Description Usage Arguments Note See Also Examples
View source: R/DiagnosticsPlots.R
Generate a plot of theoretical quantiles v/s Standarized Residuals of a regression model.
1 | NormalQQ(diagnostic, dependentVariableName)
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diagnostic |
an object of class data frame with differents error types calculated for make the graph. |
dependentVariableName |
is an optional parameter. It's an string that contains de name of your dependent variable of your regression model. |
Part of this code, it's from http://librestats.com/2012/06/11/autoplot-graphical-methods-with-ggplot2/
diagnosticData, ResidualsFitted, StResidualsFitted, StResidualsLeverange
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 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | #Example 1
iris.x <- iris[,1:3] # These are the independent variables
Petal.Width <- iris[,4] # This is the dependent variable
ir.pca <- prcomp(iris.x, center = TRUE, scale. = TRUE) # Performing prcomp
PCA <- as.data.frame(ir.pca$x)
PC1 <- PCA[,1]
PC2 <- PCA[,2]
PC3 <- PCA[,3]
fit <- lm(Petal.Width ~ PC1 + PC2 + PC3, data = PCA) # Perfoming linear regression
diagnostic <- diagnosticData(fit) # Generating data for differents plots
ResidualsFitted(diagnostic, "Petal Width") # Generating Residuals v/s Fitted Values plot
StResidualsFitted(diagnostic, "Petal Width") #Generating Standarized Residuals v/s Fitted Values plot
NormalQQ(diagnostic, "Petal Width") # Generating Normal-QQ plot
StResidualsLeverange(diagnostic, "Petal Width") # Generating Leverange v/s Standarized Residuals plot
# Plots with a different colours palette
myPalette <- c("darkolivegreen4", "goldenrod1", "dodgerblue4")
ResidualsFitted(diagnostic, "Petal Width", colours = myPalette) # Generating Residuals v/s Fitted Values plot
StResidualsFitted(diagnostic, "Petal Width", colours = myPalette) #Generating Standarized Residuals v/s Fitted Values plot
StResidualsLeverange(diagnostic, "Petal Width",colours = myPalette) # Generating Leverange v/s Standarized Residuals plot
#Example 2
# Getting a clean data set (without missing values)
cars <- read.csv("https://dl.dropboxusercontent.com/u/12599702/autosclean.csv", sep = ";", dec = ",")
cars.x <- cars[,1:16] # These are the independent variables
cars.y <- cars[,17] # This is the dependent variable
cars.pca <- prcomp(cars.x, center = TRUE, scale. = TRUE)
PCA <- as.data.frame(cars.pca$x)
PC1 <- PCA[,1]
PC2 <- PCA[,2]
PC3 <- PCA[,3]
fit <- lm(cars.y ~ PC1 + PC2 + PC3, data = PCA) # Perfoming linear regression
diagnostic <- diagnosticData(fit) # Generating data for differents plots
ResidualsFitted(diagnostic, "Price") # Generating Residuals v/s Fitted Values plot
StResidualsFitted(diagnostic, "Price") #Generating Standarized Residuals v/s Fitted Values plot
NormalQQ(diagnostic, "Price") # Generating Normal-QQ plot
StResidualsLeverange(diagnostic, "Price") # Generating Leverange v/s Standarized Residuals plot
# Plots with a different colours palette
myPalette <- c("darkolivegreen4", "goldenrod1", "dodgerblue4")
ResidualsFitted(diagnostic, "Price", colours = myPalette) # Generating Residuals v/s Fitted Values plot
StResidualsFitted(diagnostic, "Price", colours = myPalette) #Generating Standarized Residuals v/s Fitted Values plot
StResidualsLeverange(diagnostic, "Price",colours = myPalette) # Generating Leverange v/s Standarized Residuals plot
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