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preProcess函數(shù)

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, ...)

參數(shù)

參數(shù)解釋

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)

舉個例子

dfTest3 <- iris[, -length(iris)]head(dfTest3, 3) centerDf <- preProcess(dfTest3) #默認中心化標準化 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變換 pre_Df <- predict(centerDf, dfTest3) head(pre_Df, 3)head(dfTest3, 3) centerDf <- preProcess(dfTest3, method = "range") #取值在0-1之間 pre_Df <- predict(centerDf, dfTest3) head(pre_Df, 3)

輸出：

> 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

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