mc.lbart {BART} | R Documentation |
BART is a Bayesian “sum-of-trees” model.
For numeric response y, we have
y = f(x) + e,
where e ~ Log(0, 1).
For a binary response y, P(Y=1 | x) = F(f(x)), where F
denotes the standard Logistic CDF (logit link).
In both cases, 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.
mc.lbart( x.train, y.train, x.test=matrix(0.0,0,0), sparse=FALSE, a=0.5, b=1, augment=FALSE, rho=NULL, xinfo=matrix(0.0,0,0), usequants=FALSE, cont=FALSE, rm.const=TRUE, tau.interval=0.95, k=2.0, power=2.0, base=.95, binaryOffset=NULL, ntree=50L, numcut=100L, ndpost=1000L, nskip=100L, keepevery=1L, printevery=100, keeptrainfits=TRUE, transposed=FALSE, mc.cores = 2L, nice = 19L, seed = 99L )
x.train |
Explanatory variables for training (in sample) data. |
y.train |
Dependent variable for training (in sample) data. |
x.test |
Explanatory variables for test (out of sample) data. |
sparse |
Whether to perform variable selection based on a sparse Dirichlet prior rather than simply uniform; see Linero 2016. |
a |
Sparse parameter for Beta(a, b) prior: 0.5<=a<=1 where lower values inducing more sparsity. |
b |
Sparse parameter for Beta(a, b) prior; typically, b=1. |
rho |
Sparse parameter: typically rho=p where p is the number of covariates under consideration. |
augment |
Whether data augmentation is to be performed in sparse variable selection. |
xinfo |
You can provide the cutpoints to BART or let BART
choose them for you. To provide them, use the |
usequants |
If |
cont |
Whether or not to assume all variables are continuous. |
rm.const |
Whether or not to remove constant variables. |
tau.interval |
The width of the interval to scale the variance for the terminal leaf values. |
k |
For numeric y, k is the number of prior standard deviations E(Y|x) = f(x) is away from +/-.5. The response (y.train) is internally scaled to range from -.5 to .5. For binary y, k is the number of prior standard deviations f(x) is away from +/-3. In both cases, the bigger k is, the more conservative the fitting will be. |
power |
Power parameter for tree prior. |
base |
Base parameter for tree prior. |
binaryOffset |
Used for binary y. |
ntree |
The number of trees in the sum. |
numcut |
The number of possible values of c (see usequants). If a single number if given, this is used for all variables. Otherwise a vector with length equal to ncol(x.train) is required, where the i^th element gives the number of c used for the i^th variable in x.train. If usequants is false, numcut equally spaced cutoffs are used covering the range of values in the corresponding column of x.train. If usequants is true, then min(numcut, the number of unique values in the corresponding columns of x.train - 1) c values are used. |
ndpost |
The number of posterior draws returned. |
nskip |
Number of MCMC iterations to be treated as burn in. |
keepevery |
Every keepevery draw is kept to be returned to the user. |
printevery |
As the MCMC runs, a message is printed every printevery draws. |
keeptrainfits |
Whether to keep |
transposed |
When running |
seed |
Setting the seed required for reproducible MCMC. |
mc.cores |
Number of cores to employ in parallel. |
nice |
Set the job niceness. The default niceness is 19: niceness goes from 0 (highest) to 19 (lowest). |
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).
mc.lbart
returns an object of type lbart
which is
essentially a list.
yhat.train |
A matrix with ndpost rows and nrow(x.train) columns.
Each row corresponds to a draw f* from the posterior of f
and each column corresponds to a row of x.train.
The (i,j) value is f*(x) for the i\^th kept draw of f
and the j\^th row of x.train. |
yhat.test |
Same as yhat.train but now the x's are the rows of the test data. |
yhat.train.mean |
train data fits = mean of yhat.train columns. |
yhat.test.mean |
test data fits = mean of yhat.test columns. |
varcount |
a matrix with ndpost rows and nrow(x.train) columns. Each row is for a draw. For each variable (corresponding to the columns), the total count of the number of times that variable is used in a tree decision rule (over all trees) is given. |
In addition, the list
has a binaryOffset
giving the value used.
Note that in the binary y, case yhat.train and yhat.test are
f(x) + binaryOffset. If you want draws of the probability
P(Y=1 | x) you need to apply the Logistic cdf (plogis
)
to these values.
Robert McCulloch: robert.e.mcculloch@gmail.com,
Rodney Sparapani: rsparapa@mcw.edu.
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>.
Friedman, J.H. (1991) Multivariate adaptive regression splines. The Annals of Statistics, 19, 1–67.
Gramacy, RB and Polson, NG (2012) Simulation-based regularized logistic regression. Bayesian Analysis, 7, 567–590.
Holmes, C and Held, L (2006) Bayesian auxiliary variable models for binary and multinomial regression. Bayesian Analysis, 1, 145–68.
Linero, A.R. (2018) Bayesian regression trees for high dimensional prediction and variable selection. JASA, 113, 626–36.
set.seed(99) n=5000 x = sort(-2+4*runif(n)) X=matrix(x,ncol=1) f = function(x) {return((1/2)*x^3)} FL = function(x) {return(exp(x)/(1+exp(x)))} pv = FL(f(x)) y = rbinom(n,1,pv) np=100 xp=-2+4*(1:np)/np Xp=matrix(xp,ncol=1) ## parallel::mcparallel/mccollect do not exist on windows ## if(.Platform$OS.type=='unix') { ## ##test BART with token run to ensure installation works ## mf = mc.lbart(X, y, nskip=5, ndpost=5, mc.cores=1, seed=99) ## } ## Not run: set.seed(99) pf = lbart(X,y,Xp) plot(f(Xp), pf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4), xlab='True f(x)', ylab='BART f(x)') lines(c(-4, 4), c(-4, 4)) mf = mc.lbart(X,y,Xp, mc.cores=4, seed=99) plot(f(Xp), mf$yhat.test.mean, xlim=c(-4, 4), ylim=c(-4, 4), xlab='True f(x)', ylab='BART f(x)') lines(c(-4, 4), c(-4, 4)) par(mfrow=c(2,2)) plot(range(xp),range(pf$yhat.test),xlab='x',ylab='f(x)',type='n') lines(x,f(x),col='blue',lwd=2) lines(xp,apply(pf$yhat.test,2,mean),col='red') qpl = apply(pf$yhat.test,2,quantile,probs=c(.025,.975)) lines(xp,qpl[1,],col='green',lty=1) lines(xp,qpl[2,],col='green',lty=1) title(main='BART::lbart f(x) with 0.95 intervals') plot(range(xp),range(mf$yhat.test),xlab='x',ylab='f(x)',type='n') lines(x,f(x),col='blue',lwd=2) lines(xp,apply(mf$yhat.test,2,mean),col='red') qpl = apply(mf$yhat.test,2,quantile,probs=c(.025,.975)) lines(xp,qpl[1,],col='green',lty=1) lines(xp,qpl[2,],col='green',lty=1) title(main='BART::mc.lbart f(x) with 0.95 intervals') plot(pf$yhat.test.mean,apply(mf$yhat.test,2,mean),xlab='BART::lbart',ylab='BART::mc.lbart') abline(0,1,col='red') title(main="BART::lbart f(x) vs. BART::mc.lbart f(x)") ## End(Not run)