predict.wbart {BART} | R Documentation

## Predicting new observations with a previously fitted BART model

### Description

BART is a Bayesian “sum-of-trees” model.
For a numeric response y, we have y = f(x) + e, where e ~ N(0,sigma^2).

f is the sum of many tree models. The goal is to have very flexible inference for the uknown function f.

In the spirit of “ensemble models”, each tree is constrained by a prior to be a weak learner so that it contributes a small amount to the overall fit.

### Usage

    ## S3 method for class 'wbart'
    predict(object, newdata, mc.cores=1, openmp=(mc.cores.openmp()>0), ...)

### Arguments

`object` | `object` returned from previous BART fit.
`newdata` | Matrix of covariates to predict y for.
`mc.cores` | Number of threads to utilize.
`openmp` | Logical value dictating whether OpenMP is utilized for parallel processing. Of course, this depends on whether OpenMP is available on your system which, by default, is verified with `mc.cores.openmp`.
`...` | Other arguments which will be passed on to `pwbart`.

### Details

BART is an Bayesian MCMC method. At each MCMC interation, we produce a draw from the joint posterior (f,sigma) \| (x,y) in the numeric y case and just f in the binary y case.

Thus, unlike a lot of other modelling methods in R, we do not produce a single model object from which fits and summaries may be extracted. The output consists of values f*(x) (and sigma* in the numeric case) where * denotes a particular draw. The x is either a row from the training data (x.train) or the test data (x.test).

### Value

Returns a matrix of predictions corresponding to `newdata`.

### Author(s)

Robert McCulloch: robert.e.mcculloch@gmail.com,
Rodney Sparapani: rsparapa@mcw.edu.

### References

Chipman, H., George, E., and McCulloch R. (2010) Bayesian Additive Regression Trees. The Annals of Applied Statistics, 4,1, 266-298 <doi:10.1214/09-AOAS285>.

Chipman, H., George, E., and McCulloch R. (2006) Bayesian Ensemble Learning. Advances in Neural Information Processing Systems 19, Scholkopf, Platt and Hoffman, Eds., MIT Press, Cambridge, MA, 265-272.

Friedman, J.H. (1991) Multivariate adaptive regression splines. The Annals of Statistics, 19, 1–67.

### See Also

`wbart`, `mc.wbart`, `pwbart`, `mc.pwbart`, `mc.cores.openmp`

### Examples

    ##simulate data (example from Friedman MARS paper)
    f = function(x){
    10*sin(pi*x[,1]*x[,2]) + 20*(x[,3]-.5)^2+10*x[,4]+5*x[,5]
    }
    sigma = 1.0  #y = f(x) + sigma*z , z~N(0,1)
    n = 100      #number of observations
    set.seed(99)
    x=matrix(runif(n*10),n,10) #10 variables, only first 5 matter
    y=f(x)

    ##test BART with token run to ensure installation works
    set.seed(99)
    post = wbart(x,y,nskip=5,ndpost=5)
    x.test = matrix(runif(500*10),500,10)

    ## Not run:
    ##run BART
    set.seed(99)
    post = wbart(x,y)
    x.test = matrix(runif(500*10),500,10)
    pred = predict(post, x.test, mu=mean(y))

    plot(apply(pred, 2, mean), f(x.test))

    ## End(Not run)

[Package BART version 2.5 Index]