Plot.PP {MVar} | R Documentation |
Graphics of the Projection Pursuit (PP).
Plot.PP(PP, titles = NA, xlabel = NA, ylabel = NA, posleg = 2, boxleg = TRUE, size = 1.1, grid = TRUE, color = TRUE, linlab = NA, axesvar = TRUE, axes = TRUE, casc = TRUE)
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, |
boxleg |
Puts the frame in the caption (default = TRUE). |
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. |
axesvar |
Puts axes of rotation of the variables, only when dimproj > 1 (default = TRUE). |
axes |
Plots the X and Y axes (default = TRUE). |
casc |
Cascade effect in the presentation of the graphics (default = TRUE). |
Graph of the evolution of the indices, and graphs whose data were reduced in two dimensions.
Paulo Cesar Ossani
Marcelo Angelo Cirillo
PP_Optimizer
and PP_Index
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, linlab = NA, axesvar = TRUE, axes = 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, linlab = NA, axesvar = TRUE, axes = 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) Lin <- c(rep("a",50),rep("b",50),rep("c",50)) # data class Plot.PP(Res, titles = NA, posleg = 1, boxleg = FALSE, color = TRUE, linlab = Lin, axesvar = TRUE, axes = 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, linlab = NA, axesvar = TRUE, axes = TRUE, casc = FALSE)