MVar (version 2.0.4)

Plot.PP: Graphics of the Projection Pursuit (PP).

Description

Graphics of the Projection Pursuit (PP).

Usage

Plot.PP(PP, Titles = NA, xlabel = NA, ylabel = NA,
        PosLeg = 2, BoxLeg = TRUE, Color = TRUE, Label = FALSE,
        LinLab = NA, AxisVar = TRUE, Axis = TRUE, Casc = TRUE)

Arguments

PP

Data of the PP_Optimizer 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.

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).

Color

Colored graphics (default = TRUE).

Label

Puts the labels on observations (default = FALSE).

LinLab

Vector with the labels for the observations, if not set, assumes the default text.

AxisVar

Puts axes of rotation of the variables, only when DimProj > 1 (default = TRUE).

Axis

Plots the X and Y axes (default = TRUE).

Casc

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

Value

Graph of the evolution of the indices, and graphs whose data were reduced in two dimensions.

See Also

PP_Optimizer and PP_Index

Examples

# NOT RUN {
data(iris) # dataset

# Example 1 - Without the classes in the data
Data <- iris[,1:4]

FcIndex <- "kurtosismax" # index function

Dim <- 1 # dimension of data projection

Sphere <- TRUE # spherical data

Res <- PP_Optimizer(Data = Data, Class = NA, Findex = FcIndex,
                    OptMethod = "GTSA", DimProj = Dim, Sphere = Sphere, 
                    Weight = TRUE, Lambda = 0.1, r = 1, Cooling = 0.9, 
                    Eps = 1e-3, Maxiter = 500, Half = 30)
    
Plot.PP(Res, Titles = NA, PosLeg = 1, BoxLeg = FALSE, Color = TRUE,
        Label = FALSE, LinLab = NA, AxisVar = TRUE, Axis = TRUE,
        Casc = FALSE)


# Example 2 - With the classes in the data
Class <- iris[,5] # data class

Res <- PP_Optimizer(Data = Data, Class = Class, Findex = FcIndex,
                    OptMethod = "GTSA", DimProj = Dim, Sphere = Sphere, 
                    Weight = TRUE, Lambda = 0.1, r = 1, Cooling = 0.9, 
                    Eps = 1e-3, Maxiter = 500, Half = 30)

Tit <- c(NA,"Graph example") # titles for the graphics

Plot.PP(Res, Titles = Tit, PosLeg = 1, BoxLeg = FALSE, Color = TRUE,
        Label = FALSE, LinLab = Class, AxisVar = TRUE, Axis = TRUE,
        Casc = FALSE)
        

# Example 3 - Without the classes in the data, but informing 
#             the classes in the plot function
Res <- PP_Optimizer(Data = Data, Class = NA, Findex = "Moment",
                    OptMethod = "GTSA", DimProj = 2, Sphere = Sphere, 
                    Weight = TRUE, Lambda = 0.1, r = 1, Cooling = 0.9, 
                    Eps = 1e-3, Maxiter = 500, Half = 30)

Class <- c(rep("a",50),rep("b",50),rep("c",50)) # data class

Plot.PP(Res, Titles = NA, PosLeg = 1, BoxLeg = FALSE, Color = TRUE,
        Label = FALSE, LinLab = Class, AxisVar = TRUE, Axis = TRUE,
        Casc = FALSE)
        

# Example 4 - With the classes in the data, but not informed in plot function
Class <- iris[,5] # data class

Dim <- 2 # dimension of data projection

FcIndex <- "lda" # index function

Res <- PP_Optimizer(Data = Data, Class = Class, Findex = FcIndex,
                    OptMethod = "GTSA", DimProj = Dim, Sphere = Sphere, 
                    Weight = TRUE, Lambda = 0.1, r = 1, Cooling = 0.9, 
                    Eps = 1e-3, Maxiter = 500, Half = 30)

Tit <- c("",NA) # titles for the graphics

Plot.PP(Res, Titles = Tit, PosLeg = 1, BoxLeg = FALSE, Color = TRUE,
        Label = FALSE, LinLab = NA, AxisVar = TRUE, Axis = TRUE,
        Casc = FALSE)
# }