tsp.randomForest: Classification with Random Forest based on Top Scoring Pairs

Description

Classification with Random Forest based on Top Scoring Pairs

Usage

tsp.randomForest(x, y = NULL, xtest = NULL, ytest = NULL, ntree = 500, type = "classification", mtry = if (!is.null(y) && !is.factor(y)) max(floor(ncol(x)/3), 1) else floor(sqrt(ncol(x))), replace = TRUE, classwt = NULL, cutoff, strata, sampsize = if (replace) nrow(x) else ceiling(0.632 * nrow(x)), nodesize = if (!is.null(y) && !is.factor(y)) 5 else 1, maxnodes = NULL, importance = FALSE, localImp = FALSE, nPerm = 1, proximity = FALSE, oob.prox = proximity, norm.votes = TRUE, do.trace = FALSE, keep.forest = !is.null(y) && is.null(xtest), keep.inbag = FALSE, ...)

Arguments

a data frame or a matrix of predictors, or a formula describing the model to be fitted

A response vector. If omitted, tsp.randomForest will run in unsupervised mode.

xtest

a data frame or matrix (like x) containing predictors for the test set.

ytest

response for the test set.

ntree

Number of trees to grow.

type

turn on the ''classification" mode in ''randomForest".

mtry

Number of top scoring pairs randomly sampled as candidates at each split.

replace

Should sampling of cases be done with or without replacement?

classwt

Priors of the classes. Need not add up to one. Ignored for regression.

cutoff

(Classification only) 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. Default is 1/k where k is the number of classes (i.e., majority vote wins).

strata

A (factor) variable that is used for stratified sampling.

sampsize

Size(s) of sample to draw. For classification, if sampsize is a vector of the length the number of strata, then sampling is stratified by strata, and the elements of sampsize indicate the numbers to be drawn from the strata.

nodesize

Minimum size of terminal nodes. Setting this number larger causes smaller trees to be grown (and thus take less time).

maxnodes

Maximum number of terminal nodes trees in the forest can have.

importance

Should importance of top scoring pairs be assessed?

localImp

Should casewise importance measure be computed?

nPerm

Number of times the OOB data are permuted per tree for assessing top scoring pair importance.

proximity

Should proximity measure among the rows be calculated?

oob.prox

Should proximity be calculated only on ''out-of-bag" data?

norm.votes

If TRUE (default), the final result of votes are expressed as fractions. If FALSE, raw vote counts are returned (useful for combining results from different runs). Ignored for regression.

do.trace

If set to TRUE, give a more verbose output as randomForest is run. If set to some integer, then running output is printed for every do.trace trees.

keep.forest

If set to FALSE, the forest will not be retained in the output object. If xtest is given, defaults to FALSE.

keep.inbag

Should an n by ntree matrix be returned that keeps track of which samples are ''in-bag" in which trees (but not how many times, if sampling with replacement)

...

Additional arguments.

Value

call: the original call to randomForest
type: one of regression, classification, or unsupervised.
predicted: the predicted values of the input data based on out-of-bag samples.
importance: a matrix with nclass + 2 (for classification) or two (for regression) columns. For classification, the first nclass columns are the class-specific measures computed as mean descrease in accuracy. The nclass + 1st column is the mean descrease in accuracy over all classes. The last column is the mean decrease in Gini index. For Regression, the first column is the mean decrease in accuracy and the second the mean decrease in MSE. If importance=FALSE, the last measure is still returned as a vector.
importanceSD: The ``standard errors'' of the permutation-based importance measure. For classification, a p by nclass + 1 matrix corresponding to the first nclass + 1 columns of the importance matrix. For regression, a length p vector.
localImp: a p by n matrix containing the casewise importance measures, the [i,j] element of which is the importance of i-th variable on the j-th case. NULL if localImp=FALSE.
ntree: number of trees grown.
mtry: number of predictors sampled for spliting at each node.
forest: (a list that contains the entire forest; NULL if randomForest is run in unsupervised mode or if keep.forest=FALSE.
err.rate: (classification only) vector error rates of the prediction on the input data, the i-th element being the (OOB) error rate for all trees up to the i-th.
confusion: (classification only) the confusion matrix of the prediction (based on OOB data).
votes: (classification only) a matrix with one row for each input data point and one column for each class, giving the fraction or number of (OOB) `votes' from the random forest.
oob.times: number of times cases are `out-of-bag' (and thus used in computing OOB error estimate)
proximity: if proximity=TRUE when randomForest is called, a matrix of proximity measures among the input (based on the frequency that pairs of data points are in the same terminal nodes).
mse: (regression only) vector of mean square errors: sum of squared residuals divided by n.
rsq: (regression only) ``pseudo R-squared'': 1 - mse / Var(y).
test: if test set is given (through the xtest or additionally ytest arguments), this component is a list which contains the corresponding predicted, err.rate, confusion, votes (for classification) or predicted, mse and rsq (for regression) for the test set. If proximity=TRUE, there is also a component, proximity, which contains the proximity among the test set as well as proximity between test and training data.

References

Breiman, L. (2001), Random Forests, Machine Learning Breiman, L. (2002), "Manual On Setting Up, Using, And Understanding Random Forests V3.1", http://oz.berkeley.edu/users/breiman/Using_random_forests_V3.1.pdf.

Examples

Run this code

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,])
plot(fit)