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Last data update: 2014.03.03

R: prediction function for tsp.randomForest

predict.tsp.randomForest

R Documentation

prediction function for tsp.randomForest

Description

prediction function for tsp.randomForest

Usage

## S3 method for class 'tsp.randomForest'
predict(object, newdata, type = "response", norm.votes = TRUE, predict.all = FALSE, proximity = FALSE, nodes = FALSE, cutoff, ...)

Arguments

`object`	a tsp.randomForest object
`newdata`	new data matrix
`type`	one of response, prob. or votes, indicating the type of output: predicted values, matrix of class probabilities, or matrix of vote counts. class is allowed, but automatically converted to ”response", for backward compatibility.
`norm.votes`	Should the vote counts be normalized (i.e., expressed as fractions)?
`predict.all`	Should the predictions of all trees be kept?
`proximity`	Should proximity measures be computed?
`nodes`	Should the terminal node indicators (an n by ntree matrix) be return? If so, it is in the ”nodes" attribute of the returned object.
`cutoff`	A vector of length equal to number of classes. The 'winning' class for an observation is the one with the maximum ratio of proportion of votes to cutoff.
`...`	not used.

Author(s)

Xiaolin Yang, Han Liu

References

randomForest package.

Examples

library(randomForest)
x=matrix(rnorm(100*20),100,20)
y=rbinom(100,1,0.5)
y=as.factor(y)
fit=tsp.randomForest(x,y)
predict(fit,x[1:10,])

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(BigTSP)
Loading required package: glmnet
Loading required package: Matrix
Loading required package: foreach
Loaded glmnet 2.0-5

Loading required package: tree
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
Loading required package: gbm
Loading required package: survival
Loading required package: lattice
Loading required package: splines
Loading required package: parallel
Loaded gbm 2.1.1
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/BigTSP/predict.tsp.randomForest.Rd_%03d_medium.png", width=480, height=480)
> ### Name: predict.tsp.randomForest
> ### Title: prediction function for tsp.randomForest
> ### Aliases: predict.tsp.randomForest
> ### Keywords: ~kwd1 ~kwd2
> 
> ### ** Examples
> 
> library(randomForest)
> x=matrix(rnorm(100*20),100,20)
> y=rbinom(100,1,0.5)
> y=as.factor(y)
> fit=tsp.randomForest(x,y)
> predict(fit,x[1:10,])
 1  2  3  4  5  6  7  8  9 10 
 1  0  0  0  0  1  1  1  1  1 
Levels: 0 1
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>