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随机森林R语言.zip （4个子文件）

随机森林R语言

randomForest_4.6-6.tar.gz 75KB

随机森林回归.R 1KB

R_scripts_functions.zip 3KB

randomForest R语言说明.pdf 187KB

Package ‘randomForest’

March 25, 2018

Title Breiman and Cutler's Random Forests for Classiﬁcation and

Regression

Version 4.6-14

Date 2018-03-22

Depends R (>= 3.2.2), stats

Suggests RColorBrewer, MASS

Author Fortran original by Leo Breiman and Adele Cutler, R port by Andy Liaw and Matthew Wiener.

Description Classiﬁcation and regression based on a forest of trees using random in-

puts, based on Breiman (2001) <DOI:10.1023/A:1010933404324>.

Maintainer Andy Liaw <andy_liaw@merck.com>

License GPL (>= 2)

URL https://www.stat.berkeley.edu/~breiman/RandomForests/

NeedsCompilation yes

Repository CRAN

Date/Publication 2018-03-25 15:00:24 UTC

R topics documented:

classCenter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

combine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

getTree . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

grow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

importance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

imports85 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

MDSplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

na.roughﬁx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

outlier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

partialPlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

plot.randomForest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

predict.randomForest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2 classCenter

randomForest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

rfcv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

rfImpute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

rfNews . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

treesize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

tuneRF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

varImpPlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

varUsed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Index 29

classCenter Prototypes of groups.

Description

Prototypes are ‘representative’ cases of a group of data points, given the similarity matrix among

the points. They are very similar to medoids. The function is named ‘classCenter’ to avoid conﬂict

with the function prototype in the methods package.

Usage

classCenter(x, label, prox, nNbr = min(table(label))-1)

Arguments

x a matrix or data frame

label group labels of the rows in x

prox the proximity (or similarity) matrix, assumed to be symmetric with 1 on the

diagonal and in [0, 1] off the diagonal (the order of row/column must match that

of x)

nNbr number of nearest neighbors used to ﬁnd the prototypes.

Details

This version only computes one prototype per class. For each case in x, the nNbr nearest neighors

are found. Then, for each class, the case that has most neighbors of that class is identiﬁed. The pro-

totype for that class is then the medoid of these neighbors (coordinate-wise medians for numerical

variables and modes for categorical variables).

This version only computes one prototype per class. In the future more prototypes may be computed

(by removing the ‘neighbors’ used, then iterate).

Value

A data frame containing one prototype in each row.

combine 3

Author(s)

Andy Liaw

See Also

randomForest, MDSplot

Examples

data(iris)

iris.rf <- randomForest(iris[,-5], iris[,5], prox=TRUE)

iris.p <- classCenter(iris[,-5], iris[,5], iris.rf$prox)

plot(iris[,3], iris[,4], pch=21, xlab=names(iris)[3], ylab=names(iris)[4],

bg=c("red", "blue", "green")[as.numeric(factor(iris$Species))],

main="Iris Data with Prototypes")

points(iris.p[,3], iris.p[,4], pch=21, cex=2, bg=c("red", "blue", "green"))

combine Combine Ensembles of Trees

Description

Combine two more more ensembles of trees into one.

Usage

combine(...)

Arguments

... two or more objects of class randomForest, to be combined into one.

Value

An object of class randomForest.

Note

The confusion, err.rate, mse and rsq components (as well as the corresponding components in

the test compnent, if exist) of the combined object will be NULL.

Author(s)

Andy Liaw <andy\_liaw@merck.com>

See Also

randomForest, grow

4 getTree

Examples

data(iris)

rf1 <- randomForest(Species ~ ., iris, ntree=50, norm.votes=FALSE)

rf2 <- randomForest(Species ~ ., iris, ntree=50, norm.votes=FALSE)

rf3 <- randomForest(Species ~ ., iris, ntree=50, norm.votes=FALSE)

rf.all <- combine(rf1, rf2, rf3)

print(rf.all)

getTree Extract a single tree from a forest.

Description

This function extract the structure of a tree from a randomForest object.

Usage

getTree(rfobj, k=1, labelVar=FALSE)

Arguments

rfobj a randomForest object.

k which tree to extract?

labelVar Should better labels be used for splitting variables and predicted class?

Details

For numerical predictors, data with values of the variable less than or equal to the splitting point go

to the left daughter node.

For categorical predictors, the splitting point is represented by an integer, whose binary expansion

gives the identities of the categories that goes to left or right. For example, if a predictor has

four categories, and the split point is 13. The binary expansion of 13 is (1, 0, 1, 1) (because

13 = 1 ∗ 2

+ 0 ∗ 2

+ 1 ∗ 2

), so cases with categories 1, 3, or 4 in this predictor get sent

to the left, and the rest to the right.

Value

A matrix (or data frame, if labelVar=TRUE) with six columns and number of rows equal to total

number of nodes in the tree. The six columns are:

left daughter the row where the left daughter node is; 0 if the node is terminal

right daughter the row where the right daughter node is; 0 if the node is terminal

split var which variable was used to split the node; 0 if the node is terminal

split point where the best split is; see Details for categorical predictor

status is the node terminal (-1) or not (1)

prediction the prediction for the node; 0 if the node is not terminal

grow 5

Author(s)

Andy Liaw <andy\_liaw@merck.com>

See Also

randomForest

Examples

data(iris)

## Look at the third trees in the forest.

getTree(randomForest(iris[,-5], iris[,5], ntree=10), 3, labelVar=TRUE)

grow Add trees to an ensemble

Description

Add additional trees to an existing ensemble of trees.

Usage

## S3 method for class 'randomForest'

grow(x, how.many, ...)

Arguments

x an object of class randomForest, which contains a forest component.

how.many number of trees to add to the randomForest object.

... currently ignored.

Value

An object of class randomForest, containing how.many additional trees.

Note

The confusion, err.rate, mse and rsq components (as well as the corresponding components in

the test compnent, if exist) of the combined object will be NULL.

Author(s)

Andy Liaw <andy\_liaw@merck.com>

See Also

combine, randomForest

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