2. 可视化(The caret package)

1. 简介(The caret package )

2. 可视化

featurPlot是对lattice包作图的一个包装。例如：下列图表展示了用featurePlot函数画出的连续变量结果的图形。
对于分类的数据集可用iris数据来分析。
str(iris)

## 'data.frame':    150 obs. of  5 variables:##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

散点图矩阵

library(AppliedPredictiveModeling)transparentTheme(trans = 0.4)library(caret)featurePlot(x = iris[,1:4],             y = iris$Species,             plot = "pairs",             ## Add a key at the top             auto.key = list(columns = 3))

散点图矩阵加椭圆

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

覆盖密度图

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))

箱线图

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))

散点图
用Boston Housing数据进行回归：

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 ...

当预测子是连续的时候，featurePlot可对每个预测子及结果变量创建散点图。例如：

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

注意到x坐标刻度的不同，函数自动的使用scale = list(y = list(relation = "free"))，所以你不用加上它。我们也可以向lattice函数xyplot中传递其他选项。例如，我们可以通过传递新选项加入散点图平滑项：

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

degree和span选项控制平滑项的平滑度。