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

## Description

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

## Usage

 `1` ```Plot3D(data, columns, dependentVariable) ```

## Arguments

 `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.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38``` ```#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) ```

mariytu/RegressionLibs documentation built on May 21, 2017, 6:49 p.m.