caret

install.packages("AppliedPredictiveModeling",repos = "http://cran.us.r-project.org")

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##  /var/folders/4z/9kr7k7jd1q7gc792xjkrwf040000gn/T//RtmpnbRBjm/downloaded_packages

library(AppliedPredictiveModeling)
install.packages("caret",repos = "http://cran.us.r-project.org")

## 
## The downloaded binary packages are in
##  /var/folders/4z/9kr7k7jd1q7gc792xjkrwf040000gn/T//RtmpnbRBjm/downloaded_packages

library(caret)

## Warning: package 'caret' was built under R version 3.3.2

## Loading required package: lattice

## Loading required package: ggplot2

## Warning: package 'ggplot2' was built under R version 3.3.2

ScatterPlot Matrix

transparentTheme(trans = .4)
featurePlot(x = iris[, 1:4], 
            y = iris$Species, 
            plot = "pairs",
            ## Add a key at the top
            auto.key = list(columns = 3))

Overlayed Density Plots

transparentTheme(trans = .9)
featurePlot(x = iris[, 1:4], 
            y = iris$Species,
            plot = "density", 
            ## Pass in options to xyplot() to 
            ## make it prettier
            scales = list(x = list(relation="free"), 
                          y = list(relation="free")), 
            adjust = 1.5, 
            pch = "|", 
            layout = c(4, 1), 
            auto.key = list(columns = 3))

Overlayed Density Plots

transparentTheme(trans = .9)
featurePlot(x = iris[, 1:4], 
            y = iris$Species, 
            plot = "box", 
            ## Pass in options to bwplot() 
            scales = list(y = list(relation="free"),
                          x = list(rot = 90)),  
            layout = c(4,1 ), 
            auto.key = list(columns = 2))

Scatter Plots

When the predictors are continuous, featurePlot can be used to create scatter plots of each of the predictors with the outcome. For example:

install.packages("mlbench",repos = "http://cran.us.r-project.org")

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library(mlbench)
data(BostonHousing)
regVar <- c("age", "lstat", "tax")
str(BostonHousing[, regVar])

## 'data.frame':    506 obs. of  3 variables:
##  $ age  : num  65.2 78.9 61.1 45.8 54.2 58.7 66.6 96.1 100 85.9 ...
##  $ lstat: num  4.98 9.14 4.03 2.94 5.33 ...
##  $ tax  : num  296 242 242 222 222 222 311 311 311 311 ...

theme1 <- trellis.par.get()
theme1$plot.symbol$col = rgb(.2, .2, .2, .4)
theme1$plot.symbol$pch = 16
theme1$plot.line$col = rgb(1, 0, 0, .7)
theme1$plot.line$lwd <- 2
trellis.par.set(theme1)
featurePlot(x = BostonHousing[, regVar], 
            y = BostonHousing$medv, 
            plot = "scatter", 
            layout = c(3, 1))

featurePlot(x = BostonHousing[, regVar], 
            y = BostonHousing$medv, 
            plot = "scatter",
            type = c("p", "smooth"),
            span = .5,
            layout = c(3, 1))