htree (version 0.1.1)

quantile_hrf: Quantiles for historical random forest

Description

Quantiles for historical random forest

Usage

quantile_hrf(object, x=NULL,yindx=NULL,time=NULL,id=NULL,
	ntrees=NULL,prob=seq(.1,.9,length=10))

Arguments

object

The return list from running hrf.

x

a data frame or matrix containing new data. If NULL then training data in object is used.

yindx

column number of x corresponding response.

time

observation times.

id

subject identifiers

ntrees

number of trees to use in prediction.

prob

Probabilities at which quantiles are to be computed

Value

Returns a matrix of quantiles, with columns corresponding values of prob.

Details

Meinhausen (2006) showed how a random forest model could be used to estimate conditional quantiles. quantile_hrf implements this technique for a random forest with historical regression trees.

References

Meinhausen (2006) 'Quantile regression forests' Journal of Machine Learning Research.

See Also

hrf

Examples

# NOT RUN {
# }
# NOT RUN {
# -- random intercept example --- #

p=5;sigma_e=.5;sigma_a=.5;v=rep(1,p);n=500;pnoise=2
random_intercept=rnorm(n,sd=sigma_a)
random_intercept=as.numeric(matrix(random_intercept,nrow=p,ncol=n,byrow=TRUE))
x=random_intercept+rnorm(n*p,sd=sigma_e)
id=sort(rep(1:n,p))
time<-rep(1:p,n)
znoise=matrix(rnorm(n*p*pnoise),ncol=pnoise)
xx=cbind(time,x,znoise)

# fit historical random forest
hb=hrf(x=xx,time=time,id=id,yindx=2,ntrees=100,mtry=4,nsamp=5)

# get deciles on training data 
quant=quantile_hrf(hb,prob=seq(.1,.9,length=10))
# }