R/tbart2binary.R
In EoghanONeill/TobitBART: Tobit Bayesian Additive Regression Trees
Defines functions
tbart2binary
Documented in
tbart2binary
#' @title Type II Tobit Bayesian Additive Regression Trees implemented using MCMC
#'
#' @description Type II Tobit Bayesian Additive Regression Trees implemented using MCMC
#' @import dbarts
#' @import truncnorm
#' @import MASS
#' @import GIGrvg
#' @import mvtnorm
#' @import sampleSelection
#' @import progress
#' @import LaplacesDemon
#' @param x.train The outcome model training covariate data for all training observations. Number of rows equal to the number of observations. Number of columns equal to the number of covariates.
#' @param x.test The outcome model test covariate data for all test observations. Number of rows equal to the number of observations. Number of columns equal to the number of covariates.
#' @param w.train The censoring model training covariate data for all training observations. Number of rows equal to the number of observations. Number of columns equal to the number of covariates.
#' @param w.test The censoring model test covariate data for all test observations. Number of rows equal to the number of observations. Number of columns equal to the number of covariates.
#' @param y The training data vector of outcomes. A continuous, censored outcome variable. Censored observations must be included with values equal to censored_value
#' @param n.iter Number of iterations excluding burnin.
#' @param n.burnin Number of burnin iterations.
#' @param censored_value The value taken by censored observations
#' @param gamma0 The mean of the normal prior on the covariance of the errors in the censoring and outcome models.
#' @param G0 The variance of the normal prior on the covariance of the errors in the censoring and outcome models (only if cov_prior equals Omori).
#' @param nzero A prior parameter which when divided by 2 gives the mean of the normal prior on phi, where phi*gamma is the variance of the errors of the outcome model.
#' @param S0 A prior parameter which when divided by 2 gives the variance of the normal prior on phi, where phi*gamma is the variance of the errors of the outcome model.
#' @param sigest Estimate of variance of hte error term.
#' @param n.trees_outcome (dbarts control option) A positive integer giving the number of trees used in the outcome model sum-of-trees formulation.
#' @param n.trees_censoring (dbarts control option) A positive integer giving the number of trees used in the censoring model sum-of-trees formulation.
#' @param n.chains (dbarts control option) A positive integer detailing the number of independent chains for the dbarts sampler to use (more than one chain is unlikely to improve speed because only one sample for each call to dbarts).
#' @param n.threads (dbarts control option) A positive integer controlling how many threads will be used for various internal calculations, as well as the number of chains. Internal calculations are highly optimized so that single-threaded performance tends to be superior unless the number of observations is very large (>10k), so that it is often not necessary to have the number of threads exceed the number of chains.
#' @param printEvery (dbarts control option)If verbose is TRUE, every printEvery potential samples (after thinning) will issue a verbal statement. Must be a positive integer.
#' @param printCutoffs (dbarts control option) A non-negative integer specifying how many of the decision rules for a variable are printed in verbose mode
#' @param rngKind (dbarts control option) Random number generator kind, as used in set.seed. For type "default", the built-in generator will be used if possible. Otherwise, will attempt to match the built-in generator’s type. Success depends on the number of threads.
#' @param rngNormalKind (dbarts control option) Random number generator normal kind, as used in set.seed. For type "default", the built-in generator will be used if possible. Otherwise, will attempt to match the built-in generator’s type. Success depends on the number of threads and the rngKind
#' @param rngSeed (dbarts control option) Random number generator seed, as used in set.seed. If the sampler is running single-threaded or has one chain, the behavior will be as any other sequential algorithm. If the sampler is multithreaded, the seed will be used to create an additional pRNG object, which in turn will be used sequentially seed the threadspecific pRNGs. If equal to NA, the clock will be used to seed pRNGs when applicable.
#' @param updateState (dbarts control option) Logical setting the default behavior for many sampler methods with regards to the immediate updating of the cached state of the object. A current, cached state is only useful when saving/loading the sampler.
#' @param tree_power_y Tree prior parameter for outcome model.
#' @param tree_base_y Tree prior parameter for outcome model.
#' @param tree_power_z Tree prior parameter for selection model.
#' @param tree_base_z Tree prior parameter for selection model.
#' @param node.prior (dbarts option) An expression of the form dbarts:::normal or dbarts:::normal(k) that sets the prior used on the averages within nodes.
#' @param resid.prior (dbarts option) An expression of the form dbarts:::chisq or dbarts:::chisq(df,quant) that sets the prior used on the residual/error variance
#' @param proposal.probs (dbarts option) Named numeric vector or NULL, optionally specifying the proposal rules and their probabilities. Elements should be "birth_death", "change", and "swap" to control tree change proposals, and "birth" to give the relative frequency of birth/death in the "birth_death" step.
#' @param sigmadbarts (dbarts option) A positive numeric estimate of the residual standard deviation. If NA, a linear model is used with all of the predictors to obtain one.
#' @param print.opt Print every print.opt number of Gibbs samples.
#' @param eq_by_eq If TRUE, implements sampler equation by equation (as in BAVART by Huber and Rossini (2021)). If FALSE, implements sampler in similar approach to SUR-BART (Chakraborty 2016) or MPBART (Kindo 2016).
#' @param accelerate If TRUE, add extra parameter for accelerated sampler as descibed by Omori (2007).
#' @param cov_prior Prior for the covariance of the error terms. If VH, apply the prior of van Hasselt (2011), N(gamma0, tau*phi), imposing dependence between gamma and phi. If Omori, apply the prior N(gamma0,G0). If mixture, then a mixture of the VH and Omori priors with probability mixprob applied to the VH prior.
#' @param mixprob If cov_prior equals Mixture, then mixprob is the probability applied to the Van Hasselt covariance prior, and one minus mixprob is the probability applied to the Omori prior.
#' @param tau Parameter for the prior of van Hasselt (2011) on the covariance of the error terms.
#' @param simultaneous_covmat If TRUE, jointly sample the parameters that determine the covariance matrix instead of sampling from separate full conditionals.
#' @param fast If equal to true, takes faster samples of z and y and makes faster approximate calculations of selection probabilities.
#' @param nu0 For the inverseWishart prior Winv(nu0,c*I_2) of Ding (2014) on an unidentified unrestricted covariance matrix. nu = 3 corresponds to a uniform correlation prior. nu > 3 centers the correlation prior at 0, while nu < 3 places more prior probability on selection on unobservables.
#' @param quantsig For the inverseWishart prior Winv(nu0,c*I_2) of Ding (2014). The parameter c is determined by quantsig so that the marginal prior on the standard deviation of the outcome has 90% quantile equal to an estimate from a linear tobit model or sigest if sigest is not NA.
#' @param sparse If equal to TRUE, use Linero Dirichlet prior on splitting probabilities
#' @param alpha_a_y Linero alpha prior parameter for outcome equation splitting probabilities
#' @param alpha_b_y Linero alpha prior parameter for outcome equation splitting probabilities
#' @param alpha_a_z Linero alpha prior parameter for selection equation splitting probabilities
#' @param alpha_b_z Linero alpha prior parameter for selection equation splitting probabilities
#' @param alpha_split_prior If true, set hyperprior for Linero alpha parameter
#' @export
#' @return The following objects are returned:
#' \item{Z.mat_train}{Matrix of draws of censoring model latent outcomes for training observations. Number of rows equals number of training observations. Number of columns equals n.iter . Rows are ordered in order of observations in the training data.}
#' \item{Z.mat_test}{Matrix of draws of censoring model latent outcomes for test observations. Number of rows equals number of test observations. Number of columns equals n.iter . Rows are ordered in order of observations in the test data.}
#' \item{Y.mat_train}{Matrix of draws of outcome model latent outcomes for training observations. Number of rows equals number of training observations. Number of columns equals n.iter . Rows are ordered in order of observations in the training data.}
#' \item{Y.mat_test}{Matrix of draws of outcome model latent outcomes for test observations. Number of rows equals number of test observations. Number of columns equals n.iter . Rows are ordered in order of observations in the test data.}
#' \item{mu_y_train}{Matrix of draws of the outcome model sums of terminal nodes, i.e. f(x_i), for all training observations. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{mu_y_test}{Matrix of draws of the outcome model sums of terminal nodes, i.e. f(x_i), for all test observations. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{mucens_y_train}{Matrix of draws of the outcome model sums of terminal nodes, i.e. f(x_i), for all censored training observations. Number of rows equals number of censored training observations. Number of columns equals n.iter .}
#' \item{muuncens_y_train}{Matrix of draws of the outcome model sums of terminal nodes, i.e. f(x_i), for all uncensored training observations. Number of rows equals number of uncensored training observations. Number of columns equals n.iter .}
#' \item{mu_z_train}{Matrix of draws of the censoring model sums of terminal nodes, i.e. f(w_i), for all training observations. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{mu_z_test}{Matrix of draws of the censoring model sums of terminal nodes, i.e. f(w_i), for all test observations. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{train.probcens}{Matrix of draws of probabilities of training sample observations being censored. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{test.probcens}{Matrix of draws of probabilities of test sample observations being censored. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{cond_exp_train}{Matrix of draws of the conditional (i.e. possibly censored) expectations of the outcome for all training observations. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{cond_exp_test}{Matrix of draws of the conditional (i.e. possibly censored) expectations of the outcome for all test observations. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{uncond_exp_train}{Only defined if censored_value is a number. Matrix of draws of the unconditional (i.e. possibly censored) expectations of the outcome for all training observations. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{uncond_exp_test}{Only defined if censored_value is a number. Matrix of draws of the unconditional (i.e. possibly censored) expectations of the outcome for all test observations. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{ystar_train}{Matrix of training sample draws of the outcome assuming uncensored. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{ystar_test}{Matrix of test sample draws of the outcome assuming uncensored . Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{zstar_train}{Matrix of training sample draws of the censoring model latent outcome. Number of rows equals number of training observations. Number of columns equals n.iter.}
#' \item{zstar_test}{Matrix of test sample draws of the censoring model latent outcome. Number of rows equals number of test observations. Number of columns equals n.iter.}
#' \item{ydraws_train}{Only defined if censored_value is a number. Matrix of training sample unconditional (i.e. possibly censored) draws of the outcome. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{ydraws_test}{Only defined if censored_value is a number. Matrix of test sample unconditional (i.e. possibly censored) draws of the outcome . Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{ycond_draws_train}{List of training sample conditional (i.e. zstar >0 for draw) draws of the outcome. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{ycond_draws_test}{List of test sample conditional (i.e. zstar >0 for draw) draws of the outcome . Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{Sigma_draws}{3 dimensional array of MCMC draws of the covariance matrix for the censoring and outcome error terms. The numbers of rows and columns equal are equal to 2. The first row and column correspond to the censoring model. The second row and column correspond to the outcome model. The number of slices equals n.iter . }
#' \item{alpha_s_y_store}{For Dirichlet prior on splitting probabilities in outcome equation, vector of alpha hyperparameter draws for each iteration.}
#' \item{alpha_s_z_store}{For Dirichlet prior on splitting probabilities in selection equation, vector of alpha hyperparameter draws for each iteration }
#' \item{var_count_y_store}{Matrix of counts of splits on each variable in outcome observation. The number of rows is the number of potential splitting variables. The number of columns is the number of post-burn-in iterations.}
#' \item{var_count_z_store}{Matrix of counts of splits on each variable in selection observation. The number of rows is the number of potential splitting variables. The number of columns is the number of post-burn-in iterations. }
#' \item{s_prob_y_store}{Splitting probabilities for the outcome equation. The number of rows is the number of potential splitting variables. The number of columns is the number of post-burn-in iterations. }
#' \item{s_prob_z_store}{Splitting probabilities for the selection equation. The number of rows is the number of potential splitting variables. The number of columns is the number of post-burn-in iterations. }
#' @examples
#'
#'#example taken from Zhang, J., Li, Z., Song, X., & Ning, H. (2021). Deep Tobit networks: A novel machine learning approach to microeconometrics. Neural Networks, 144, 279-296.
#'
#'
#'
#' #Type II tobit simulation
#'
#' num_train <- 5000
#'
#' #consider increasing the number of covariates
#'
#' Xmat_train <- matrix(NA,nrow = num_train,
#' ncol = 8)
#'
#' Xmat_train[,1] <- runif(num_train, min = -1, max = 1)
#' Xmat_train[,2] <- rf(num_train,20,20)
#' Xmat_train[,3] <- rbinom(num_train, size = 1, prob = 0.75)
#' Xmat_train[,4] <- rnorm(num_train, mean = 1, sd = 1)
#' Xmat_train[,5] <- rnorm(num_train)
#' Xmat_train[,6] <- rbinom(num_train, size = 1, prob = 0.5)
#' Xmat_train[,7] <- rf(num_train,20,200)
#' Xmat_train[,8] <- runif(num_train, min = 0, max = 2)
#'
#' #it would be better to test performance of the models when there is correlation in the error terms.
#' varepsilon1_train <- rnorm(num_train, mean = 0, sd = sqrt(0.00025))
#' varepsilon2_train <- rnorm(num_train, mean = 0, sd = sqrt(0.00025))
#'
#' y1star_train <- 1 - 0.75*Xmat_train[,1] + 0.75*Xmat_train[,2] -
#' 0.5*Xmat_train[,4] - 0.5*Xmat_train[,6] - 0.25*Xmat_train[,1]^2 -
#' 0.75*Xmat_train[,1]*Xmat_train[,4] - 0.25*Xmat_train[,1]*Xmat_train[,2] -
#' 1*Xmat_train[,1]*Xmat_train[,6] + 0.5*Xmat_train[,2]*Xmat_train[,6] +
#' varepsilon1_train
#'
#' y2star_train <- 1 + 0.25*Xmat_train[,4] - 0.75*Xmat_train[,6] +
#' 0.5*Xmat_train[,7] + 0.25*Xmat_train[,8] +
#' 0.25*Xmat_train[,4]^2 + 0.75*Xmat_train[,7]^2 + 0.5*Xmat_train[,8]^2 -
#' 1*Xmat_train[,4]*Xmat_train[,6] + 0.5*Xmat_train[,4]*Xmat_train[,8] +
#' 1*Xmat_train[,6]*Xmat_train[,7] - 0.25*Xmat_train[,7]*Xmat_train[,8] +
#' varepsilon2_train
#'
#' y2obs_train <- ifelse(y1star_train>0, y2star_train,0)
#'
#' #Type II tobit simulation
#'
#' num_test <- 5000
#'
#' #consider increasing the number of covariates
#'
#' Xmat_test <- matrix(NA,nrow = num_test,
#' ncol = 8)
#'
#' Xmat_test[,1] <- runif(num_test, min = -1, max = 1)
#' Xmat_test[,2] <- rf(num_test,20,20)
#' Xmat_test[,3] <- rbinom(num_test, size = 1, prob = 0.75)
#' Xmat_test[,4] <- rnorm(num_test, mean = 1, sd = 1)
#' Xmat_test[,5] <- rnorm(num_test)
#' Xmat_test[,6] <- rbinom(num_test, size = 1, prob = 0.5)
#' Xmat_test[,7] <- rf(num_test,20,200)
#' Xmat_test[,8] <- runif(num_test, min = 0, max = 2)
#'
#' #it would be better to test performance of the models when there is correlation in the error terms.
#' varepsilon1_test <- rnorm(num_test, mean = 0, sd = sqrt(0.00025))
#' varepsilon2_test <- rnorm(num_test, mean = 0, sd = sqrt(0.00025))
#'
#' y1star_test <- 1 - 0.75*Xmat_test[,1] + 0.75*Xmat_test[,2] -
#' 0.5*Xmat_test[,4] - 0.5*Xmat_test[,6] - 0.25*Xmat_test[,1]^2 -
#' 0.75*Xmat_test[,1]*Xmat_test[,4] - 0.25*Xmat_test[,1]*Xmat_test[,2] -
#' 1*Xmat_test[,1]*Xmat_test[,6] + 0.5*Xmat_test[,2]*Xmat_test[,6] +
#' varepsilon1_test
#'
#' y2star_test <- 1 + 0.25*Xmat_test[,4] - 0.75*Xmat_test[,6] +
#' 0.5*Xmat_test[,7] + 0.25*Xmat_test[,8] +
#' 0.25*Xmat_test[,4]^2 + 0.75*Xmat_test[,7]^2 + 0.5*Xmat_test[,8]^2 -
#' 1*Xmat_test[,4]*Xmat_test[,6] + 0.5*Xmat_test[,4]*Xmat_test[,8] +
#' 1*Xmat_test[,6]*Xmat_test[,7] - 0.25*Xmat_test[,7]*Xmat_test[,8] +
#' varepsilon2_test
#'
#' y2obs_test <- ifelse(y1star_test>0, y2star_test,0)
#'
#' y2response_test <- ifelse(y1star_test>0, 1,0)
#'
#' tbartII_example <- tbart2c(Xmat_train,
#' Xmat_test,
#' Xmat_train,
#' Xmat_test,
#' y2obs_train,
#' n.iter=5000,
#' n.burnin=1000,
#' censored_value = 0,
#' eq_by_eq = TRUE)
#'
#'
#' pred_probs_tbart2_test <- rowMeans(tbartII_example$test.probcens)
#'
#' #Training (within-sample) Prediction Realization Table
#'
#' cutoff_point <- mean(y2obs_train>0)
#'
#' test_bin_preds <- ifelse(1 - pred_probs_tbart2_test > cutoff_point,1,0)
#'
#' #Training (within-sample) Prediction Realization Table
#'
#' pred_realization_test <- rbind(cbind(table(y2response_test, test_bin_preds)/length(y2response_test),
#' apply(table(y2response_test, test_bin_preds)/length(y2response_test),1,sum)),
#' c(t(apply(table(y2response_test, test_bin_preds)/length(y2response_test),2,sum)), 1))
#'
#' hit_rate_test <- pred_realization_test[1,1] +pred_realization_test[2,2]
#'
#' testpreds_tbart2 <- rowMeans(tbartII_example$uncond_exp_test)
#'
#' sqrt(mean((y2obs_test - testpreds_tbart2 )^2 ))
#'
#' @export
tbart2binary <- function(x.train,
x.test,
w.train,
w.test,
y,
n.iter=1000,
n.burnin=100,
censored_value = NA,
gamma0 = 0,
G0=1,
nzero = 6,#0.002,
S0= 12,#0.002,
sigest = NA,
n.trees_outcome = 50L,
n.trees_censoring = 50L,
n.burn = 0L,
n.samples = 1L,
n.thin = 1L,
n.chains = 1L,
n.threads = 1L, #guessNumCores(),
printEvery = 100L,
printCutoffs = 0L,
rngKind = "default",
rngNormalKind = "default",
rngSeed = NA_integer_,
updateState = TRUE,
tree_power_z = 2,
tree_power_y = 2,
tree_base_z = 0.95,
tree_base_y = 0.95,
node.prior = dbarts:::normal,
resid.prior = dbarts:::chisq,
proposal.probs = c(birth_death = 0.5, swap = 0.1, change = 0.4, birth = 0.5),
sigmadbarts = NA_real_,
print.opt = 100,
eq_by_eq = TRUE,
# accelerate = FALSE,
cov_prior = "Ding",
tau = 1/3,
mixprob = 0.5,
simultaneous_covmat = TRUE,
fast = TRUE,
nu0 = 3,
quantsig = 0.9,
sparse = FALSE,
alpha_a_y = 0.5,
alpha_b_y = 1,
alpha_a_z = 0.5,
alpha_b_z = 1,
alpha_split_prior = TRUE){
if(!(cov_prior %in% c("VH","Omori","Mixture", "Ding"))){
stop("cov_prior must equal VH, Omori, Mixture, or Ding")
}
if((mixprob < 0)| (mixprob > 1) ){
stop("mixprob must be between zero and one.")
}
# if(is.vector(x.train) | is.factor(x.train)| is.data.frame(x.train)) x.train = as.matrix(x.train)
# if(is.vector(x.test) | is.factor(x.test)| is.data.frame(x.test)) x.test = as.matrix(x.test)
# if((!is.matrix(x.train))) stop("argument x.train must be a double matrix")
# if((!is.matrix(x.test)) ) stop("argument x.test must be a double matrix")
if(nrow(x.train) != length(y)) stop("number of rows in x.train must equal length of y.train")
if((ncol(x.test)!=ncol(x.train))) stop("input x.test must have the same number of columns as x.train")
if((ncol(w.test)!=ncol(w.train))) stop("input w.test must have the same number of columns as w.train")
if((nrow(w.test)!=nrow(x.test))) stop("input w.test must have the same number of rows as x.test")
if((nrow(w.train)!=nrow(x.train))) stop("input w.train must have the same number of rows as x.train")
#indexes of censored observations
if(is.na(censored_value)){
cens_inds <- which(is.na(y))
uncens_inds <- which(!(is.na(y)))
}else{
cens_inds <- which(y == censored_value)
uncens_inds <- which(y != censored_value)
}
# print("line 292")
if(length(cens_inds)==0) stop("No censored observations")
# print("line 296")
if(!all(y %in% c(0,1,NA))){
stop("All elements of y must be 0,1, or NA")
}
# print("line 302")
# normalize the outcome
# tempmean <- mean(y[uncens_inds])
# tempsd <- sd(y[uncens_inds])
# originaly <- y
# y <- (y-tempmean)/tempsd
#
# if(is.numeric(censored_value)){
# censored_value <- (censored_value- tempmean)/tempsd
# }
tempsd <- 1
tempmean <- 0
n <- length(y)
n0 <- length(cens_inds)
n1 <- length(uncens_inds)
uncens0_inds <- which(y==0)
uncens1_inds <- which(y==1)
n_zeros <- sum(y==0,na.rm = TRUE)
n_ones <- sum(y==1,na.rm = TRUE)
# offsety <- 0 # qnorm(n_ones/n1)
offsety <- qnorm(n_ones/n1)
ystar <- rep(offsety, length(y))
# print("line 331")
sub_uncens0_inds <- which(y[uncens_inds]==0)
sub_uncens1_inds <- which(y[uncens_inds]==1)
# print("line 339")
# print("line 344")
# z[cens_inds] <- qnorm(0.001) #rtruncnorm(n0, a= -Inf, b = 0, mean = offsety, sd = 1)
#
# z[uncens_inds] <- qnorm(0.999) #rtruncnorm(n1, a= 0, b = Inf, mean = offsety, sd = 1)
# print("uncens0_inds = ")
# print(uncens0_inds)
#
# print("uncens1_inds = ")
# print(uncens1_inds)
#
# print("n_zeros = ")
# print(n_zeros)
#
# print("n_ones = ")
# print(n_ones)
ystar[uncens0_inds] <- rtruncnorm(n_zeros, a= -Inf, b = 0, mean = offsety, sd = 1)
ystar[uncens1_inds] <- rtruncnorm(n_ones, a= 0, b = Inf, mean = offsety, sd = 1)
# print("line 349")
#create ystar vector
# ystar <- rep(mean(y[uncens_inds]), length(y))
# ystar[uncens_inds] <- y[uncens_inds]
ecdfs <- list()
for(i in 1:ncol(x.train)) {
ecdfs[[i]] <- ecdf(x.train[,i])
if(length(unique(x.train[,i])) == 1) ecdfs[[i]] <- identity
if(length(unique(x.train[,i])) == 2) ecdfs[[i]] <- make_01_norm(x.train[,i])
}
for(i in 1:ncol(x.train)) {
x.train[,i] <- ecdfs[[i]](x.train[,i])
x.test[,i] <- ecdfs[[i]](x.test[,i])
}
rm(ecdfs)
ecdfs <- list()
for(i in 1:ncol(w.train)) {
ecdfs[[i]] <- ecdf(w.train[,i])
if(length(unique(w.train[,i])) == 1) ecdfs[[i]] <- identity
if(length(unique(w.train[,i])) == 2) ecdfs[[i]] <- make_01_norm(w.train[,i])
}
for(i in 1:ncol(w.train)) {
w.train[,i] <- ecdfs[[i]](w.train[,i])
w.test[,i] <- ecdfs[[i]](w.test[,i])
}
rm(ecdfs)
#create z vector
# print("line 392")
ntest = nrow(x.test)
p_y <- ncol(x.train)
p_z <- ncol(w.train)
if(sparse){
s_y <- rep(1 / p_y, p_y) # probability vector to be used during the growing process for DART feature weighting
rho_y <- p_y # For DART
if(alpha_split_prior){
alpha_s_y <- p_y
}else{
alpha_s_y <- 1
}
alpha_scale_y <- p_y
s_z <- rep(1 / p_z, p_z) # probability vector to be used during the growing process for DART feature weighting
rho_z <- p_z # For DART
if(alpha_split_prior){
alpha_s_z <- p_z
}else{
alpha_s_z <- 1
}
alpha_scale_z <- p_z
}
offsetz <- 0 # qnorm(n1/n)
# offsetz <- qnorm(n1/n)
z <- rep(offsetz, length(y))
# z[cens_inds] <- qnorm(0.001) #rtruncnorm(n0, a= -Inf, b = 0, mean = offsetz, sd = 1)
#
# z[uncens_inds] <- qnorm(0.999) #rtruncnorm(n1, a= 0, b = Inf, mean = offsetz, sd = 1)
z[cens_inds] <- rtruncnorm(n0, a= -Inf, b = 0, mean = offsetz, sd = 1)
z[uncens_inds] <- rtruncnorm(n1, a= 0, b = Inf, mean = offsetz, sd = 1)
# z <- rnorm(n = length(y), mean = offsetz, sd =1)
# print("line 448")
# if(is.null(S0)){
#
# #use uncensored observations
# if(is.na(sigest)) {
# if(ncol(x.train) < n1) {
# df = data.frame(x = x.train[uncens_inds,],y = y[uncens_inds])
# lmf = lm(y~.,df)
# sigest = summary(lmf)$sigma
# } else {
# sigest = sd(y[uncens_inds])
# }
# }
#
# S0 <- 2*(sigest^2 - (1/(8*(G0^2))) - 4*(gamma0^2)*G0 )
#
# # S0 <- 2*(sigest^2)# - (1/(8*(G0^2))) - 4*(gamma0^2)*G0 )
#
# # S0 <- 0.002
#
# }
#use uncensored observations
if(is.na(sigest)) {
if(ncol(x.train) < n1) {
# df = data.frame(x = x.train[uncens_inds,],y = y[uncens_inds])
# lmf = lm(y~.,df)
# sigest = summary(lmf)$sigma
if(is.na(censored_value)){
dtemp <- 1*(!(is.na(y)))
}else{
dtemp <- 1*(y != censored_value)
}
df = data.frame(x = cbind(x.train,w.train), ystar = ystar, d = dtemp )
# print("x.train = ")
# print(x.train)
# print("colnames(df) = ")
# print(colnames(df))
# colnames(df)[1:ncol(x.train)] <- paste("x",1:ncol(x.train),sep = ".")
# colnames(df)[(ncol(x.train)+1):(ncol(x.train) + ncol(w.train))] <- paste("x",(ncol(x.train)+1):(ncol(x.train) + ncol(w.train)),sep = ".")
#
# seleq <- paste0("d ~ " , paste(paste("x",(ncol(x.train)+1):(ncol(x.train) + ncol(w.train)),sep = "."),collapse = " + "))
# outeq <- paste0("y ~ " , paste(paste("x",1:ncol(x.train),sep = "."),collapse = " + "))
seleq <- paste0("d ~ " , paste(colnames(df)[(ncol(x.train)+1):(ncol(x.train) + ncol(w.train))],
collapse = " + "))
outeq <- paste0("ystar ~ " , paste(colnames(df)[1:ncol(x.train)],
collapse = " + "))
heckit.ml <- heckit(selection = as.formula(seleq),
outcome = as.formula(outeq),
data = df,
method = "ml")
correst <- heckit.ml$estimate["rho"]
sigest <- heckit.ml$estimate["sigma"]
# correst <- heckit.2step$coefficients["rho"]
# sigest <- heckit.2step$coefficients["sigma"]
# print("heckit.2step$coefficients = ")
# print(heckit.2step$coefficients)
# print("heckit.2step$lm$coefficients = ")
# print(heckit.2step$lm$coefficients)
# print("correst = ")
# print(correst)
# print("correst = ")
# print(correst)
# print("correst = ")
# print(correst)
} else {
sigest = sd(ystar[uncens_inds])
correst <- 0
}
}
# if(is.null(nzero)){
#
# nzero <- 2*(sigest^2)
#
# }
#set initial sigma
#alternatively, draw this from the prior
Sigma_mat <- cbind(c(1,0),c(0,sigest^2))
#set initial gamma
gamma1 <- correst*sigest #0#cov(ystar,z)
#set initial phi
phi1 <- sigest^2 - gamma1^2
# print("phi1 = ")
# print(phi1)
# print("correst = ")
# print(correst)
# print("sigest = ")
# print(sigest)
# print("gamma1 = ")
# print(gamma1)
# if(sigest > G0){
# S0 <- (nzero - 2)*(sigest-G0)
# }else{
# sigquant <- 0.9
# qchi <- qchisq(1.0-sigquant,nzero/2)
# S0 <- 2*(sigest*sigest*qchi)/(nzero/2)
# }
# S0 <- (sigest^2)*(nzero-2)/(1+tau)
S0 <- (sigest^2)*(1 - correst^2)*(nzero-2)/(1+tau)
gamma0 <- correst*sigest
if(cov_prior == "Ding"){
gamma0 <- 0
# sigquant <- 0.9
qchi <- qchisq(1.0-quantsig,nu0-1)
cdivnu <- (sigest*sigest*qchi)/(nu0-1) #lambda parameter for sigma prior
cding <- cdivnu*(nu0-1)
rhoinit <- 0
siginit <- sigest
}
draw = list(
# Z.mat_train = array(NA, dim = c(n, n.iter)),
# Z.mat_test = array(NA, dim = c(ntest, n.iter)),
# Y.mat_train = array(NA, dim = c(n, n.iter)),
# Y.mat_test = array(NA, dim = c(ntest, n.iter)),
mu_y_train = array(NA, dim = c(n1, n.iter)),# array(NA, dim = c(n, n.iter)),
mu_y_test = array(NA, dim = c(ntest, n.iter)),
# mucens_y_train = array(NA, dim = c(n0, n.iter)),
muuncens_y_train = array(NA, dim = c(n1, n.iter)),
mu_z_train = array(NA, dim = c(n, n.iter)),
mu_z_test = array(NA, dim = c(ntest, n.iter)),
train.probcens = array(NA, dim = c(n1, n.iter)),#array(NA, dim = c(n, n.iter)),#,
test.probcens = array(NA, dim = c(ntest, n.iter)),#,
train.prob.y = array(NA, dim = c(n1, n.iter)),#array(NA, dim = c(n, n.iter)),#,
test.prob.y = array(NA, dim = c(ntest, n.iter)),#,
cond_exp_train = array(NA, dim = c(n1, n.iter)),#cond_exp_train = array(NA, dim = c(n, n.iter)),
cond_exp_test = array(NA, dim = c(ntest, n.iter)),
ystar_train = array(NA, dim = c(n, n.iter)),
ystar_test = array(NA, dim = c(ntest, n.iter)),
zstar_train = array(NA, dim = c(n, n.iter)),
zstar_test = array(NA, dim = c(ntest, n.iter)),
ycond_draws_train = list(),
ycond_draws_test = list(),
Sigma_draws = array(NA, dim = c(2, 2, n.iter))
)
if(is.numeric(censored_value)){
draw$uncond_exp_train <- array(NA, dim = c(n1, n.iter)) #array(NA, dim = c(n, n.iter))
draw$uncond_exp_test <- array(NA, dim = c(ntest, n.iter))
# draw$ydraws_train <- array(NA, dim = c(n, n.iter))
draw$ydraws_test <- array(NA, dim = c(ntest, n.iter))
}
if(sparse){
var_count_y <- rep(0, p_y)
var_count_z <- rep(0, p_z)
draw$alpha_s_y_store <- rep(NA, n.iter)
draw$alpha_s_z_store <- rep(NA, n.iter)
draw$var_count_y_store <- matrix(0, ncol = p_y, nrow = n.iter)
draw$var_count_z_store <- matrix(0, ncol = p_z, nrow = n.iter)
draw$s_prob_y_store <- matrix(0, ncol = p_y, nrow = n.iter)
draw$s_prob_z_store <- matrix(0, ncol = p_z, nrow = n.iter)
}
########## Initialize dbarts #####################
control_z <- dbartsControl(updateState = updateState, verbose = FALSE, keepTrainingFits = TRUE,
keepTrees = TRUE,
n.trees = n.trees_censoring,
n.burn = n.burn,
n.samples = n.samples,
n.thin = n.thin,
n.chains = n.chains,
n.threads = n.threads,
printEvery = printEvery,
printCutoffs = printCutoffs,
rngKind = rngKind,
rngNormalKind = rngNormalKind,
rngSeed = rngSeed)
control_y <- dbartsControl(updateState = updateState, verbose = FALSE, keepTrainingFits = TRUE,
keepTrees = TRUE,
n.trees = n.trees_outcome,
n.burn = n.burn,
n.samples = n.samples,
n.thin = n.thin,
n.chains = n.chains,
n.threads = n.threads,
printEvery = printEvery,
printCutoffs = printCutoffs,
rngKind = rngKind,
rngNormalKind = rngNormalKind,
rngSeed = rngSeed)
# print(colnames(Xmat.train))
# print("begin dbarts")
weightstemp <- rep(1,n)
weightstemp[uncens_inds] <- (gamma1^2 + phi1)/phi1
# print("weightstemp = ")
# print(weightstemp)
#
# print("length(weightstemp) = ")
# print(length(weightstemp))
#
# print("length(uncens_inds) = ")
# print(length(uncens_inds))
# print("line 681")
if(nrow(x.test ) == 0){
xdf_y <- data.frame(y = ystar[uncens_inds], x = x.train[uncens_inds,])
sampler_y <- dbarts(y ~ .,
data = xdf_y,
#test = x.test,
control = control_y,
tree.prior = dbarts:::cgm(power = tree_power_y, base = tree_base_y, split.probs = rep(1 / p_y, p_y)),
node.prior = node.prior,
resid.prior = resid.prior,
proposal.probs = proposal.probs,
sigma = sigmadbarts
)
# print("line 697")
# print("Line 425")
xdf_z <- data.frame(y = z - offsetz, x = w.train)
sampler_z <- dbarts(y ~ .,
data = xdf_z,
#test = x.test,
weights = weightstemp,
control = control_z,
tree.prior = dbarts:::cgm(power = tree_power_z, base = tree_base_z, split.probs = rep(1 / p_z, p_z)),
node.prior = node.prior,
resid.prior = resid.prior,
proposal.probs = proposal.probs,
sigma = 1
)
}else{
xdf_y <- data.frame(y = ystar[uncens_inds], x = x.train[uncens_inds,])
xdf_y_test <- data.frame(x = x.test)
sampler_y <- dbarts(y ~ .,
data = xdf_y,
test = xdf_y_test,
control = control_y,
tree.prior = dbarts:::cgm(power = tree_power_y, base = tree_base_y, split.probs = rep(1 / p_y, p_y)),
node.prior = node.prior,
resid.prior = resid.prior,
proposal.probs = proposal.probs,
sigma = sigmadbarts
)
# print("Line 425")
xdf_z <- data.frame(y = z - offsetz, x = w.train)
xdf_z_test <- data.frame(x = w.test)
sampler_z <- dbarts(y ~ .,
data = xdf_z,
test = xdf_z_test,
# weights = weightstemp,
control = control_z,
tree.prior = dbarts:::cgm(power = tree_power_z, base = tree_base_z, split.probs = rep(1 / p_z, p_z)),
node.prior = node.prior,
resid.prior = resid.prior,
proposal.probs = proposal.probs,
sigma = 1#sigmadbarts
)
}
# print("Line 473")
preds.train_ystar <- matrix(NA, n, 1)
preds.train_z <- matrix(NA, n, 1)
preds.test_ystar <- matrix(NA, ntest, 1)
preds.test_z <- matrix(NA, ntest, 1)
#initialize sum-of-tree sampler
z_resids <- z - offsetz #z_epsilon
z_resids[uncens_inds] <- z[uncens_inds] - offsetz - (ystar[uncens_inds] - 0)*gamma1/(phi1 + gamma1^2)
sampler_z$setResponse(y = z_resids)
sampler_z$setSigma(sigma = 1)
sampler_z$setWeights(weights = weightstemp)
# sampler_z$model@node.scale <- 3
if(sparse){
tempmodel <- sampler_z$model
tempmodel@tree.prior@splitProbabilities <- s_z
sampler_z$setModel(newModel = tempmodel)
# print("check z probs")
# print("sampler_z@tree.prior@splitProbabilities = ")
# print(sampler_z@tree.prior@splitProbabilities)
}
# print("Line 509")
# sampler_z$sampleTreesFromPrior()
# priormean_z <- sampler_z$predict(xdf_z)[1,]
# sampler_z$sampleNodeParametersFromPrior()
samplestemp_z <- sampler_z$run()
# mutemp_z <- rep(0,n) # samplestemp_z$train[,1]
# mutemp_test_z <- rep(0,ntest) #samplestemp_z$test[,1]
mutemp_z <- samplestemp_z$train[,1]
mutemp_test_z <- samplestemp_z$test[,1]
# mutemp_test_z <- sampler_z$predict(xdf_z_test)[,1]#samplestemp_z$test[,1]
if(sparse){
tempcounts <- fcount(sampler_z$getTrees()$var)
# print("line 766. tempcounts = ")
# print(tempcounts)
tempcounts <- tempcounts[tempcounts$x != -1, ]
# print("line 769. tempcounts = ")
# print(tempcounts)
var_count_z <- rep(0, p_z)
var_count_z[tempcounts$x] <- tempcounts$N
# print("line 773. var_count_z = ")
# print(var_count_z)
}
# print("length(mutemp_test_z) = ")
# print(length(mutemp_test_z))
#
# print("mutemp_test_z[1000:1010] = ")
# print(mutemp_test_z[1000:1010])
#
# print("mutemp_test_z[1:10] = ")
# print(mutemp_test_z[1:10])
#
# print("nrow(xdf_z_test) = ")
# print(nrow(xdf_z_test))
y_resids <- ystar[uncens_inds] - gamma1*(z[uncens_inds] - offsetz - mutemp_z[uncens_inds])
sampler_y$setResponse(y = y_resids)
sampler_y$setSigma(sigma = sqrt(phi1) )
# sampler_y$setSigma(sigma = sigest)
# sampler_y$model@node.scale <- 3
if(sparse){
tempmodel <- sampler_y$model
tempmodel@tree.prior@splitProbabilities <- s_y
sampler_y$setModel(newModel = tempmodel)
# print("check y probs")
# print("sampler_y@tree.prior@splitProbabilities = ")
# print(sampler_y@tree.prior@splitProbabilities)
}
# sampler_y$sampleTreesFromPrior()
# priormean_y <- sampler_y$predict(xdf_y)[1,]
# sampler_y$sampleNodeParametersFromPrior()
samplestemp_y <- sampler_y$run()
# mutemp_y <- rep(mean(y),n) #samplestemp_y$train[,1]
# mutemp_test_y <- rep(mean(y),ntest) # samplestemp_y$test[,1]
mutemp_y <- samplestemp_y$train[,1]
mutemp_test_y <- samplestemp_y$test[,1]
if(sparse){
tempcounts <- fcount(sampler_y$getTrees()$var)
# print("line 815. tempcounts = ")
# print(tempcounts)
tempcounts <- tempcounts[tempcounts$x != -1, ]
# print("line 824. tempcounts = ")
# print(tempcounts)
var_count_y <- rep(0, p_y)
var_count_y[tempcounts$x] <- tempcounts$N
# print("line 824. var_count_y = ")
# print(var_count_y)
}
# print("length(mutemp_test_y) = ")
# print(length(mutemp_test_y))
# if(sigest != samplestemp_y$sigma){
# print("sigest = ")
# print(sigest)
# print("dbarts sigma estimate =")
# print(samplestemp_y$sigma)
#
# df = data.frame(x.train,y)
# lmf = lm(y~.,df)
# sigest2 = summary(lmf)$sigma
#
# print("sigest2 = ")
# print(sigest2)
#
# # stop("sigest != samplestemp_y$sigma")
#
# }
# sigest <- samplestemp_y$sigma
# S0 <- 2*(sigest^2 - (1/(8*(G0^2))) - 4*(gamma0^2)*G0 )
#COMMENTING OUT INITIALIZATION FOR FIRST ITERATION
#MIGHT NEED TO EDIT THIS
# sampler$setResponse(y = z)
# sampler$setSigma(sigma = 1)
#sampler$setPredictor(x= Xmat.train$x, column = 1, forceUpdate = TRUE)
#mu = as.vector( alpha + X.mat %*% beta )
# sampler$sampleTreesFromPrior()
# samplestemp <- sampler$run()
#mutemp <- samplestemp$train[,1]
#suppose there are a number of samples
# print("sigma = ")
# sigma <- samplestemp$sigma
#
# mu <- samplestemp$train[,1]
# mutest <- samplestemp$test[,1]
#
# ystar <- rnorm(n,mean = mu, sd = sigma)
# ystartest <- rnorm(ntest,mean = mutest, sd = sigma)
#
# ystartestcens <-rtruncnorm(ntest, a = below_cens, b = above_cens, mean = mutest, sd = sigma)
#
# probcensbelow <- pnorm(below_cens, mean = mutest, sd = sigma)
# probcensabove <- 1 - pnorm(above_cens, mean = mutest, sd = sigma)
#save the first round of values
# if(n.burnin == 0){
# draw$Z.mat[,1] = z
# draw$Z.matcens[,1] = z[cens_inds]
# # draw$Z.matuncens[,1] = z[uncens_inds]
# draw$Z.matcensbelow[,1] = z[censbelow_inds]
# draw$Z.matcensabove[,1] = z[censabove_inds]
# draw$mu[,1] = mu
# draw$mucens[,1] = mu[cens_inds]
# draw$muuncens[,1] = mu[uncens_inds]
# draw$mucensbelow[,1] = mu[censbelow_inds]
# draw$mucensabove[,1] = mu[censabove_inds]
# draw$ystar[,1] = ystar
# draw$ystarcens[,1] = ystar[cens_inds]
# draw$ystaruncens[,1] = ystar[uncens_inds]
# draw$ystarcensbelow[,1] = ystar[censbelow_inds]
# draw$ystarcensabove[,1] = ystar[censabove_inds]
# draw$test.mu[,1] = mutest
# draw$test.y_nocensoring[,1] = ystartest
# draw$test.y_withcensoring[,1] = ystartestcens
# draw$test.probcensbelow[,1] = probcensbelow
# draw$test.probcensabove[,1] = probcensabove
# draw$sigma[1] <- sigma
# }
# print("line 964")
######### Begin Gibbs sampler ######################################################
# pb <- progress_bar$new(total = n.iter+n.burnin)
# pb <- progress_bar$new(
# format = " [:bar] :percent eta: :eta",
# total = n.iter+n.burnin, clear = FALSE, width= 60)
#loop through the Gibbs sampler iterations
for(iter in 1:(n.iter+n.burnin)){
# if(eq_by_eq){
# var_zdraw <- 1
# # sig_ydraw <- phi1
#
# }else{
# var_zdraw <- phi1/(gamma1^2+phi1)
# # sig_ydraw <- phi1
#
# }
# print("line 988")
####### samples of ystar conditional on everything else ######################
ystar[uncens0_inds] <- rtruncnorm(n_zeros, a= -Inf, b = 0,
mean = offsety + mutemp_y[sub_uncens0_inds] + gamma1*(z[uncens0_inds] - offsetz - mutemp_z[uncens0_inds]),
sd = sqrt(phi1) )
ystar[uncens1_inds] <- rtruncnorm(n_ones, a= 0, b = Inf,
mean = offsety + mutemp_y[sub_uncens1_inds] + gamma1*(z[uncens1_inds] - offsetz - mutemp_z[uncens1_inds]),
sd = sqrt(phi1) )
#//////////////////////////////////////////////////////////////////
temp_sd_z <- sqrt( phi1/(phi1+gamma1^2) )
######### #draw the latent outcome z ##########################
# z[cens_inds] <- rtruncnorm(n0, a= below_cens, b = above_cens, mean = mu[cens_inds], sd = sigma)
if(length(cens_inds)>0){
# temp_sd_y <- sqrt(phi1 + gamma1^2)
# print("mutemp_y[cens_inds] = ")
# print(mutemp_y[cens_inds])
# print("temp_sd_y = ")
# print(temp_sd_y)
#
# print("cens_inds = ")
# print(cens_inds)
# ystar[cens_inds] <- rnorm(n0, mean = mutemp_y[cens_inds], sd = temp_sd_y)
# temp_zmean_cens <- offsetz + mutemp_z[cens_inds] + (ystar[cens_inds] - mutemp_y[cens_inds])*gamma1/(phi1 + gamma1^2)
temp_zmean_cens <- offsetz + mutemp_z[cens_inds] #+ (ystar[cens_inds] - mutemp_y[cens_inds])*gamma1/(phi1 + gamma1^2)
z[cens_inds] <- rtruncnorm(n0, a= -Inf, b = 0, mean = temp_zmean_cens, sd = 1)
}
# temp_zmean_uncens <- offsetz + mutemp_z[uncens_inds] + (ystar[uncens_inds] - mutemp_y[uncens_inds])*gamma1/(phi1 + gamma1^2)
temp_zmean_uncens <- offsetz + mutemp_z[uncens_inds] + (ystar[uncens_inds] - mutemp_y)*gamma1/(phi1 + gamma1^2)
z[uncens_inds] <- rtruncnorm(n1, a= 0, b = Inf, mean = temp_zmean_uncens,
sd = temp_sd_z)
# z_epsilon <- z - offsetz - mutemp_z
# y_epsilon <- ystar - mutemp_y
z_epsilon <- z - offsetz - mutemp_z
y_epsilon <- rep(0, n)
y_epsilon[uncens_inds] <- ystar[uncens_inds] - mutemp_y
# print("temp_sd_z = ")
# print(temp_sd_z)
#
# print("mutemp_z = ")
# print(mutemp_z)
#
# print("temp_zmean_cens = ")
# print(temp_zmean_cens)
#
# print("z = ")
# print(z)
####### draw sums of trees for z #######################################################
#create residuals for z and set variance
# if(eq_by_eq){
# z_resids <- z - offsetz #z_epsilon
# sd_zdraw <- 1
# }else{
# #not sure about this step for tobit2b
# z_resids <- z - offsetz - y_epsilon*(gamma1/(phi1+gamma1^2))
# sd_zdraw <- sqrt(phi1 / (phi1 + gamma1^2) )
# }
z_resids <- z - offsetz #z_epsilon
z_resids[uncens_inds] <- z[uncens_inds] - offsetz - (ystar[uncens_inds] - mutemp_y)*gamma1/(phi1 + gamma1^2)
#set the response for draws of z trees
sampler_z$setResponse(y = z_resids)
#set the standard deivation
sampler_z$setSigma(sigma = 1)
weightstemp[uncens_inds] <- (gamma1^2 + phi1)/phi1
# print("weightstemp = ")
# print(weightstemp)
# print("Line 737")
# print("weightstemp = ")
# print(weightstemp)
#
# print("gamma1 = ")
# print(gamma1)
#
# print("phi1 = ")
# print(phi1)
sampler_z$setWeights(weights = weightstemp)
if(sparse){
tempmodel <- sampler_z$model
tempmodel@tree.prior@splitProbabilities <- s_z
sampler_z$setModel(newModel = tempmodel)
# print("check z probs")
# print("sampler_z@tree.prior@splitProbabilities = ")
# print(sampler_z@tree.prior@splitProbabilities)
}
# print("Line 741")
samplestemp_z <- sampler_z$run()
mutemp_z <- samplestemp_z$train[,1]
mutemp_test_z <- samplestemp_z$test[,1]
# mutemp_test_z <- sampler_z$test[,1]#samplestemp_z$test[,1]
# mutemp_test_z <- sampler_z$predict(xdf_z_test)[,1]#samplestemp_z$test[,1]
if(sparse){
tempcounts <- fcount(sampler_z$getTrees()$var)
# print("line 1041. tempcounts = ")
# print(tempcounts)
tempcounts <- tempcounts[tempcounts$x != -1, ]
var_count_z <- rep(0, p_z)
var_count_z[tempcounts$x] <- tempcounts$N
}
# print("length(mutemp_test_z) = ")
# print(length(mutemp_test_z))
#
# print("nrow(xdf_z_test) = ")
# print(nrow(xdf_z_test))
#update z_epsilon
z_epsilon <- z - offsetz - mutemp_z
####### draw sums of trees for y #######################################################
#create residuals for z and set variance
# print("y_epsilon = ")
#
# print(y_epsilon)
#
#
# print("z_epsilon = ")
#
# print(z_epsilon)
#
# print("gamma1 = ")
#
# print(gamma1)
# if(eq_by_eq){
# y_resids <- ystar[uncens_inds] - gamma1*z_epsilon[uncens_inds]
# sd_ydraw <- sqrt(phi1)
# }else{
# y_resids <- ystar[uncens_inds] - gamma1*z_epsilon[uncens_inds]
# sd_ydraw <- sqrt(phi1)
# }
y_resids <- ystar[uncens_inds] - gamma1*(z[uncens_inds] - offsetz - mutemp_z[uncens_inds])
sd_ydraw <- sqrt(phi1)
# print("y_resids = ")
#
# print(y_resids)
#set the response for draws of z trees
sampler_y$setResponse(y = y_resids)
#set the standard deviation
sampler_y$setSigma(sigma = sd_ydraw)
if(sparse){
tempmodel <- sampler_y$model
tempmodel@tree.prior@splitProbabilities <- s_y
sampler_y$setModel(newModel = tempmodel)
# print("check y probs")
# print("sampler_y@tree.prior@splitProbabilities = ")
# print(sampler_y@tree.prior@splitProbabilities)
}
samplestemp_y <- sampler_y$run()
mutemp_y <- samplestemp_y$train[,1]
mutemp_test_y <- samplestemp_y$test[,1]
if(sparse){
tempcounts <- fcount(sampler_y$getTrees()$var)
# print("line 1107. tempcounts = ")
# print(tempcounts)
tempcounts <- tempcounts[tempcounts$x != -1, ]
var_count_y <- rep(0, p_y)
var_count_y[tempcounts$x] <- tempcounts$N
}
#update z_epsilon
y_epsilon[uncens_inds] <- ystar[uncens_inds] - mutemp_y
############# Covariance matrix samples ##########################
if(cov_prior == "Ding"){
rho1 <- gamma1/sqrt(Sigma_mat[2,2])
sigz2 <- 1/rgamma(n = 1,
shape = nu0/2,
rate = cding/(2*(1- (rho1^2))))
z_epsilon2 <- sqrt(sigz2)*(z - offsetz - mutemp_z)
zsquares <- crossprod(z_epsilon2[uncens_inds], z_epsilon2[uncens_inds])[1]
ysquares <- crossprod(y_epsilon[uncens_inds], y_epsilon[uncens_inds])[1]
zycross <- crossprod(z_epsilon2[uncens_inds], y_epsilon[uncens_inds])[1]
Stemp <- cbind(c(ysquares,zycross),
c(zycross, zsquares))
tempsigma <- rinvwishart(nu = n1 + nu0,
S = Stemp+cding*diag(2))
transmat <- cbind(c(1,0),c(0,1/sqrt(tempsigma[2,2])))
tempomega <- (transmat %*% tempsigma) %*% transmat
temprho <- tempomega[1,2]/(sqrt(tempomega[1,1]))
gamma1 <- tempomega[1,2]
phi1 <- tempomega[1,1] - (gamma1^2)
# if(tempomega[2,2] != 1){
# print("tempomega[2,2] = ")
# print(tempomega[2,2])
# }
#
# if(phi1 < 0){
# print("phi1 = ")
# print(phi1)
# }
}else{
########### Simultaneous phi and gamma draw #####################
if(simultaneous_covmat == TRUE){
if(cov_prior == "VH"){
h_num <- (gamma0/tau) + crossprod(z_epsilon[uncens_inds], y_epsilon[uncens_inds])[1]
a_temp <- (1/tau) + crossprod(z_epsilon[uncens_inds], z_epsilon[uncens_inds])[1]
h_temp <- h_num/a_temp
k_temp <- ((gamma0^2)/tau)+S0 +
crossprod(y_epsilon[uncens_inds], y_epsilon[uncens_inds])[1] -
((h_num^2)/(a_temp))
phi1 <- 1/rgamma(n = 1,
shape = (nzero + n1 )/2,
rate = k_temp/2)
gamma1 <- rnorm(n = 1, mean = h_temp, sd = sqrt(phi1/a_temp))
}else{
stop("If simultaneous_covmat == TRUE, then must use Van Hasselt Covariance prior. Set cov_prior to VH.")
}
}else{
######### set parameters for gamma draw ######################################################
# if(cov_prior == TRUE){
# G0 <- tau*phi1
# }
if(cov_prior == "VH"){
G0draw <- tau*phi1
}else{
if(cov_prior == "Omori"){
G0draw <- G0
}else{
mixind <- rbinom(n = 1,size = 1,prob = mixprob)
if(mixind == 1){
G0draw <- tau*phi1
}else{
G0draw <- G0
}
}
}
# G1inv <- (1/G0) + (1/phi1)*crossprod(z_epsilon)
G1inv <- (1/G0draw) + (1/phi1)*crossprod(z_epsilon[uncens_inds])[1]
# G1inv <- (1/tau) + (1/phi1)*crossprod(z_epsilon[uncens_inds])
G1 <- (1/G1inv)#[1,1]
# gamma_one <- (G1*( (1/G0draw)*gamma0 + (1/phi1)*crossprod(z_epsilon , y_epsilon ) ))[1,1]
gamma_one <- (G1*( (1/G0draw)*gamma0 + (1/phi1)*crossprod(z_epsilon[uncens_inds] , y_epsilon[uncens_inds] )[1] ))
# gamma_one <- (G1*( (1/tau)*gamma0 + (1/phi1)*crossprod(z_epsilon[uncens_inds] , y_epsilon[uncens_inds] ) ))[1,1]
# print("phi1 = ")
# print(phi1)
# print("G0draw = ")
# print(G0draw)
#
# print("(G1*( (1/G0draw)*gamma0 + (1/phi1)*crossprod(z_epsilon[uncens_inds] , y_epsilon[uncens_inds] ) )) = ")
# print((G1*( (1/G0draw)*gamma0 + (1/phi1)*crossprod(z_epsilon[uncens_inds] , y_epsilon[uncens_inds] ) )))
#
# print("gamma_one = ")
# print(gamma_one)
#
# print("crossprod(z_epsilon , y_epsilon ) = ")
# print(crossprod(z_epsilon , y_epsilon ))
#
# print("crossprod(z_epsilon ) = ")
# print(crossprod(z_epsilon ))
#
# print("crossprod(z_epsilon[uncens_inds])[1] = ")
# print(crossprod(z_epsilon[uncens_inds])[1] )
#
# print("G1 = ")
# print(G1)
gamma1 <- rnorm(n = 1, mean = gamma_one, sd = sqrt(G1) )
# print("gamma1 = ")
# print(gamma1)
######### set parameters for phi draw ######################################################
n_one <- nzero + n1 + 1
# print("S0 = ")
# print(S0)
# print("(gamma1^2)*crossprod(z_epsilon) = ")
# print((gamma1^2)*crossprod(z_epsilon))
#
# print("2*gamma1*crossprod(z_epsilon , y_epsilon ) = ")
# print(2*gamma1*crossprod(z_epsilon , y_epsilon ))
#
# print("crossprod(y_epsilon) = ")
# print(crossprod(y_epsilon))
# S1 <- S0 + (gamma1^2)*crossprod(z_epsilon) - 2*gamma1*crossprod(z_epsilon , y_epsilon ) + crossprod(y_epsilon)
S1 <- 0 #S0 + (gamma1^2)/G0 + gamma1*crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] ) + crossprod(y_epsilon)
if(cov_prior == "VH"){
# S1 <- S0 + (gamma1^2)/tau + gamma1*crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] ) + crossprod(y_epsilon)
S1 <- S0 + (gamma1^2)/tau +
crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] )[1] # + crossprod(y_epsilon)
}else{
if(cov_prior == "Omori"){
S1 <- S0 + #+ (gamma1^2)/G0 +
crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] )[1] #+crossprod(y_epsilon)[1]
}else{
mixind <- rbinom(n = 1,size = 1,prob = mixprob)
if(mixind == 1){
# S1 <- S0 + (gamma1^2)/tau + gamma1*crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] ) + crossprod(y_epsilon)
S1 <- S0 + (gamma1^2)/tau +
crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] )[1] # + crossprod(y_epsilon)
}else{
S1 <- S0 + #+ (gamma1^2)/G0 +
crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] )[1] #+crossprod(y_epsilon)[1]
}
}
}
# print("S1 = ")
# print(S1)
# print("n_one = ")
# print(n_one)
# print("Line 883 phi1 = ")
# print(phi1)
# draw from inverse gamma
phi1 <- 1/rgamma(n = 1, shape = n_one/2, rate = S1/2)
# print("Line 890 phi1 = ")
# print(phi1)
#
# print("Line 890 n_one = ")
# print(n_one)
#
# print("Line 890 S1 = ")
# print(S1)
# print("n1 = ")
# print(n1)
} # end of else statement, for simultaneous_covmat = FALSE
}
######### update Sigma matrix #####################################################
Sigma_mat <- cbind(c(1,gamma1),c(gamma1,phi1+gamma1^2))
Sigma_orig_scale <- cbind(c(1,tempsd*gamma1),c(tempsd*gamma1, (tempsd^2)*(phi1+gamma1^2)) )
###### Accelerated sampler ###############################
# if(accelerate == TRUE){
#
# meanmu_z <- (min(z - offsetz) +max(z- offsetz))/(2*n.trees_censoring)
#
# # the variance should be zero?
# sigmu_z <- (max(z- offsetz) - min(z- offsetz))/(2*2*sqrt(n.trees_censoring))
# #set prior parameter values
#
#
# #if prior mean for mu parameters is zero (does this make sense? require an offset for y?)
#
# nu1 <- sum(sampler_z$getTrees@.Data()$var ==-1) - nzero + 1
#
#
# asquared <- (1/phi1)*(S0 + crossprod(y_epsilon[uncens_inds]))
#
# znodestemp <- sampler_z$getTrees@.Data()$value[sampler_z$getTrees@.Data()$var!=-1]
#
# bsquared <- (1 + (gamma1^2)/phi1)*crossprod(z_epsilon) +
# (1/sigmu_z)*crossprod(znodestemp) + (gamma1^2)*(1/G0)
#
#
# if(sqrt(asquared*bsquared) > 150^2){
# print("GIG sample will be slow.")
# }
#
# #candidate g parameer value
# gprime <- rgig(n = 1, lambda = nu1/2, chi = asquared, psi = bsquared )
#
#
#
#
# probaccept <- min(1, exp((gprime-1)* ((1/sigmu_z)*sum(znodestemp)*meanmu_z +
# gamma1*gamma0/G0 ) ) )
#
# g_accepted <- 1
#
# #check if accept
# accept_bin <- rbinom(n = 1,size = 1, prob = probaccept)
#
# if(is.na(accept_bin)){
#
# print("accept_bin is na. probaccept =")
# print(probaccept)
#
# print("gprime = ")
# print(gprime)
#
# print("sigmu_z = ")
# print(sigmu_z)
#
# print("sum(znodestemp) = ")
# print(sum(znodestemp))
#
# print("meanmu_z = ")
# print(meanmu_z)
#
# }
#
# if(accept_bin == 1){
# g_accepted <- gprime
#
# phi1 <- (gprime^2)*phi1
#
# gamma1 <- gprime*gamma1
#
# mutemp_z <- gprime*mutemp_z
#
# z <- gprime*z
#
# }
#
# }
########### splitting probability draws #############################
if (sparse & (iter > floor(n.burnin * 0.5))) {
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("var_count_z = ")
# print(var_count_z)
# print("p_z = ")
# print(p_z)
# print("alpha_s_z = ")
# print(alpha_s_z)
# }
s_update_z <- update_s(var_count_z, p_z, alpha_s_z)
s_z <- s_update_z[[1]]
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("s_update_z = ")
# print(s_update_z)
# }
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("var_count_y = ")
# print(var_count_y)
# print("p_y = ")
# print(p_y)
# print("alpha_s_y = ")
# print(alpha_s_y)
# }
s_update_y <- update_s(var_count_y, p_y, alpha_s_y)
s_y <- s_update_y[[1]]
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("s_update_y = ")
# print(s_update_y)
# }
if(alpha_split_prior){
alpha_s_z <- update_alpha(s_z, alpha_scale_z, alpha_a_z, alpha_b_z, p_z, s_update_z[[2]])
alpha_s_y <- update_alpha(s_y, alpha_scale_y, alpha_a_y, alpha_b_y, p_y, s_update_y[[2]])
}
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("alpha_s_z = ")
# print(alpha_s_z)
# print("alpha_s_y = ")
# print(alpha_s_y)
# }
}
###### Store results ###############################
# print("iter = ")
# print(iter)
if(iter > n.burnin){
iter_min_burnin <- iter-n.burnin
#NOTE y and z training sample values saved here
#do not correspond to the the same means and errors as
#the test values and expectations saved here.
#However, they are the values to which the trees in this round were fitted.
#draw z and y for test observations
zytest <- matrix(NA, nrow = ntest, ncol = 2)
if(fast == TRUE){
zytest <- Rfast::rmvnorm(n = ntest,
mu = c(0, 0),
sigma = Sigma_mat)
}else{
zytest <- mvrnorm(n = ntest,
mu = c(0, 0),
Sigma = Sigma_mat)
}
# print("length(mutemp_test_z) = ")
# print(length(mutemp_test_z))
#
# print("offsetz = ")
# print(offsetz)
#
# print("length(zytest[,1]) = ")
# print(length(zytest[,1]))
zytest[,1] <- zytest[,1] + offsetz + mutemp_test_z
zytest[,2] <- zytest[,2] + mutemp_test_y
# for(i in 1:ntest){
# zytest[i,] <- mvrnorm(n = 1,
# mu = c(offsetz + mutemp_test_z[i], mutemp_test_y[i]),
# Sigma = Sigma_mat)
# }
if(fast == TRUE){
probcens_train <- fastpnorm(- mutemp_z[uncens_inds] - offsetz )
probcens_test <- fastpnorm(- mutemp_test_z - offsetz)
prob_y_train <- 1 - fastpnorm(- mutemp_y - offsety )
prob_y_test <- 1 - fastpnorm(- mutemp_test_y - offsety )
}else{
probcens_train <- pnorm(- mutemp_z[uncens_inds] - offsetz )
probcens_test <- pnorm(- mutemp_test_z - offsetz)
prob_y_train <- 1 - pnorm(- mutemp_y - offsety )
prob_y_test <- 1 - pnorm(- mutemp_test_y - offsety )
}
#calculate conditional expectation
# condexptrain <- mutemp_y + gamma1*(dnorm(- mutemp_z - offsetz ))/(1-probcens_train)
# condexptrain <- mutemp_y + gamma1*(dnorm(- mutemp_z[uncens_inds] - offsetz ))/(1-probcens_train)
# condexptest <- mutemp_test_y + gamma1*(dnorm(- mutemp_test_z - offsetz ))/(1-probcens_test)
temp_ztrain <- mutemp_z[uncens_inds] + offsetz
temp_ztest <- mutemp_test_z + offsetz
#
# if(fast == TRUE){
# IMR_train <- exp( dnorm(temp_ztrain,log=T) - log(fastpnorm(temp_ztrain) ))
# IMR_test <- exp( dnorm(temp_ztest,log=T) - log(fastpnorm(temp_ztest) ))
#
# # IMR_train <- fastnormdens(temp_ztrain)/fastpnorm(temp_ztrain)
# # IMR_test <- fastnormdens(temp_ztest)/fastpnorm(temp_ztest)
# }else{
IMR_train <- exp( dnorm(temp_ztrain,log=T) - pnorm(temp_ztrain,log.p = T) )
IMR_test <- exp( dnorm(temp_ztest,log=T) - pnorm(temp_ztest,log.p = T) )
# }
condexptrain <- mutemp_y + gamma1*IMR_train
condexptest <- mutemp_test_y + gamma1*IMR_test
# draw$Z.mat_train[,iter_min_burnin] <- z
# draw$Z.mat_test[,iter_min_burnin] <- zytest[,1]
# draw$Y.mat_train = array(NA, dim = c(n, n.iter)),
# draw$Y.mat_test = array(NA, dim = c(ntest, n.iter)),
draw$mu_y_train[, iter_min_burnin] <- mutemp_y*tempsd+tempmean
draw$mu_y_test[, iter_min_burnin] <- mutemp_test_y*tempsd+tempmean
# draw$mucens_y_train[, iter_min_burnin] <- mutemp_y[cens_inds]
# draw$muuncens_y_train[, iter_min_burnin] <- mutemp_y[uncens_inds]
draw$muuncens_y_train[, iter_min_burnin] <- mutemp_y*tempsd+tempmean
draw$mu_z_train[, iter_min_burnin] <- mutemp_z
draw$mu_z_test[, iter_min_burnin] <- mutemp_test_z
draw$train.probcens[, iter_min_burnin] <- probcens_train
draw$test.probcens[, iter_min_burnin] <- probcens_test
draw$train.prob.y[, iter_min_burnin] <- prob_y_train
draw$test.prob.y[, iter_min_burnin] <- prob_y_test
draw$cond_exp_train[, iter_min_burnin] <- condexptrain*tempsd+tempmean
draw$cond_exp_test[, iter_min_burnin] <- condexptest*tempsd+tempmean
draw$ystar_train[, ] <- ystar*tempsd+tempmean
draw$ystar_test[, iter_min_burnin] <- zytest[,2]*tempsd+tempmean
draw$zstar_train[,iter_min_burnin] <- z
draw$zstar_test[,iter_min_burnin] <- zytest[,1]
draw$ycond_draws_train[[iter_min_burnin]] <- ystar[z >=0]*tempsd+tempmean
draw$ycond_draws_test[[iter_min_burnin]] <- zytest[,2][zytest[,1] >= 0]*tempsd+tempmean
draw$Sigma_draws[,, iter_min_burnin] <- Sigma_orig_scale#Sigma_mat
if(is.numeric(censored_value)){
# uncondexptrain <- censored_value*probcens_train + mutemp_y*(1- probcens_train ) + gamma1*dnorm(- mutemp_z - offsetz )
uncondexptrain <- censored_value*probcens_train + mutemp_y*(1- probcens_train ) + gamma1*dnorm(- mutemp_z[uncens_inds] - offsetz )
uncondexptest <- censored_value*probcens_test + mutemp_test_y*(1- probcens_test ) + gamma1*dnorm(- mutemp_test_z - offsetz)
draw$uncond_exp_train[, iter_min_burnin] <- uncondexptrain*tempsd+tempmean
draw$uncond_exp_test[, iter_min_burnin] <- uncondexptest*tempsd+tempmean
# draw$ydraws_train[, iter_min_burnin] <- ifelse(z < 0, censored_value, ystar )
draw$ydraws_test[, iter_min_burnin] <- ifelse(zytest[,1] < 0, censored_value, zytest[,2] )*tempsd+tempmean
}
if(sparse){
draw$alpha_s_y_store[iter_min_burnin] <- alpha_s_y
draw$alpha_s_z_store[iter_min_burnin] <- alpha_s_z
draw$var_count_y_store[iter_min_burnin,] <- var_count_y
draw$var_count_z_store[iter_min_burnin,] <- var_count_z
draw$s_prob_y_store[iter_min_burnin,] <- s_y
draw$s_prob_z_store[iter_min_burnin,] <- s_z
}
} # end if iter > burnin
# pb$tick()
if(iter %% print.opt == 0){
print(paste("Gibbs Iteration", iter))
# print(c(sigma2.alpha, sigma2.beta))
}
}#end iterations of Giibs sampler
return(draw)
}
EoghanONeill/TobitBART documentation built on Feb. 6, 2025, 6:52 a.m.
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Defines functions tbart2binary
Documented in tbart2binary
#' @title Type II Tobit Bayesian Additive Regression Trees implemented using MCMC
#'
#' @description Type II Tobit Bayesian Additive Regression Trees implemented using MCMC
#' @import dbarts
#' @import truncnorm
#' @import MASS
#' @import GIGrvg
#' @import mvtnorm
#' @import sampleSelection
#' @import progress
#' @import LaplacesDemon
#' @param x.train The outcome model training covariate data for all training observations. Number of rows equal to the number of observations. Number of columns equal to the number of covariates.
#' @param x.test The outcome model test covariate data for all test observations. Number of rows equal to the number of observations. Number of columns equal to the number of covariates.
#' @param w.train The censoring model training covariate data for all training observations. Number of rows equal to the number of observations. Number of columns equal to the number of covariates.
#' @param w.test The censoring model test covariate data for all test observations. Number of rows equal to the number of observations. Number of columns equal to the number of covariates.
#' @param y The training data vector of outcomes. A continuous, censored outcome variable. Censored observations must be included with values equal to censored_value
#' @param n.iter Number of iterations excluding burnin.
#' @param n.burnin Number of burnin iterations.
#' @param censored_value The value taken by censored observations
#' @param gamma0 The mean of the normal prior on the covariance of the errors in the censoring and outcome models.
#' @param G0 The variance of the normal prior on the covariance of the errors in the censoring and outcome models (only if cov_prior equals Omori).
#' @param nzero A prior parameter which when divided by 2 gives the mean of the normal prior on phi, where phi*gamma is the variance of the errors of the outcome model.
#' @param S0 A prior parameter which when divided by 2 gives the variance of the normal prior on phi, where phi*gamma is the variance of the errors of the outcome model.
#' @param sigest Estimate of variance of hte error term.
#' @param n.trees_outcome (dbarts control option) A positive integer giving the number of trees used in the outcome model sum-of-trees formulation.
#' @param n.trees_censoring (dbarts control option) A positive integer giving the number of trees used in the censoring model sum-of-trees formulation.
#' @param n.chains (dbarts control option) A positive integer detailing the number of independent chains for the dbarts sampler to use (more than one chain is unlikely to improve speed because only one sample for each call to dbarts).
#' @param n.threads (dbarts control option) A positive integer controlling how many threads will be used for various internal calculations, as well as the number of chains. Internal calculations are highly optimized so that single-threaded performance tends to be superior unless the number of observations is very large (>10k), so that it is often not necessary to have the number of threads exceed the number of chains.
#' @param printEvery (dbarts control option)If verbose is TRUE, every printEvery potential samples (after thinning) will issue a verbal statement. Must be a positive integer.
#' @param printCutoffs (dbarts control option) A non-negative integer specifying how many of the decision rules for a variable are printed in verbose mode
#' @param rngKind (dbarts control option) Random number generator kind, as used in set.seed. For type "default", the built-in generator will be used if possible. Otherwise, will attempt to match the built-in generator’s type. Success depends on the number of threads.
#' @param rngNormalKind (dbarts control option) Random number generator normal kind, as used in set.seed. For type "default", the built-in generator will be used if possible. Otherwise, will attempt to match the built-in generator’s type. Success depends on the number of threads and the rngKind
#' @param rngSeed (dbarts control option) Random number generator seed, as used in set.seed. If the sampler is running single-threaded or has one chain, the behavior will be as any other sequential algorithm. If the sampler is multithreaded, the seed will be used to create an additional pRNG object, which in turn will be used sequentially seed the threadspecific pRNGs. If equal to NA, the clock will be used to seed pRNGs when applicable.
#' @param updateState (dbarts control option) Logical setting the default behavior for many sampler methods with regards to the immediate updating of the cached state of the object. A current, cached state is only useful when saving/loading the sampler.
#' @param tree_power_y Tree prior parameter for outcome model.
#' @param tree_base_y Tree prior parameter for outcome model.
#' @param tree_power_z Tree prior parameter for selection model.
#' @param tree_base_z Tree prior parameter for selection model.
#' @param node.prior (dbarts option) An expression of the form dbarts:::normal or dbarts:::normal(k) that sets the prior used on the averages within nodes.
#' @param resid.prior (dbarts option) An expression of the form dbarts:::chisq or dbarts:::chisq(df,quant) that sets the prior used on the residual/error variance
#' @param proposal.probs (dbarts option) Named numeric vector or NULL, optionally specifying the proposal rules and their probabilities. Elements should be "birth_death", "change", and "swap" to control tree change proposals, and "birth" to give the relative frequency of birth/death in the "birth_death" step.
#' @param sigmadbarts (dbarts option) A positive numeric estimate of the residual standard deviation. If NA, a linear model is used with all of the predictors to obtain one.
#' @param print.opt Print every print.opt number of Gibbs samples.
#' @param eq_by_eq If TRUE, implements sampler equation by equation (as in BAVART by Huber and Rossini (2021)). If FALSE, implements sampler in similar approach to SUR-BART (Chakraborty 2016) or MPBART (Kindo 2016).
#' @param accelerate If TRUE, add extra parameter for accelerated sampler as descibed by Omori (2007).
#' @param cov_prior Prior for the covariance of the error terms. If VH, apply the prior of van Hasselt (2011), N(gamma0, tau*phi), imposing dependence between gamma and phi. If Omori, apply the prior N(gamma0,G0). If mixture, then a mixture of the VH and Omori priors with probability mixprob applied to the VH prior.
#' @param mixprob If cov_prior equals Mixture, then mixprob is the probability applied to the Van Hasselt covariance prior, and one minus mixprob is the probability applied to the Omori prior.
#' @param tau Parameter for the prior of van Hasselt (2011) on the covariance of the error terms.
#' @param simultaneous_covmat If TRUE, jointly sample the parameters that determine the covariance matrix instead of sampling from separate full conditionals.
#' @param fast If equal to true, takes faster samples of z and y and makes faster approximate calculations of selection probabilities.
#' @param nu0 For the inverseWishart prior Winv(nu0,c*I_2) of Ding (2014) on an unidentified unrestricted covariance matrix. nu = 3 corresponds to a uniform correlation prior. nu > 3 centers the correlation prior at 0, while nu < 3 places more prior probability on selection on unobservables.
#' @param quantsig For the inverseWishart prior Winv(nu0,c*I_2) of Ding (2014). The parameter c is determined by quantsig so that the marginal prior on the standard deviation of the outcome has 90% quantile equal to an estimate from a linear tobit model or sigest if sigest is not NA.
#' @param sparse If equal to TRUE, use Linero Dirichlet prior on splitting probabilities
#' @param alpha_a_y Linero alpha prior parameter for outcome equation splitting probabilities
#' @param alpha_b_y Linero alpha prior parameter for outcome equation splitting probabilities
#' @param alpha_a_z Linero alpha prior parameter for selection equation splitting probabilities
#' @param alpha_b_z Linero alpha prior parameter for selection equation splitting probabilities
#' @param alpha_split_prior If true, set hyperprior for Linero alpha parameter
#' @export
#' @return The following objects are returned:
#' \item{Z.mat_train}{Matrix of draws of censoring model latent outcomes for training observations. Number of rows equals number of training observations. Number of columns equals n.iter . Rows are ordered in order of observations in the training data.}
#' \item{Z.mat_test}{Matrix of draws of censoring model latent outcomes for test observations. Number of rows equals number of test observations. Number of columns equals n.iter . Rows are ordered in order of observations in the test data.}
#' \item{Y.mat_train}{Matrix of draws of outcome model latent outcomes for training observations. Number of rows equals number of training observations. Number of columns equals n.iter . Rows are ordered in order of observations in the training data.}
#' \item{Y.mat_test}{Matrix of draws of outcome model latent outcomes for test observations. Number of rows equals number of test observations. Number of columns equals n.iter . Rows are ordered in order of observations in the test data.}
#' \item{mu_y_train}{Matrix of draws of the outcome model sums of terminal nodes, i.e. f(x_i), for all training observations. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{mu_y_test}{Matrix of draws of the outcome model sums of terminal nodes, i.e. f(x_i), for all test observations. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{mucens_y_train}{Matrix of draws of the outcome model sums of terminal nodes, i.e. f(x_i), for all censored training observations. Number of rows equals number of censored training observations. Number of columns equals n.iter .}
#' \item{muuncens_y_train}{Matrix of draws of the outcome model sums of terminal nodes, i.e. f(x_i), for all uncensored training observations. Number of rows equals number of uncensored training observations. Number of columns equals n.iter .}
#' \item{mu_z_train}{Matrix of draws of the censoring model sums of terminal nodes, i.e. f(w_i), for all training observations. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{mu_z_test}{Matrix of draws of the censoring model sums of terminal nodes, i.e. f(w_i), for all test observations. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{train.probcens}{Matrix of draws of probabilities of training sample observations being censored. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{test.probcens}{Matrix of draws of probabilities of test sample observations being censored. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{cond_exp_train}{Matrix of draws of the conditional (i.e. possibly censored) expectations of the outcome for all training observations. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{cond_exp_test}{Matrix of draws of the conditional (i.e. possibly censored) expectations of the outcome for all test observations. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{uncond_exp_train}{Only defined if censored_value is a number. Matrix of draws of the unconditional (i.e. possibly censored) expectations of the outcome for all training observations. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{uncond_exp_test}{Only defined if censored_value is a number. Matrix of draws of the unconditional (i.e. possibly censored) expectations of the outcome for all test observations. Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{ystar_train}{Matrix of training sample draws of the outcome assuming uncensored. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{ystar_test}{Matrix of test sample draws of the outcome assuming uncensored . Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{zstar_train}{Matrix of training sample draws of the censoring model latent outcome. Number of rows equals number of training observations. Number of columns equals n.iter.}
#' \item{zstar_test}{Matrix of test sample draws of the censoring model latent outcome. Number of rows equals number of test observations. Number of columns equals n.iter.}
#' \item{ydraws_train}{Only defined if censored_value is a number. Matrix of training sample unconditional (i.e. possibly censored) draws of the outcome. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{ydraws_test}{Only defined if censored_value is a number. Matrix of test sample unconditional (i.e. possibly censored) draws of the outcome . Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{ycond_draws_train}{List of training sample conditional (i.e. zstar >0 for draw) draws of the outcome. Number of rows equals number of training observations. Number of columns equals n.iter .}
#' \item{ycond_draws_test}{List of test sample conditional (i.e. zstar >0 for draw) draws of the outcome . Number of rows equals number of test observations. Number of columns equals n.iter .}
#' \item{Sigma_draws}{3 dimensional array of MCMC draws of the covariance matrix for the censoring and outcome error terms. The numbers of rows and columns equal are equal to 2. The first row and column correspond to the censoring model. The second row and column correspond to the outcome model. The number of slices equals n.iter . }
#' \item{alpha_s_y_store}{For Dirichlet prior on splitting probabilities in outcome equation, vector of alpha hyperparameter draws for each iteration.}
#' \item{alpha_s_z_store}{For Dirichlet prior on splitting probabilities in selection equation, vector of alpha hyperparameter draws for each iteration }
#' \item{var_count_y_store}{Matrix of counts of splits on each variable in outcome observation. The number of rows is the number of potential splitting variables. The number of columns is the number of post-burn-in iterations.}
#' \item{var_count_z_store}{Matrix of counts of splits on each variable in selection observation. The number of rows is the number of potential splitting variables. The number of columns is the number of post-burn-in iterations. }
#' \item{s_prob_y_store}{Splitting probabilities for the outcome equation. The number of rows is the number of potential splitting variables. The number of columns is the number of post-burn-in iterations. }
#' \item{s_prob_z_store}{Splitting probabilities for the selection equation. The number of rows is the number of potential splitting variables. The number of columns is the number of post-burn-in iterations. }
#' @examples
#'
#'#example taken from Zhang, J., Li, Z., Song, X., & Ning, H. (2021). Deep Tobit networks: A novel machine learning approach to microeconometrics. Neural Networks, 144, 279-296.
#'
#'
#'
#' #Type II tobit simulation
#'
#' num_train <- 5000
#'
#' #consider increasing the number of covariates
#'
#' Xmat_train <- matrix(NA,nrow = num_train,
#' ncol = 8)
#'
#' Xmat_train[,1] <- runif(num_train, min = -1, max = 1)
#' Xmat_train[,2] <- rf(num_train,20,20)
#' Xmat_train[,3] <- rbinom(num_train, size = 1, prob = 0.75)
#' Xmat_train[,4] <- rnorm(num_train, mean = 1, sd = 1)
#' Xmat_train[,5] <- rnorm(num_train)
#' Xmat_train[,6] <- rbinom(num_train, size = 1, prob = 0.5)
#' Xmat_train[,7] <- rf(num_train,20,200)
#' Xmat_train[,8] <- runif(num_train, min = 0, max = 2)
#'
#' #it would be better to test performance of the models when there is correlation in the error terms.
#' varepsilon1_train <- rnorm(num_train, mean = 0, sd = sqrt(0.00025))
#' varepsilon2_train <- rnorm(num_train, mean = 0, sd = sqrt(0.00025))
#'
#' y1star_train <- 1 - 0.75*Xmat_train[,1] + 0.75*Xmat_train[,2] -
#' 0.5*Xmat_train[,4] - 0.5*Xmat_train[,6] - 0.25*Xmat_train[,1]^2 -
#' 0.75*Xmat_train[,1]*Xmat_train[,4] - 0.25*Xmat_train[,1]*Xmat_train[,2] -
#' 1*Xmat_train[,1]*Xmat_train[,6] + 0.5*Xmat_train[,2]*Xmat_train[,6] +
#' varepsilon1_train
#'
#' y2star_train <- 1 + 0.25*Xmat_train[,4] - 0.75*Xmat_train[,6] +
#' 0.5*Xmat_train[,7] + 0.25*Xmat_train[,8] +
#' 0.25*Xmat_train[,4]^2 + 0.75*Xmat_train[,7]^2 + 0.5*Xmat_train[,8]^2 -
#' 1*Xmat_train[,4]*Xmat_train[,6] + 0.5*Xmat_train[,4]*Xmat_train[,8] +
#' 1*Xmat_train[,6]*Xmat_train[,7] - 0.25*Xmat_train[,7]*Xmat_train[,8] +
#' varepsilon2_train
#'
#' y2obs_train <- ifelse(y1star_train>0, y2star_train,0)
#'
#' #Type II tobit simulation
#'
#' num_test <- 5000
#'
#' #consider increasing the number of covariates
#'
#' Xmat_test <- matrix(NA,nrow = num_test,
#' ncol = 8)
#'
#' Xmat_test[,1] <- runif(num_test, min = -1, max = 1)
#' Xmat_test[,2] <- rf(num_test,20,20)
#' Xmat_test[,3] <- rbinom(num_test, size = 1, prob = 0.75)
#' Xmat_test[,4] <- rnorm(num_test, mean = 1, sd = 1)
#' Xmat_test[,5] <- rnorm(num_test)
#' Xmat_test[,6] <- rbinom(num_test, size = 1, prob = 0.5)
#' Xmat_test[,7] <- rf(num_test,20,200)
#' Xmat_test[,8] <- runif(num_test, min = 0, max = 2)
#'
#' #it would be better to test performance of the models when there is correlation in the error terms.
#' varepsilon1_test <- rnorm(num_test, mean = 0, sd = sqrt(0.00025))
#' varepsilon2_test <- rnorm(num_test, mean = 0, sd = sqrt(0.00025))
#'
#' y1star_test <- 1 - 0.75*Xmat_test[,1] + 0.75*Xmat_test[,2] -
#' 0.5*Xmat_test[,4] - 0.5*Xmat_test[,6] - 0.25*Xmat_test[,1]^2 -
#' 0.75*Xmat_test[,1]*Xmat_test[,4] - 0.25*Xmat_test[,1]*Xmat_test[,2] -
#' 1*Xmat_test[,1]*Xmat_test[,6] + 0.5*Xmat_test[,2]*Xmat_test[,6] +
#' varepsilon1_test
#'
#' y2star_test <- 1 + 0.25*Xmat_test[,4] - 0.75*Xmat_test[,6] +
#' 0.5*Xmat_test[,7] + 0.25*Xmat_test[,8] +
#' 0.25*Xmat_test[,4]^2 + 0.75*Xmat_test[,7]^2 + 0.5*Xmat_test[,8]^2 -
#' 1*Xmat_test[,4]*Xmat_test[,6] + 0.5*Xmat_test[,4]*Xmat_test[,8] +
#' 1*Xmat_test[,6]*Xmat_test[,7] - 0.25*Xmat_test[,7]*Xmat_test[,8] +
#' varepsilon2_test
#'
#' y2obs_test <- ifelse(y1star_test>0, y2star_test,0)
#'
#' y2response_test <- ifelse(y1star_test>0, 1,0)
#'
#' tbartII_example <- tbart2c(Xmat_train,
#' Xmat_test,
#' Xmat_train,
#' Xmat_test,
#' y2obs_train,
#' n.iter=5000,
#' n.burnin=1000,
#' censored_value = 0,
#' eq_by_eq = TRUE)
#'
#'
#' pred_probs_tbart2_test <- rowMeans(tbartII_example$test.probcens)
#'
#' #Training (within-sample) Prediction Realization Table
#'
#' cutoff_point <- mean(y2obs_train>0)
#'
#' test_bin_preds <- ifelse(1 - pred_probs_tbart2_test > cutoff_point,1,0)
#'
#' #Training (within-sample) Prediction Realization Table
#'
#' pred_realization_test <- rbind(cbind(table(y2response_test, test_bin_preds)/length(y2response_test),
#' apply(table(y2response_test, test_bin_preds)/length(y2response_test),1,sum)),
#' c(t(apply(table(y2response_test, test_bin_preds)/length(y2response_test),2,sum)), 1))
#'
#' hit_rate_test <- pred_realization_test[1,1] +pred_realization_test[2,2]
#'
#' testpreds_tbart2 <- rowMeans(tbartII_example$uncond_exp_test)
#'
#' sqrt(mean((y2obs_test - testpreds_tbart2 )^2 ))
#'
#' @export
tbart2binary <- function(x.train,
x.test,
w.train,
w.test,
y,
n.iter=1000,
n.burnin=100,
censored_value = NA,
gamma0 = 0,
G0=1,
nzero = 6,#0.002,
S0= 12,#0.002,
sigest = NA,
n.trees_outcome = 50L,
n.trees_censoring = 50L,
n.burn = 0L,
n.samples = 1L,
n.thin = 1L,
n.chains = 1L,
n.threads = 1L, #guessNumCores(),
printEvery = 100L,
printCutoffs = 0L,
rngKind = "default",
rngNormalKind = "default",
rngSeed = NA_integer_,
updateState = TRUE,
tree_power_z = 2,
tree_power_y = 2,
tree_base_z = 0.95,
tree_base_y = 0.95,
node.prior = dbarts:::normal,
resid.prior = dbarts:::chisq,
proposal.probs = c(birth_death = 0.5, swap = 0.1, change = 0.4, birth = 0.5),
sigmadbarts = NA_real_,
print.opt = 100,
eq_by_eq = TRUE,
# accelerate = FALSE,
cov_prior = "Ding",
tau = 1/3,
mixprob = 0.5,
simultaneous_covmat = TRUE,
fast = TRUE,
nu0 = 3,
quantsig = 0.9,
sparse = FALSE,
alpha_a_y = 0.5,
alpha_b_y = 1,
alpha_a_z = 0.5,
alpha_b_z = 1,
alpha_split_prior = TRUE){
if(!(cov_prior %in% c("VH","Omori","Mixture", "Ding"))){
stop("cov_prior must equal VH, Omori, Mixture, or Ding")
}
if((mixprob < 0)| (mixprob > 1) ){
stop("mixprob must be between zero and one.")
}
# if(is.vector(x.train) | is.factor(x.train)| is.data.frame(x.train)) x.train = as.matrix(x.train)
# if(is.vector(x.test) | is.factor(x.test)| is.data.frame(x.test)) x.test = as.matrix(x.test)
# if((!is.matrix(x.train))) stop("argument x.train must be a double matrix")
# if((!is.matrix(x.test)) ) stop("argument x.test must be a double matrix")
if(nrow(x.train) != length(y)) stop("number of rows in x.train must equal length of y.train")
if((ncol(x.test)!=ncol(x.train))) stop("input x.test must have the same number of columns as x.train")
if((ncol(w.test)!=ncol(w.train))) stop("input w.test must have the same number of columns as w.train")
if((nrow(w.test)!=nrow(x.test))) stop("input w.test must have the same number of rows as x.test")
if((nrow(w.train)!=nrow(x.train))) stop("input w.train must have the same number of rows as x.train")
#indexes of censored observations
if(is.na(censored_value)){
cens_inds <- which(is.na(y))
uncens_inds <- which(!(is.na(y)))
}else{
cens_inds <- which(y == censored_value)
uncens_inds <- which(y != censored_value)
}
# print("line 292")
if(length(cens_inds)==0) stop("No censored observations")
# print("line 296")
if(!all(y %in% c(0,1,NA))){
stop("All elements of y must be 0,1, or NA")
}
# print("line 302")
# normalize the outcome
# tempmean <- mean(y[uncens_inds])
# tempsd <- sd(y[uncens_inds])
# originaly <- y
# y <- (y-tempmean)/tempsd
#
# if(is.numeric(censored_value)){
# censored_value <- (censored_value- tempmean)/tempsd
# }
tempsd <- 1
tempmean <- 0
n <- length(y)
n0 <- length(cens_inds)
n1 <- length(uncens_inds)
uncens0_inds <- which(y==0)
uncens1_inds <- which(y==1)
n_zeros <- sum(y==0,na.rm = TRUE)
n_ones <- sum(y==1,na.rm = TRUE)
# offsety <- 0 # qnorm(n_ones/n1)
offsety <- qnorm(n_ones/n1)
ystar <- rep(offsety, length(y))
# print("line 331")
sub_uncens0_inds <- which(y[uncens_inds]==0)
sub_uncens1_inds <- which(y[uncens_inds]==1)
# print("line 339")
# print("line 344")
# z[cens_inds] <- qnorm(0.001) #rtruncnorm(n0, a= -Inf, b = 0, mean = offsety, sd = 1)
#
# z[uncens_inds] <- qnorm(0.999) #rtruncnorm(n1, a= 0, b = Inf, mean = offsety, sd = 1)
# print("uncens0_inds = ")
# print(uncens0_inds)
#
# print("uncens1_inds = ")
# print(uncens1_inds)
#
# print("n_zeros = ")
# print(n_zeros)
#
# print("n_ones = ")
# print(n_ones)
ystar[uncens0_inds] <- rtruncnorm(n_zeros, a= -Inf, b = 0, mean = offsety, sd = 1)
ystar[uncens1_inds] <- rtruncnorm(n_ones, a= 0, b = Inf, mean = offsety, sd = 1)
# print("line 349")
#create ystar vector
# ystar <- rep(mean(y[uncens_inds]), length(y))
# ystar[uncens_inds] <- y[uncens_inds]
ecdfs <- list()
for(i in 1:ncol(x.train)) {
ecdfs[[i]] <- ecdf(x.train[,i])
if(length(unique(x.train[,i])) == 1) ecdfs[[i]] <- identity
if(length(unique(x.train[,i])) == 2) ecdfs[[i]] <- make_01_norm(x.train[,i])
}
for(i in 1:ncol(x.train)) {
x.train[,i] <- ecdfs[[i]](x.train[,i])
x.test[,i] <- ecdfs[[i]](x.test[,i])
}
rm(ecdfs)
ecdfs <- list()
for(i in 1:ncol(w.train)) {
ecdfs[[i]] <- ecdf(w.train[,i])
if(length(unique(w.train[,i])) == 1) ecdfs[[i]] <- identity
if(length(unique(w.train[,i])) == 2) ecdfs[[i]] <- make_01_norm(w.train[,i])
}
for(i in 1:ncol(w.train)) {
w.train[,i] <- ecdfs[[i]](w.train[,i])
w.test[,i] <- ecdfs[[i]](w.test[,i])
}
rm(ecdfs)
#create z vector
# print("line 392")
ntest = nrow(x.test)
p_y <- ncol(x.train)
p_z <- ncol(w.train)
if(sparse){
s_y <- rep(1 / p_y, p_y) # probability vector to be used during the growing process for DART feature weighting
rho_y <- p_y # For DART
if(alpha_split_prior){
alpha_s_y <- p_y
}else{
alpha_s_y <- 1
}
alpha_scale_y <- p_y
s_z <- rep(1 / p_z, p_z) # probability vector to be used during the growing process for DART feature weighting
rho_z <- p_z # For DART
if(alpha_split_prior){
alpha_s_z <- p_z
}else{
alpha_s_z <- 1
}
alpha_scale_z <- p_z
}
offsetz <- 0 # qnorm(n1/n)
# offsetz <- qnorm(n1/n)
z <- rep(offsetz, length(y))
# z[cens_inds] <- qnorm(0.001) #rtruncnorm(n0, a= -Inf, b = 0, mean = offsetz, sd = 1)
#
# z[uncens_inds] <- qnorm(0.999) #rtruncnorm(n1, a= 0, b = Inf, mean = offsetz, sd = 1)
z[cens_inds] <- rtruncnorm(n0, a= -Inf, b = 0, mean = offsetz, sd = 1)
z[uncens_inds] <- rtruncnorm(n1, a= 0, b = Inf, mean = offsetz, sd = 1)
# z <- rnorm(n = length(y), mean = offsetz, sd =1)
# print("line 448")
# if(is.null(S0)){
#
# #use uncensored observations
# if(is.na(sigest)) {
# if(ncol(x.train) < n1) {
# df = data.frame(x = x.train[uncens_inds,],y = y[uncens_inds])
# lmf = lm(y~.,df)
# sigest = summary(lmf)$sigma
# } else {
# sigest = sd(y[uncens_inds])
# }
# }
#
# S0 <- 2*(sigest^2 - (1/(8*(G0^2))) - 4*(gamma0^2)*G0 )
#
# # S0 <- 2*(sigest^2)# - (1/(8*(G0^2))) - 4*(gamma0^2)*G0 )
#
# # S0 <- 0.002
#
# }
#use uncensored observations
if(is.na(sigest)) {
if(ncol(x.train) < n1) {
# df = data.frame(x = x.train[uncens_inds,],y = y[uncens_inds])
# lmf = lm(y~.,df)
# sigest = summary(lmf)$sigma
if(is.na(censored_value)){
dtemp <- 1*(!(is.na(y)))
}else{
dtemp <- 1*(y != censored_value)
}
df = data.frame(x = cbind(x.train,w.train), ystar = ystar, d = dtemp )
# print("x.train = ")
# print(x.train)
# print("colnames(df) = ")
# print(colnames(df))
# colnames(df)[1:ncol(x.train)] <- paste("x",1:ncol(x.train),sep = ".")
# colnames(df)[(ncol(x.train)+1):(ncol(x.train) + ncol(w.train))] <- paste("x",(ncol(x.train)+1):(ncol(x.train) + ncol(w.train)),sep = ".")
#
# seleq <- paste0("d ~ " , paste(paste("x",(ncol(x.train)+1):(ncol(x.train) + ncol(w.train)),sep = "."),collapse = " + "))
# outeq <- paste0("y ~ " , paste(paste("x",1:ncol(x.train),sep = "."),collapse = " + "))
seleq <- paste0("d ~ " , paste(colnames(df)[(ncol(x.train)+1):(ncol(x.train) + ncol(w.train))],
collapse = " + "))
outeq <- paste0("ystar ~ " , paste(colnames(df)[1:ncol(x.train)],
collapse = " + "))
heckit.ml <- heckit(selection = as.formula(seleq),
outcome = as.formula(outeq),
data = df,
method = "ml")
correst <- heckit.ml$estimate["rho"]
sigest <- heckit.ml$estimate["sigma"]
# correst <- heckit.2step$coefficients["rho"]
# sigest <- heckit.2step$coefficients["sigma"]
# print("heckit.2step$coefficients = ")
# print(heckit.2step$coefficients)
# print("heckit.2step$lm$coefficients = ")
# print(heckit.2step$lm$coefficients)
# print("correst = ")
# print(correst)
# print("correst = ")
# print(correst)
# print("correst = ")
# print(correst)
} else {
sigest = sd(ystar[uncens_inds])
correst <- 0
}
}
# if(is.null(nzero)){
#
# nzero <- 2*(sigest^2)
#
# }
#set initial sigma
#alternatively, draw this from the prior
Sigma_mat <- cbind(c(1,0),c(0,sigest^2))
#set initial gamma
gamma1 <- correst*sigest #0#cov(ystar,z)
#set initial phi
phi1 <- sigest^2 - gamma1^2
# print("phi1 = ")
# print(phi1)
# print("correst = ")
# print(correst)
# print("sigest = ")
# print(sigest)
# print("gamma1 = ")
# print(gamma1)
# if(sigest > G0){
# S0 <- (nzero - 2)*(sigest-G0)
# }else{
# sigquant <- 0.9
# qchi <- qchisq(1.0-sigquant,nzero/2)
# S0 <- 2*(sigest*sigest*qchi)/(nzero/2)
# }
# S0 <- (sigest^2)*(nzero-2)/(1+tau)
S0 <- (sigest^2)*(1 - correst^2)*(nzero-2)/(1+tau)
gamma0 <- correst*sigest
if(cov_prior == "Ding"){
gamma0 <- 0
# sigquant <- 0.9
qchi <- qchisq(1.0-quantsig,nu0-1)
cdivnu <- (sigest*sigest*qchi)/(nu0-1) #lambda parameter for sigma prior
cding <- cdivnu*(nu0-1)
rhoinit <- 0
siginit <- sigest
}
draw = list(
# Z.mat_train = array(NA, dim = c(n, n.iter)),
# Z.mat_test = array(NA, dim = c(ntest, n.iter)),
# Y.mat_train = array(NA, dim = c(n, n.iter)),
# Y.mat_test = array(NA, dim = c(ntest, n.iter)),
mu_y_train = array(NA, dim = c(n1, n.iter)),# array(NA, dim = c(n, n.iter)),
mu_y_test = array(NA, dim = c(ntest, n.iter)),
# mucens_y_train = array(NA, dim = c(n0, n.iter)),
muuncens_y_train = array(NA, dim = c(n1, n.iter)),
mu_z_train = array(NA, dim = c(n, n.iter)),
mu_z_test = array(NA, dim = c(ntest, n.iter)),
train.probcens = array(NA, dim = c(n1, n.iter)),#array(NA, dim = c(n, n.iter)),#,
test.probcens = array(NA, dim = c(ntest, n.iter)),#,
train.prob.y = array(NA, dim = c(n1, n.iter)),#array(NA, dim = c(n, n.iter)),#,
test.prob.y = array(NA, dim = c(ntest, n.iter)),#,
cond_exp_train = array(NA, dim = c(n1, n.iter)),#cond_exp_train = array(NA, dim = c(n, n.iter)),
cond_exp_test = array(NA, dim = c(ntest, n.iter)),
ystar_train = array(NA, dim = c(n, n.iter)),
ystar_test = array(NA, dim = c(ntest, n.iter)),
zstar_train = array(NA, dim = c(n, n.iter)),
zstar_test = array(NA, dim = c(ntest, n.iter)),
ycond_draws_train = list(),
ycond_draws_test = list(),
Sigma_draws = array(NA, dim = c(2, 2, n.iter))
)
if(is.numeric(censored_value)){
draw$uncond_exp_train <- array(NA, dim = c(n1, n.iter)) #array(NA, dim = c(n, n.iter))
draw$uncond_exp_test <- array(NA, dim = c(ntest, n.iter))
# draw$ydraws_train <- array(NA, dim = c(n, n.iter))
draw$ydraws_test <- array(NA, dim = c(ntest, n.iter))
}
if(sparse){
var_count_y <- rep(0, p_y)
var_count_z <- rep(0, p_z)
draw$alpha_s_y_store <- rep(NA, n.iter)
draw$alpha_s_z_store <- rep(NA, n.iter)
draw$var_count_y_store <- matrix(0, ncol = p_y, nrow = n.iter)
draw$var_count_z_store <- matrix(0, ncol = p_z, nrow = n.iter)
draw$s_prob_y_store <- matrix(0, ncol = p_y, nrow = n.iter)
draw$s_prob_z_store <- matrix(0, ncol = p_z, nrow = n.iter)
}
########## Initialize dbarts #####################
control_z <- dbartsControl(updateState = updateState, verbose = FALSE, keepTrainingFits = TRUE,
keepTrees = TRUE,
n.trees = n.trees_censoring,
n.burn = n.burn,
n.samples = n.samples,
n.thin = n.thin,
n.chains = n.chains,
n.threads = n.threads,
printEvery = printEvery,
printCutoffs = printCutoffs,
rngKind = rngKind,
rngNormalKind = rngNormalKind,
rngSeed = rngSeed)
control_y <- dbartsControl(updateState = updateState, verbose = FALSE, keepTrainingFits = TRUE,
keepTrees = TRUE,
n.trees = n.trees_outcome,
n.burn = n.burn,
n.samples = n.samples,
n.thin = n.thin,
n.chains = n.chains,
n.threads = n.threads,
printEvery = printEvery,
printCutoffs = printCutoffs,
rngKind = rngKind,
rngNormalKind = rngNormalKind,
rngSeed = rngSeed)
# print(colnames(Xmat.train))
# print("begin dbarts")
weightstemp <- rep(1,n)
weightstemp[uncens_inds] <- (gamma1^2 + phi1)/phi1
# print("weightstemp = ")
# print(weightstemp)
#
# print("length(weightstemp) = ")
# print(length(weightstemp))
#
# print("length(uncens_inds) = ")
# print(length(uncens_inds))
# print("line 681")
if(nrow(x.test ) == 0){
xdf_y <- data.frame(y = ystar[uncens_inds], x = x.train[uncens_inds,])
sampler_y <- dbarts(y ~ .,
data = xdf_y,
#test = x.test,
control = control_y,
tree.prior = dbarts:::cgm(power = tree_power_y, base = tree_base_y, split.probs = rep(1 / p_y, p_y)),
node.prior = node.prior,
resid.prior = resid.prior,
proposal.probs = proposal.probs,
sigma = sigmadbarts
)
# print("line 697")
# print("Line 425")
xdf_z <- data.frame(y = z - offsetz, x = w.train)
sampler_z <- dbarts(y ~ .,
data = xdf_z,
#test = x.test,
weights = weightstemp,
control = control_z,
tree.prior = dbarts:::cgm(power = tree_power_z, base = tree_base_z, split.probs = rep(1 / p_z, p_z)),
node.prior = node.prior,
resid.prior = resid.prior,
proposal.probs = proposal.probs,
sigma = 1
)
}else{
xdf_y <- data.frame(y = ystar[uncens_inds], x = x.train[uncens_inds,])
xdf_y_test <- data.frame(x = x.test)
sampler_y <- dbarts(y ~ .,
data = xdf_y,
test = xdf_y_test,
control = control_y,
tree.prior = dbarts:::cgm(power = tree_power_y, base = tree_base_y, split.probs = rep(1 / p_y, p_y)),
node.prior = node.prior,
resid.prior = resid.prior,
proposal.probs = proposal.probs,
sigma = sigmadbarts
)
# print("Line 425")
xdf_z <- data.frame(y = z - offsetz, x = w.train)
xdf_z_test <- data.frame(x = w.test)
sampler_z <- dbarts(y ~ .,
data = xdf_z,
test = xdf_z_test,
# weights = weightstemp,
control = control_z,
tree.prior = dbarts:::cgm(power = tree_power_z, base = tree_base_z, split.probs = rep(1 / p_z, p_z)),
node.prior = node.prior,
resid.prior = resid.prior,
proposal.probs = proposal.probs,
sigma = 1#sigmadbarts
)
}
# print("Line 473")
preds.train_ystar <- matrix(NA, n, 1)
preds.train_z <- matrix(NA, n, 1)
preds.test_ystar <- matrix(NA, ntest, 1)
preds.test_z <- matrix(NA, ntest, 1)
#initialize sum-of-tree sampler
z_resids <- z - offsetz #z_epsilon
z_resids[uncens_inds] <- z[uncens_inds] - offsetz - (ystar[uncens_inds] - 0)*gamma1/(phi1 + gamma1^2)
sampler_z$setResponse(y = z_resids)
sampler_z$setSigma(sigma = 1)
sampler_z$setWeights(weights = weightstemp)
# sampler_z$model@node.scale <- 3
if(sparse){
tempmodel <- sampler_z$model
tempmodel@tree.prior@splitProbabilities <- s_z
sampler_z$setModel(newModel = tempmodel)
# print("check z probs")
# print("sampler_z@tree.prior@splitProbabilities = ")
# print(sampler_z@tree.prior@splitProbabilities)
}
# print("Line 509")
# sampler_z$sampleTreesFromPrior()
# priormean_z <- sampler_z$predict(xdf_z)[1,]
# sampler_z$sampleNodeParametersFromPrior()
samplestemp_z <- sampler_z$run()
# mutemp_z <- rep(0,n) # samplestemp_z$train[,1]
# mutemp_test_z <- rep(0,ntest) #samplestemp_z$test[,1]
mutemp_z <- samplestemp_z$train[,1]
mutemp_test_z <- samplestemp_z$test[,1]
# mutemp_test_z <- sampler_z$predict(xdf_z_test)[,1]#samplestemp_z$test[,1]
if(sparse){
tempcounts <- fcount(sampler_z$getTrees()$var)
# print("line 766. tempcounts = ")
# print(tempcounts)
tempcounts <- tempcounts[tempcounts$x != -1, ]
# print("line 769. tempcounts = ")
# print(tempcounts)
var_count_z <- rep(0, p_z)
var_count_z[tempcounts$x] <- tempcounts$N
# print("line 773. var_count_z = ")
# print(var_count_z)
}
# print("length(mutemp_test_z) = ")
# print(length(mutemp_test_z))
#
# print("mutemp_test_z[1000:1010] = ")
# print(mutemp_test_z[1000:1010])
#
# print("mutemp_test_z[1:10] = ")
# print(mutemp_test_z[1:10])
#
# print("nrow(xdf_z_test) = ")
# print(nrow(xdf_z_test))
y_resids <- ystar[uncens_inds] - gamma1*(z[uncens_inds] - offsetz - mutemp_z[uncens_inds])
sampler_y$setResponse(y = y_resids)
sampler_y$setSigma(sigma = sqrt(phi1) )
# sampler_y$setSigma(sigma = sigest)
# sampler_y$model@node.scale <- 3
if(sparse){
tempmodel <- sampler_y$model
tempmodel@tree.prior@splitProbabilities <- s_y
sampler_y$setModel(newModel = tempmodel)
# print("check y probs")
# print("sampler_y@tree.prior@splitProbabilities = ")
# print(sampler_y@tree.prior@splitProbabilities)
}
# sampler_y$sampleTreesFromPrior()
# priormean_y <- sampler_y$predict(xdf_y)[1,]
# sampler_y$sampleNodeParametersFromPrior()
samplestemp_y <- sampler_y$run()
# mutemp_y <- rep(mean(y),n) #samplestemp_y$train[,1]
# mutemp_test_y <- rep(mean(y),ntest) # samplestemp_y$test[,1]
mutemp_y <- samplestemp_y$train[,1]
mutemp_test_y <- samplestemp_y$test[,1]
if(sparse){
tempcounts <- fcount(sampler_y$getTrees()$var)
# print("line 815. tempcounts = ")
# print(tempcounts)
tempcounts <- tempcounts[tempcounts$x != -1, ]
# print("line 824. tempcounts = ")
# print(tempcounts)
var_count_y <- rep(0, p_y)
var_count_y[tempcounts$x] <- tempcounts$N
# print("line 824. var_count_y = ")
# print(var_count_y)
}
# print("length(mutemp_test_y) = ")
# print(length(mutemp_test_y))
# if(sigest != samplestemp_y$sigma){
# print("sigest = ")
# print(sigest)
# print("dbarts sigma estimate =")
# print(samplestemp_y$sigma)
#
# df = data.frame(x.train,y)
# lmf = lm(y~.,df)
# sigest2 = summary(lmf)$sigma
#
# print("sigest2 = ")
# print(sigest2)
#
# # stop("sigest != samplestemp_y$sigma")
#
# }
# sigest <- samplestemp_y$sigma
# S0 <- 2*(sigest^2 - (1/(8*(G0^2))) - 4*(gamma0^2)*G0 )
#COMMENTING OUT INITIALIZATION FOR FIRST ITERATION
#MIGHT NEED TO EDIT THIS
# sampler$setResponse(y = z)
# sampler$setSigma(sigma = 1)
#sampler$setPredictor(x= Xmat.train$x, column = 1, forceUpdate = TRUE)
#mu = as.vector( alpha + X.mat %*% beta )
# sampler$sampleTreesFromPrior()
# samplestemp <- sampler$run()
#mutemp <- samplestemp$train[,1]
#suppose there are a number of samples
# print("sigma = ")
# sigma <- samplestemp$sigma
#
# mu <- samplestemp$train[,1]
# mutest <- samplestemp$test[,1]
#
# ystar <- rnorm(n,mean = mu, sd = sigma)
# ystartest <- rnorm(ntest,mean = mutest, sd = sigma)
#
# ystartestcens <-rtruncnorm(ntest, a = below_cens, b = above_cens, mean = mutest, sd = sigma)
#
# probcensbelow <- pnorm(below_cens, mean = mutest, sd = sigma)
# probcensabove <- 1 - pnorm(above_cens, mean = mutest, sd = sigma)
#save the first round of values
# if(n.burnin == 0){
# draw$Z.mat[,1] = z
# draw$Z.matcens[,1] = z[cens_inds]
# # draw$Z.matuncens[,1] = z[uncens_inds]
# draw$Z.matcensbelow[,1] = z[censbelow_inds]
# draw$Z.matcensabove[,1] = z[censabove_inds]
# draw$mu[,1] = mu
# draw$mucens[,1] = mu[cens_inds]
# draw$muuncens[,1] = mu[uncens_inds]
# draw$mucensbelow[,1] = mu[censbelow_inds]
# draw$mucensabove[,1] = mu[censabove_inds]
# draw$ystar[,1] = ystar
# draw$ystarcens[,1] = ystar[cens_inds]
# draw$ystaruncens[,1] = ystar[uncens_inds]
# draw$ystarcensbelow[,1] = ystar[censbelow_inds]
# draw$ystarcensabove[,1] = ystar[censabove_inds]
# draw$test.mu[,1] = mutest
# draw$test.y_nocensoring[,1] = ystartest
# draw$test.y_withcensoring[,1] = ystartestcens
# draw$test.probcensbelow[,1] = probcensbelow
# draw$test.probcensabove[,1] = probcensabove
# draw$sigma[1] <- sigma
# }
# print("line 964")
######### Begin Gibbs sampler ######################################################
# pb <- progress_bar$new(total = n.iter+n.burnin)
# pb <- progress_bar$new(
# format = " [:bar] :percent eta: :eta",
# total = n.iter+n.burnin, clear = FALSE, width= 60)
#loop through the Gibbs sampler iterations
for(iter in 1:(n.iter+n.burnin)){
# if(eq_by_eq){
# var_zdraw <- 1
# # sig_ydraw <- phi1
#
# }else{
# var_zdraw <- phi1/(gamma1^2+phi1)
# # sig_ydraw <- phi1
#
# }
# print("line 988")
####### samples of ystar conditional on everything else ######################
ystar[uncens0_inds] <- rtruncnorm(n_zeros, a= -Inf, b = 0,
mean = offsety + mutemp_y[sub_uncens0_inds] + gamma1*(z[uncens0_inds] - offsetz - mutemp_z[uncens0_inds]),
sd = sqrt(phi1) )
ystar[uncens1_inds] <- rtruncnorm(n_ones, a= 0, b = Inf,
mean = offsety + mutemp_y[sub_uncens1_inds] + gamma1*(z[uncens1_inds] - offsetz - mutemp_z[uncens1_inds]),
sd = sqrt(phi1) )
#//////////////////////////////////////////////////////////////////
temp_sd_z <- sqrt( phi1/(phi1+gamma1^2) )
######### #draw the latent outcome z ##########################
# z[cens_inds] <- rtruncnorm(n0, a= below_cens, b = above_cens, mean = mu[cens_inds], sd = sigma)
if(length(cens_inds)>0){
# temp_sd_y <- sqrt(phi1 + gamma1^2)
# print("mutemp_y[cens_inds] = ")
# print(mutemp_y[cens_inds])
# print("temp_sd_y = ")
# print(temp_sd_y)
#
# print("cens_inds = ")
# print(cens_inds)
# ystar[cens_inds] <- rnorm(n0, mean = mutemp_y[cens_inds], sd = temp_sd_y)
# temp_zmean_cens <- offsetz + mutemp_z[cens_inds] + (ystar[cens_inds] - mutemp_y[cens_inds])*gamma1/(phi1 + gamma1^2)
temp_zmean_cens <- offsetz + mutemp_z[cens_inds] #+ (ystar[cens_inds] - mutemp_y[cens_inds])*gamma1/(phi1 + gamma1^2)
z[cens_inds] <- rtruncnorm(n0, a= -Inf, b = 0, mean = temp_zmean_cens, sd = 1)
}
# temp_zmean_uncens <- offsetz + mutemp_z[uncens_inds] + (ystar[uncens_inds] - mutemp_y[uncens_inds])*gamma1/(phi1 + gamma1^2)
temp_zmean_uncens <- offsetz + mutemp_z[uncens_inds] + (ystar[uncens_inds] - mutemp_y)*gamma1/(phi1 + gamma1^2)
z[uncens_inds] <- rtruncnorm(n1, a= 0, b = Inf, mean = temp_zmean_uncens,
sd = temp_sd_z)
# z_epsilon <- z - offsetz - mutemp_z
# y_epsilon <- ystar - mutemp_y
z_epsilon <- z - offsetz - mutemp_z
y_epsilon <- rep(0, n)
y_epsilon[uncens_inds] <- ystar[uncens_inds] - mutemp_y
# print("temp_sd_z = ")
# print(temp_sd_z)
#
# print("mutemp_z = ")
# print(mutemp_z)
#
# print("temp_zmean_cens = ")
# print(temp_zmean_cens)
#
# print("z = ")
# print(z)
####### draw sums of trees for z #######################################################
#create residuals for z and set variance
# if(eq_by_eq){
# z_resids <- z - offsetz #z_epsilon
# sd_zdraw <- 1
# }else{
# #not sure about this step for tobit2b
# z_resids <- z - offsetz - y_epsilon*(gamma1/(phi1+gamma1^2))
# sd_zdraw <- sqrt(phi1 / (phi1 + gamma1^2) )
# }
z_resids <- z - offsetz #z_epsilon
z_resids[uncens_inds] <- z[uncens_inds] - offsetz - (ystar[uncens_inds] - mutemp_y)*gamma1/(phi1 + gamma1^2)
#set the response for draws of z trees
sampler_z$setResponse(y = z_resids)
#set the standard deivation
sampler_z$setSigma(sigma = 1)
weightstemp[uncens_inds] <- (gamma1^2 + phi1)/phi1
# print("weightstemp = ")
# print(weightstemp)
# print("Line 737")
# print("weightstemp = ")
# print(weightstemp)
#
# print("gamma1 = ")
# print(gamma1)
#
# print("phi1 = ")
# print(phi1)
sampler_z$setWeights(weights = weightstemp)
if(sparse){
tempmodel <- sampler_z$model
tempmodel@tree.prior@splitProbabilities <- s_z
sampler_z$setModel(newModel = tempmodel)
# print("check z probs")
# print("sampler_z@tree.prior@splitProbabilities = ")
# print(sampler_z@tree.prior@splitProbabilities)
}
# print("Line 741")
samplestemp_z <- sampler_z$run()
mutemp_z <- samplestemp_z$train[,1]
mutemp_test_z <- samplestemp_z$test[,1]
# mutemp_test_z <- sampler_z$test[,1]#samplestemp_z$test[,1]
# mutemp_test_z <- sampler_z$predict(xdf_z_test)[,1]#samplestemp_z$test[,1]
if(sparse){
tempcounts <- fcount(sampler_z$getTrees()$var)
# print("line 1041. tempcounts = ")
# print(tempcounts)
tempcounts <- tempcounts[tempcounts$x != -1, ]
var_count_z <- rep(0, p_z)
var_count_z[tempcounts$x] <- tempcounts$N
}
# print("length(mutemp_test_z) = ")
# print(length(mutemp_test_z))
#
# print("nrow(xdf_z_test) = ")
# print(nrow(xdf_z_test))
#update z_epsilon
z_epsilon <- z - offsetz - mutemp_z
####### draw sums of trees for y #######################################################
#create residuals for z and set variance
# print("y_epsilon = ")
#
# print(y_epsilon)
#
#
# print("z_epsilon = ")
#
# print(z_epsilon)
#
# print("gamma1 = ")
#
# print(gamma1)
# if(eq_by_eq){
# y_resids <- ystar[uncens_inds] - gamma1*z_epsilon[uncens_inds]
# sd_ydraw <- sqrt(phi1)
# }else{
# y_resids <- ystar[uncens_inds] - gamma1*z_epsilon[uncens_inds]
# sd_ydraw <- sqrt(phi1)
# }
y_resids <- ystar[uncens_inds] - gamma1*(z[uncens_inds] - offsetz - mutemp_z[uncens_inds])
sd_ydraw <- sqrt(phi1)
# print("y_resids = ")
#
# print(y_resids)
#set the response for draws of z trees
sampler_y$setResponse(y = y_resids)
#set the standard deviation
sampler_y$setSigma(sigma = sd_ydraw)
if(sparse){
tempmodel <- sampler_y$model
tempmodel@tree.prior@splitProbabilities <- s_y
sampler_y$setModel(newModel = tempmodel)
# print("check y probs")
# print("sampler_y@tree.prior@splitProbabilities = ")
# print(sampler_y@tree.prior@splitProbabilities)
}
samplestemp_y <- sampler_y$run()
mutemp_y <- samplestemp_y$train[,1]
mutemp_test_y <- samplestemp_y$test[,1]
if(sparse){
tempcounts <- fcount(sampler_y$getTrees()$var)
# print("line 1107. tempcounts = ")
# print(tempcounts)
tempcounts <- tempcounts[tempcounts$x != -1, ]
var_count_y <- rep(0, p_y)
var_count_y[tempcounts$x] <- tempcounts$N
}
#update z_epsilon
y_epsilon[uncens_inds] <- ystar[uncens_inds] - mutemp_y
############# Covariance matrix samples ##########################
if(cov_prior == "Ding"){
rho1 <- gamma1/sqrt(Sigma_mat[2,2])
sigz2 <- 1/rgamma(n = 1,
shape = nu0/2,
rate = cding/(2*(1- (rho1^2))))
z_epsilon2 <- sqrt(sigz2)*(z - offsetz - mutemp_z)
zsquares <- crossprod(z_epsilon2[uncens_inds], z_epsilon2[uncens_inds])[1]
ysquares <- crossprod(y_epsilon[uncens_inds], y_epsilon[uncens_inds])[1]
zycross <- crossprod(z_epsilon2[uncens_inds], y_epsilon[uncens_inds])[1]
Stemp <- cbind(c(ysquares,zycross),
c(zycross, zsquares))
tempsigma <- rinvwishart(nu = n1 + nu0,
S = Stemp+cding*diag(2))
transmat <- cbind(c(1,0),c(0,1/sqrt(tempsigma[2,2])))
tempomega <- (transmat %*% tempsigma) %*% transmat
temprho <- tempomega[1,2]/(sqrt(tempomega[1,1]))
gamma1 <- tempomega[1,2]
phi1 <- tempomega[1,1] - (gamma1^2)
# if(tempomega[2,2] != 1){
# print("tempomega[2,2] = ")
# print(tempomega[2,2])
# }
#
# if(phi1 < 0){
# print("phi1 = ")
# print(phi1)
# }
}else{
########### Simultaneous phi and gamma draw #####################
if(simultaneous_covmat == TRUE){
if(cov_prior == "VH"){
h_num <- (gamma0/tau) + crossprod(z_epsilon[uncens_inds], y_epsilon[uncens_inds])[1]
a_temp <- (1/tau) + crossprod(z_epsilon[uncens_inds], z_epsilon[uncens_inds])[1]
h_temp <- h_num/a_temp
k_temp <- ((gamma0^2)/tau)+S0 +
crossprod(y_epsilon[uncens_inds], y_epsilon[uncens_inds])[1] -
((h_num^2)/(a_temp))
phi1 <- 1/rgamma(n = 1,
shape = (nzero + n1 )/2,
rate = k_temp/2)
gamma1 <- rnorm(n = 1, mean = h_temp, sd = sqrt(phi1/a_temp))
}else{
stop("If simultaneous_covmat == TRUE, then must use Van Hasselt Covariance prior. Set cov_prior to VH.")
}
}else{
######### set parameters for gamma draw ######################################################
# if(cov_prior == TRUE){
# G0 <- tau*phi1
# }
if(cov_prior == "VH"){
G0draw <- tau*phi1
}else{
if(cov_prior == "Omori"){
G0draw <- G0
}else{
mixind <- rbinom(n = 1,size = 1,prob = mixprob)
if(mixind == 1){
G0draw <- tau*phi1
}else{
G0draw <- G0
}
}
}
# G1inv <- (1/G0) + (1/phi1)*crossprod(z_epsilon)
G1inv <- (1/G0draw) + (1/phi1)*crossprod(z_epsilon[uncens_inds])[1]
# G1inv <- (1/tau) + (1/phi1)*crossprod(z_epsilon[uncens_inds])
G1 <- (1/G1inv)#[1,1]
# gamma_one <- (G1*( (1/G0draw)*gamma0 + (1/phi1)*crossprod(z_epsilon , y_epsilon ) ))[1,1]
gamma_one <- (G1*( (1/G0draw)*gamma0 + (1/phi1)*crossprod(z_epsilon[uncens_inds] , y_epsilon[uncens_inds] )[1] ))
# gamma_one <- (G1*( (1/tau)*gamma0 + (1/phi1)*crossprod(z_epsilon[uncens_inds] , y_epsilon[uncens_inds] ) ))[1,1]
# print("phi1 = ")
# print(phi1)
# print("G0draw = ")
# print(G0draw)
#
# print("(G1*( (1/G0draw)*gamma0 + (1/phi1)*crossprod(z_epsilon[uncens_inds] , y_epsilon[uncens_inds] ) )) = ")
# print((G1*( (1/G0draw)*gamma0 + (1/phi1)*crossprod(z_epsilon[uncens_inds] , y_epsilon[uncens_inds] ) )))
#
# print("gamma_one = ")
# print(gamma_one)
#
# print("crossprod(z_epsilon , y_epsilon ) = ")
# print(crossprod(z_epsilon , y_epsilon ))
#
# print("crossprod(z_epsilon ) = ")
# print(crossprod(z_epsilon ))
#
# print("crossprod(z_epsilon[uncens_inds])[1] = ")
# print(crossprod(z_epsilon[uncens_inds])[1] )
#
# print("G1 = ")
# print(G1)
gamma1 <- rnorm(n = 1, mean = gamma_one, sd = sqrt(G1) )
# print("gamma1 = ")
# print(gamma1)
######### set parameters for phi draw ######################################################
n_one <- nzero + n1 + 1
# print("S0 = ")
# print(S0)
# print("(gamma1^2)*crossprod(z_epsilon) = ")
# print((gamma1^2)*crossprod(z_epsilon))
#
# print("2*gamma1*crossprod(z_epsilon , y_epsilon ) = ")
# print(2*gamma1*crossprod(z_epsilon , y_epsilon ))
#
# print("crossprod(y_epsilon) = ")
# print(crossprod(y_epsilon))
# S1 <- S0 + (gamma1^2)*crossprod(z_epsilon) - 2*gamma1*crossprod(z_epsilon , y_epsilon ) + crossprod(y_epsilon)
S1 <- 0 #S0 + (gamma1^2)/G0 + gamma1*crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] ) + crossprod(y_epsilon)
if(cov_prior == "VH"){
# S1 <- S0 + (gamma1^2)/tau + gamma1*crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] ) + crossprod(y_epsilon)
S1 <- S0 + (gamma1^2)/tau +
crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] )[1] # + crossprod(y_epsilon)
}else{
if(cov_prior == "Omori"){
S1 <- S0 + #+ (gamma1^2)/G0 +
crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] )[1] #+crossprod(y_epsilon)[1]
}else{
mixind <- rbinom(n = 1,size = 1,prob = mixprob)
if(mixind == 1){
# S1 <- S0 + (gamma1^2)/tau + gamma1*crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] ) + crossprod(y_epsilon)
S1 <- S0 + (gamma1^2)/tau +
crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] )[1] # + crossprod(y_epsilon)
}else{
S1 <- S0 + #+ (gamma1^2)/G0 +
crossprod( y_epsilon[uncens_inds] - gamma1*z_epsilon[uncens_inds] )[1] #+crossprod(y_epsilon)[1]
}
}
}
# print("S1 = ")
# print(S1)
# print("n_one = ")
# print(n_one)
# print("Line 883 phi1 = ")
# print(phi1)
# draw from inverse gamma
phi1 <- 1/rgamma(n = 1, shape = n_one/2, rate = S1/2)
# print("Line 890 phi1 = ")
# print(phi1)
#
# print("Line 890 n_one = ")
# print(n_one)
#
# print("Line 890 S1 = ")
# print(S1)
# print("n1 = ")
# print(n1)
} # end of else statement, for simultaneous_covmat = FALSE
}
######### update Sigma matrix #####################################################
Sigma_mat <- cbind(c(1,gamma1),c(gamma1,phi1+gamma1^2))
Sigma_orig_scale <- cbind(c(1,tempsd*gamma1),c(tempsd*gamma1, (tempsd^2)*(phi1+gamma1^2)) )
###### Accelerated sampler ###############################
# if(accelerate == TRUE){
#
# meanmu_z <- (min(z - offsetz) +max(z- offsetz))/(2*n.trees_censoring)
#
# # the variance should be zero?
# sigmu_z <- (max(z- offsetz) - min(z- offsetz))/(2*2*sqrt(n.trees_censoring))
# #set prior parameter values
#
#
# #if prior mean for mu parameters is zero (does this make sense? require an offset for y?)
#
# nu1 <- sum(sampler_z$getTrees@.Data()$var ==-1) - nzero + 1
#
#
# asquared <- (1/phi1)*(S0 + crossprod(y_epsilon[uncens_inds]))
#
# znodestemp <- sampler_z$getTrees@.Data()$value[sampler_z$getTrees@.Data()$var!=-1]
#
# bsquared <- (1 + (gamma1^2)/phi1)*crossprod(z_epsilon) +
# (1/sigmu_z)*crossprod(znodestemp) + (gamma1^2)*(1/G0)
#
#
# if(sqrt(asquared*bsquared) > 150^2){
# print("GIG sample will be slow.")
# }
#
# #candidate g parameer value
# gprime <- rgig(n = 1, lambda = nu1/2, chi = asquared, psi = bsquared )
#
#
#
#
# probaccept <- min(1, exp((gprime-1)* ((1/sigmu_z)*sum(znodestemp)*meanmu_z +
# gamma1*gamma0/G0 ) ) )
#
# g_accepted <- 1
#
# #check if accept
# accept_bin <- rbinom(n = 1,size = 1, prob = probaccept)
#
# if(is.na(accept_bin)){
#
# print("accept_bin is na. probaccept =")
# print(probaccept)
#
# print("gprime = ")
# print(gprime)
#
# print("sigmu_z = ")
# print(sigmu_z)
#
# print("sum(znodestemp) = ")
# print(sum(znodestemp))
#
# print("meanmu_z = ")
# print(meanmu_z)
#
# }
#
# if(accept_bin == 1){
# g_accepted <- gprime
#
# phi1 <- (gprime^2)*phi1
#
# gamma1 <- gprime*gamma1
#
# mutemp_z <- gprime*mutemp_z
#
# z <- gprime*z
#
# }
#
# }
########### splitting probability draws #############################
if (sparse & (iter > floor(n.burnin * 0.5))) {
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("var_count_z = ")
# print(var_count_z)
# print("p_z = ")
# print(p_z)
# print("alpha_s_z = ")
# print(alpha_s_z)
# }
s_update_z <- update_s(var_count_z, p_z, alpha_s_z)
s_z <- s_update_z[[1]]
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("s_update_z = ")
# print(s_update_z)
# }
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("var_count_y = ")
# print(var_count_y)
# print("p_y = ")
# print(p_y)
# print("alpha_s_y = ")
# print(alpha_s_y)
# }
s_update_y <- update_s(var_count_y, p_y, alpha_s_y)
s_y <- s_update_y[[1]]
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("s_update_y = ")
# print(s_update_y)
# }
if(alpha_split_prior){
alpha_s_z <- update_alpha(s_z, alpha_scale_z, alpha_a_z, alpha_b_z, p_z, s_update_z[[2]])
alpha_s_y <- update_alpha(s_y, alpha_scale_y, alpha_a_y, alpha_b_y, p_y, s_update_y[[2]])
}
# if( iter == floor(n.burnin * 0.5) +1 ){
# print("alpha_s_z = ")
# print(alpha_s_z)
# print("alpha_s_y = ")
# print(alpha_s_y)
# }
}
###### Store results ###############################
# print("iter = ")
# print(iter)
if(iter > n.burnin){
iter_min_burnin <- iter-n.burnin
#NOTE y and z training sample values saved here
#do not correspond to the the same means and errors as
#the test values and expectations saved here.
#However, they are the values to which the trees in this round were fitted.
#draw z and y for test observations
zytest <- matrix(NA, nrow = ntest, ncol = 2)
if(fast == TRUE){
zytest <- Rfast::rmvnorm(n = ntest,
mu = c(0, 0),
sigma = Sigma_mat)
}else{
zytest <- mvrnorm(n = ntest,
mu = c(0, 0),
Sigma = Sigma_mat)
}
# print("length(mutemp_test_z) = ")
# print(length(mutemp_test_z))
#
# print("offsetz = ")
# print(offsetz)
#
# print("length(zytest[,1]) = ")
# print(length(zytest[,1]))
zytest[,1] <- zytest[,1] + offsetz + mutemp_test_z
zytest[,2] <- zytest[,2] + mutemp_test_y
# for(i in 1:ntest){
# zytest[i,] <- mvrnorm(n = 1,
# mu = c(offsetz + mutemp_test_z[i], mutemp_test_y[i]),
# Sigma = Sigma_mat)
# }
if(fast == TRUE){
probcens_train <- fastpnorm(- mutemp_z[uncens_inds] - offsetz )
probcens_test <- fastpnorm(- mutemp_test_z - offsetz)
prob_y_train <- 1 - fastpnorm(- mutemp_y - offsety )
prob_y_test <- 1 - fastpnorm(- mutemp_test_y - offsety )
}else{
probcens_train <- pnorm(- mutemp_z[uncens_inds] - offsetz )
probcens_test <- pnorm(- mutemp_test_z - offsetz)
prob_y_train <- 1 - pnorm(- mutemp_y - offsety )
prob_y_test <- 1 - pnorm(- mutemp_test_y - offsety )
}
#calculate conditional expectation
# condexptrain <- mutemp_y + gamma1*(dnorm(- mutemp_z - offsetz ))/(1-probcens_train)
# condexptrain <- mutemp_y + gamma1*(dnorm(- mutemp_z[uncens_inds] - offsetz ))/(1-probcens_train)
# condexptest <- mutemp_test_y + gamma1*(dnorm(- mutemp_test_z - offsetz ))/(1-probcens_test)
temp_ztrain <- mutemp_z[uncens_inds] + offsetz
temp_ztest <- mutemp_test_z + offsetz
#
# if(fast == TRUE){
# IMR_train <- exp( dnorm(temp_ztrain,log=T) - log(fastpnorm(temp_ztrain) ))
# IMR_test <- exp( dnorm(temp_ztest,log=T) - log(fastpnorm(temp_ztest) ))
#
# # IMR_train <- fastnormdens(temp_ztrain)/fastpnorm(temp_ztrain)
# # IMR_test <- fastnormdens(temp_ztest)/fastpnorm(temp_ztest)
# }else{
IMR_train <- exp( dnorm(temp_ztrain,log=T) - pnorm(temp_ztrain,log.p = T) )
IMR_test <- exp( dnorm(temp_ztest,log=T) - pnorm(temp_ztest,log.p = T) )
# }
condexptrain <- mutemp_y + gamma1*IMR_train
condexptest <- mutemp_test_y + gamma1*IMR_test
# draw$Z.mat_train[,iter_min_burnin] <- z
# draw$Z.mat_test[,iter_min_burnin] <- zytest[,1]
# draw$Y.mat_train = array(NA, dim = c(n, n.iter)),
# draw$Y.mat_test = array(NA, dim = c(ntest, n.iter)),
draw$mu_y_train[, iter_min_burnin] <- mutemp_y*tempsd+tempmean
draw$mu_y_test[, iter_min_burnin] <- mutemp_test_y*tempsd+tempmean
# draw$mucens_y_train[, iter_min_burnin] <- mutemp_y[cens_inds]
# draw$muuncens_y_train[, iter_min_burnin] <- mutemp_y[uncens_inds]
draw$muuncens_y_train[, iter_min_burnin] <- mutemp_y*tempsd+tempmean
draw$mu_z_train[, iter_min_burnin] <- mutemp_z
draw$mu_z_test[, iter_min_burnin] <- mutemp_test_z
draw$train.probcens[, iter_min_burnin] <- probcens_train
draw$test.probcens[, iter_min_burnin] <- probcens_test
draw$train.prob.y[, iter_min_burnin] <- prob_y_train
draw$test.prob.y[, iter_min_burnin] <- prob_y_test
draw$cond_exp_train[, iter_min_burnin] <- condexptrain*tempsd+tempmean
draw$cond_exp_test[, iter_min_burnin] <- condexptest*tempsd+tempmean
draw$ystar_train[, ] <- ystar*tempsd+tempmean
draw$ystar_test[, iter_min_burnin] <- zytest[,2]*tempsd+tempmean
draw$zstar_train[,iter_min_burnin] <- z
draw$zstar_test[,iter_min_burnin] <- zytest[,1]
draw$ycond_draws_train[[iter_min_burnin]] <- ystar[z >=0]*tempsd+tempmean
draw$ycond_draws_test[[iter_min_burnin]] <- zytest[,2][zytest[,1] >= 0]*tempsd+tempmean
draw$Sigma_draws[,, iter_min_burnin] <- Sigma_orig_scale#Sigma_mat
if(is.numeric(censored_value)){
# uncondexptrain <- censored_value*probcens_train + mutemp_y*(1- probcens_train ) + gamma1*dnorm(- mutemp_z - offsetz )
uncondexptrain <- censored_value*probcens_train + mutemp_y*(1- probcens_train ) + gamma1*dnorm(- mutemp_z[uncens_inds] - offsetz )
uncondexptest <- censored_value*probcens_test + mutemp_test_y*(1- probcens_test ) + gamma1*dnorm(- mutemp_test_z - offsetz)
draw$uncond_exp_train[, iter_min_burnin] <- uncondexptrain*tempsd+tempmean
draw$uncond_exp_test[, iter_min_burnin] <- uncondexptest*tempsd+tempmean
# draw$ydraws_train[, iter_min_burnin] <- ifelse(z < 0, censored_value, ystar )
draw$ydraws_test[, iter_min_burnin] <- ifelse(zytest[,1] < 0, censored_value, zytest[,2] )*tempsd+tempmean
}
if(sparse){
draw$alpha_s_y_store[iter_min_burnin] <- alpha_s_y
draw$alpha_s_z_store[iter_min_burnin] <- alpha_s_z
draw$var_count_y_store[iter_min_burnin,] <- var_count_y
draw$var_count_z_store[iter_min_burnin,] <- var_count_z
draw$s_prob_y_store[iter_min_burnin,] <- s_y
draw$s_prob_z_store[iter_min_burnin,] <- s_z
}
} # end if iter > burnin
# pb$tick()
if(iter %% print.opt == 0){
print(paste("Gibbs Iteration", iter))
# print(c(sigma2.alpha, sigma2.beta))
}
}#end iterations of Giibs sampler
return(draw)
}
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