0
点赞
收藏
分享

微信扫一扫

R语言观察日志(part2)--preProcess函数

学习笔记,仅供参考,有错必纠,按需更新

PS:大量参考R的帮助文档,照搬英文不翻译

preProcess函数



所在包:caret



描述



Pre-processing transformation (centering, scaling etc.) can be estimated from the training data and applied to any data set with the same variables.



使用



preProcess(x, ...)

## Default S3 method:
preProcess(x, method = c("center", "scale"),
thresh = 0.95, pcaComp = NULL, na.remove = TRUE, k = 5,
knnSummary = mean, outcome = NULL, fudge = 0.2, numUnique = 3,
verbose = FALSE, freqCut = 95/5, uniqueCut = 10, cutoff = 0.9,
rangeBounds = c(0, 1), ...)

## S3 method for class 'preProcess'
predict(object, newdata, ...)



参数



参数

解释

x

a matrix or data frame. Non-numeric predictors are allowed but will be ignored.

method

a character vector specifying the type of processing. Possible values are “BoxCox”, “YeoJohnson”, “expoTrans”, “center”, “scale”, “range”, “knnImpute”, “bagImpute”, “medianImpute”, “pca”, “ica”, “spatialSign”, “corr”, “zv”, “nzv”, and “conditionalX” (see Details below)


举个例子



dfTest3 <- iris[, -length(iris)]

head(dfTest3, 3)
centerDf <- preProcess(dfTest3) #默认中心化标准化
pre_Df <- predict(centerDf, dfTest3)
head(pre_Df, 3)


head(dfTest3, 3)
centerDf <- preProcess(dfTest3, method = "center") #中心化
pre_Df <- predict(centerDf, dfTest3)
head(pre_Df, 3)



head(dfTest3, 3)
centerDf <- preProcess(dfTest3, method = "BoxCox") #BoxCox变换
pre_Df <- predict(centerDf, dfTest3)
head(pre_Df, 3)


head(dfTest3, 3)
centerDf <- preProcess(dfTest3, method = "range") #取值在0-1之间
pre_Df <- predict(centerDf, dfTest3)
head(pre_Df, 3)

输出:

> head(dfTest3, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
> head(pre_Df, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 -0.8976739 1.0156020 -1.335752 -1.311052
2 -1.1392005 -0.1315388 -1.335752 -1.311052
3 -1.3807271 0.3273175 -1.392399 -1.311052
>
>
> head(dfTest3, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
> head(pre_Df, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 -0.7433333 0.44266667 -2.358 -0.9993333
2 -0.9433333 -0.05733333 -2.358 -0.9993333
3 -1.1433333 0.14266667 -2.458 -0.9993333
>
>
> head(dfTest3, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
> head(pre_Df, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 1.629241 1.520660 1.4 -1.032115
2 1.589235 1.301297 1.4 -1.032115
3 1.547563 1.391905 1.3 -1.032115
>
>
> head(dfTest3, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 5.1 3.5 1.4 0.2
2 4.9 3.0 1.4 0.2
3 4.7 3.2 1.3 0.2
> head(pre_Df, 3)
Sepal.Length Sepal.Width Petal.Length Petal.Width
1 0.2222222 0.6250000 0.06779661 0.04166667
2 0.1666667 0.4166667 0.06779661 0.04166667
3 0.1111111 0.5000000 0.05084746 0.04166667


举报

相关推荐

0 条评论