bartBMA_with_ITEs_exact_par: Prediction intervals for bart-bma output obtained using...
In bartBMA: Bayesian Additive Regression Trees using Bayesian Model Averaging

Description Usage Arguments Value Examples

View source: R/bartBMA_with_ITEs_exact_par.R

This function produces prediction intervals for bart-bma output.

bartBMA_with_ITEs_exact_par(
  l_quant,
  u_quant,
  newdata = NULL,
  update_resids = 1,
  num_cores = 1,
  root_alg_precision = 1e-05,
  x_covariates,
  z_train,
  y_train,
  a = 3,
  nu = 3,
  sigquant = 0.9,
  c = 1000,
  pen = 12,
  num_cp = 20,
  x.test = matrix(0, 0, 0),
  num_rounds = 5,
  alpha = 0.95,
  beta = 2,
  split_rule_node = 0,
  gridpoint = 0,
  maxOWsize = 100,
  num_splits = 5,
  gridsize = 10,
  zero_split = 1,
  only_max_num_trees = 1,
  min_num_obs_for_split = 2,
  min_num_obs_after_split = 2,
  exact_residuals = 1,
  spike_tree = 0,
  s_t_hyperprior = 1,
  p_s_t = 0.5,
  a_s_t = 1,
  b_s_t = 3,
  lambda_poisson = 10,
  less_greedy = 0
)

`l_quant`	Lower quantile of credible intervals for the ITEs, CATT, CATNT.
`u_quant`	Upper quantile of credible intervals for the ITEs, CATT, CATNT.
`newdata`	Test data for which predictions are to be produced. Default = NULL. If NULL, then produces prediction intervals for training data if no test data was used in producing the bartBMA object, or produces prediction intervals for the original test data if test data was used in producing the bartBMA object.
`update_resids`	Option for whether to update the partial residuals in the gibbs sampler. If equal to 1, updates partial residuals, if equal to zero, does not update partial residuals. The defaullt setting is to update the partial residuals.
`num_cores`	Number of cores used in parallel.
`root_alg_precision`	The algorithm should obtain approximate bounds that are within the distance root_alg_precision of the true quantile for the chosen average of models.
`x_covariates`	Covaraite matrix for training bartBMA.
`z_train`	treatment vector for traiing bartBMA.
`y_train`	outcome vector for training bartBMA.
`a`	This is a parameter that influences the variance of terminal node parameter values. Default value a=3.
`nu`	This is a hyperparameter in the distribution of the variance of the error term. THe inverse of the variance is distributed as Gamma (nu/2, nu*lambda/2). Default value nu=3.
`sigquant`	Calibration quantile for the inverse chi-squared prior on the variance of the error term.
`c`	This determines the size of Occam's Window
`pen`	This is a parameter used by the Pruned Exact Linear Time Algorithm when finding changepoints. Default value pen=12.
`num_cp`	This is a number between 0 and 100 that determines the proportion of changepoints proposed by the changepoint detection algorithm to keep when growing trees. Default num_cp=20.
`x.test`	Test data covariate matrix. Default x.test=matrix(0.0,0,0).
`num_rounds`	Number of trees. (Maximum number of trees in a sum-of-tree model). Default num_rounds=5.
`alpha`	Parameter in prior probability of tree node splitting. Default alpha=0.95
`beta`	Parameter in prior probability of tree node splitting. Default beta=1
`split_rule_node`	Binary variable. If equals 1, then find a new set of potential splitting points via a changepoint algorithm after adding each split to a tree. If equals zero, use the same set of potential split points for all splits in a tree. Default split_rule_node=0.
`gridpoint`	Binary variable. If equals 1, then a grid search changepoint detection algorithm will be used. If equals 0, then the Pruned Exact Linear Time (PELT) changepoint detection algorithm will be used (Killick et al. 2012). Default gridpoint=0.
`maxOWsize`	Maximum number of models to keep in Occam's window. Default maxOWsize=100.
`num_splits`	Maximum number of splits in a tree
`gridsize`	This integer determines the size of the grid across which to search if gridpoint=1 when finding changepoints for constructing trees.
`zero_split`	Binary variable. If equals 1, then zero split trees can be included in a sum-of-trees model. If equals zero, then only trees with at least one split can be included in a sum-of-trees model.
`only_max_num_trees`	Binary variable. If equals 1, then only sum-of-trees models containing the maximum number of trees, num_rounds, are selected. If equals 0, then sum-of-trees models containing less than num_rounds trees can be selected. The default is only_max_num_trees=1.
`min_num_obs_for_split`	This integer determines the minimum number of observations in a (parent) tree node for the algorithm to consider potential splits of the node.
`min_num_obs_after_split`	This integer determines the minimum number of observations in a child node resulting from a split in order for a split to occur. If the left or right chikd node has less than this number of observations, then the split can not occur.
`exact_residuals`	Binary variable. If equal to 1, then trees are added to sum-of-tree models within each round of the algorithm by detecting changepoints in the exact residuals. If equals zero, then changepoints are detected in residuals that are constructed from approximate predictions.
`spike_tree`	If equal to 1, then the Spike-and-Tree prior will be used, otherwise the standard BART prior will be used. The number of splitting variables has a beta-binomial prior. The number of terminal nodes has a truncated Poisson prior, and then a uniform prior is placed on the set of valid constructions of trees given the splitting variables and number of terminal nodes.
`s_t_hyperprior`	If equals 1 and spike_tree equals 1, then a beta distribution hyperprior is placed on the variable inclusion probabilities for the spike and tree prior. The hyperprior parameters are a_s_t and b_s_t.
`p_s_t`	If spike_tree=1 and s_t_hyperprior=0, then p_s_t is the prior variable inclusion probability.
`a_s_t`	If spike_tree=1 and s_t_hyperprior=1, then a_s_t is a parameter of a beta distribution hyperprior.
`b_s_t`	If spike_tree=1 and s_t_hyperprior=1, then b_s_t is a parameter of a beta distribution hyperprior.
`lambda_poisson`	This is a parameter for the Spike-and-Tree prior. It is the parameter for the (truncated and conditional on the number of splitting variables) Poisson prior on the number of terminal nodes.
`less_greedy`	If equal to one, then a less greedy model search algorithm is used.

The output is a list of length 4:

`ITE_intervals`	A 3 by n matrix, where n is the number of observations. The first row gives the l_quant100 quantiles of the individual treatment effects. The second row gives the medians of the ITEs. The third row gives the u_quant100 quantiles of the ITEs.
`ITE_estimates`	An n by 1 matrix containing the Individual Treatment Effect estimates.
`CATE_estimate`	The Conditional Average Treatment Effect Estimates
`CATE_Interval`	A 3 by 1 matrix. The first element is the l_quant100 quantile of the CATE distribution, the second element is the median of the CATE distribution, and the thied element is the u_quant100 quantile of the CATE distribution.

## Not run: 
#Example of BART-BMA for ITE estimation
#Applied to data simulations from Hahn et al. (2020, Bayesian Analysis) 
#"Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, 
# and Heterogeneous Effects
n <- 250
x1 <- rnorm(n)
x2 <- rnorm(n)
x3 <- rnorm(n) 
x4 <- rbinom(n,1,0.5)
x5 <- as.factor(sample( LETTERS[1:3], n, replace=TRUE))

p= 0
xnoise = matrix(rnorm(n*p), nrow=n)
x5A <- ifelse(x5== 'A',1,0)
x5B <- ifelse(x5== 'B',1,0)
x5C <- ifelse(x5== 'C',1,0)

x_covs_train <- cbind(x1,x2,x3,x4,x5A,x5B,x5C,xnoise)

#Treatment effect
#tautrain <- 3
tautrain <- 1+2*x_covs_train[,2]*x_covs_train[,4]

#Prognostic function
mutrain <- 1 + 2*x_covs_train[,5] -1*x_covs_train[,6]-4*x_covs_train[,7] +
x_covs_train[,1]*x_covs_train[,3]
sd_mtrain <- sd(mutrain)
utrain <- runif(n)
#pitrain <- 0.8*pnorm((3*mutrain/sd_mtrain)-0.5*x_covs_train[,1])+0.05+utrain/10
pitrain <- 0.5
ztrain <- rbinom(n,1,pitrain)
ytrain <- mutrain + tautrain*ztrain
#pihattrain <- pbart(x_covs_train,ztrain )$prob.train.mean

#set lower and upper quantiles for intervals
lbound <- 0.025
ubound <- 0.975

example_output <- bartBMA_with_ITEs_exact_par(l_quant = lbound, 
                                           u_quant= ubound, 
                                           x_covariates = x_covs_train, 
                                           z_train = ztrain, 
                                           y_train = ytrain)
                                           
## End(Not run)