Plot3D: Plot PC 3D (3DPlot)
In mariytu/RegressionLibs: What the package does (short line)

Generate a 3D plot Generates a 3D graphic for a set of 3 columns of the data set.

1	Plot3D(data, columns, dependentVariable)

`data`	an object of class data frame with the data.
`columns`	an object of class "numeric" containing the list of columns that you want in your parallel plot.
`dependentVariable`	an object of class "numeric", "factor" or "integer" is a list of values containig the dependent variable.

#Example 1
iris.x <- iris[,1:4] # These are the independent variables
Species <- iris[,5] # This is the dependent variable

# 3D Plot of 3 first columns of data set
Plot3D(iris.x, c(1,2,3), Species)


#Example 2
iris.x <- iris[,1:4] # These are the independent variables
Species <- iris[,5] # This is the dependent variable

ir.pca <- prcomp(iris.x, center = TRUE, scale. = TRUE) # performin prcomp

# 3D Plot of 3 first columns of data set
Plot3D(as.data.frame(ir.pca$x), c(1,2,3), Species)


#Example 3
# Getting a clean data set (without missing values)
cars <- read.csv("https://dl.dropboxusercontent.com/u/12599702/autosclean.csv", sep = ";", dec = ",")
cars.x <- cars[,1:16] # These are the independent variables
cars.y <- cars[,17] # This is the dependent variable

# 3D Plot of 3 first columns of data set
Plot3D(cars.x, c(1,2,3), cars.y)


#Example 4
# Getting a clean data set (without missing values)
cars <- read.csv("https://dl.dropboxusercontent.com/u/12599702/autosclean.csv", sep = ";", dec = ",")
cars.x <- cars[,1:16] # These are the independent variables
cars.y <- cars[,17] # This is the dependent variable

cars.pca <- prcomp(cars.x, center = TRUE, scale. = TRUE) # performin prcomp

# 3D Plot of 3 first columns of data set
Plot3D(as.data.frame(cars.pca$x), c(1,2,3), cars.y)