 300).
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(iris) # data set

data <- iris[,1:4]

cls <- iris[,5] # data class

pc <- PCA(data, 2)

tit <- c("Scree-plot","Observations","Correlations")

cls <- as.character(iris[,5])

Plot.PCA(PC = pc, titles = tit, xlabel = NA, ylabel = NA,
         color = TRUE, linlab = NA, savptc = FALSE, size = 1.1,
         posleg = 2, boxleg = FALSE, class = cls, axes = TRUE,
         classcolor = c("blue3","red","goldenrod3"),
         width = 3236, height = 2000, res = 300, casc = FALSE)
         

MVar documentation built on Aug. 19, 2023, 5:12 p.m.