predict.snpRF: predict method for snpRF objects

Description

Prediction of test data using the modified random forest algorithm implemented in snpRF.

Usage

## S3 method for class 'snpRF':
predict(object, newdata.autosome=NULL, newdata.xchrom=NULL, 
                xchrom.names=NULL, newdata.covar=NULL, type = "response",
                norm.votes = TRUE, predict.all=FALSE, proximity = FALSE, 
                nodes=FALSE, cutoff, ...)

Arguments

object

an object of class snpRF, as that created by the function snpRF.

newdata.autosome

A matrix of autosomal markers with each column corresponding to a SNP coded as the count of a particular allele (i.e. 0,1 or 2), and each row corresponding to a sample/individual.(Note: If all newdata.*

newdata.xchrom

A matrix of X chromosome markers, each marker coded as two adjacent columns, alleles of a marker are coded as 0 or 1 for carrying a particular allele. Although males only have one X-chromosome, their markers are coded as

xchrom.names

A vector of names for markers (1 name per marker) in the newdata.xchrom matrix ordered in the same manner as markers in newdata.xchrom.

newdata.covar

A matrix of covariates, each column being a different covariate and each row, a sample/individual. (Note: If all newdata.* are not given, the out-of-bag prediction in object is returned.)

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 "respons

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

(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 taken from the forest$cutoff component of

...

not used currently.

Value

The object returned depends on the argument type:
responsepredicted classes (the classes with majority vote).
probmatrix of class probabilities (one column for each class and one row for each input).
votematrix of vote counts (one column for each class and one row for each new input); either in raw counts or in fractions (if norm.votes=TRUE).
If predict.all=TRUE, then the individual component of the returned object is a character matrix where each column contains the predicted class by a tree in the forest.
If proximity=TRUE, the returned object is a list with two components: pred is the prediction (as described above) and proximity is the proximitry matrix.
If nodes=TRUE, the returned object has a ``nodes'' attribute, which is an n by ntree matrix, each column containing the node number that the cases fall in for that tree.
NOTE: Any ties are broken at random, so if this is undesirable, avoid it by using odd number ntree in snpRF().

References

Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.

Examples

Run this code

data(snpRFexample)
set.seed(111)
ind <- sample(2, nrow(autosome.snps), replace = TRUE, prob=c(0.8, 0.2))
eg.rf <- snpRF(x.autosome=autosome.snps[ind==1,],x.xchrom=xchrom.snps[ind==1,],
               xchrom.names=xchrom.snps.names,x.covar=covariates[ind==1,], 
               y=phenotype[ind==1])
eg.pred <- predict(eg.rf, newdata.autosome=autosome.snps[ind==2,], 
                   newdata.xchrom=xchrom.snps[ind==2,], 
                   xchrom.names=xchrom.snps.names, 
                   newdata.covar=covariates[ind==2,])
table(observed = phenotype[ind==2], predicted = eg.pred)
## Get prediction for all trees.
predict(eg.rf,newdata.autosome=autosome.snps[ind==2,], 
        newdata.xchrom=xchrom.snps[ind==2,], 
        xchrom.names=xchrom.snps.names, 
        newdata.covar=covariates[ind==2,], predict.all=TRUE)
## Proximities.
predict(eg.rf,newdata.autosome=autosome.snps[ind==2,], 
        newdata.xchrom=xchrom.snps[ind==2,], 
        xchrom.names=xchrom.snps.names,	
        newdata.covar=covariates[ind==2,], proximity=TRUE)
## Nodes matrix.
str(attr(predict(eg.rf,newdata.autosome=autosome.snps[ind==2,], 
                 newdata.xchrom=xchrom.snps[ind==2,], 
                 xchrom.names=xchrom.snps.names, 
                 newdata.covar=covariates[ind==2,], nodes=TRUE), "nodes"))