,
Thomas Klausch [aut],
Mark A. van de Wiel [aut],
Vincent Dorie [ctb] (Author of 'dbarts' 'R' package and auxiliary
function getDepth),
Hanarth Fonds [fnd]| Title: | Co-Data Learning for Bayesian Additive Regression Trees |
|---|---|
| Description: | Estimate prior variable weights for Bayesian Additive Regression Trees (BART). These weights correspond to the probabilities of the variables being selected in the splitting rules of the sum-of-trees. Weights are estimated using empirical Bayes and external information on the explanatory variables (co-data). BART models are fitted using the 'dbarts' 'R' package. See Goedhart and others (2023) <doi:10.48550/arXiv.2311.09997> for details. |
| Authors: | Jeroen M. Goedhart [aut, cre, cph]
,
Thomas Klausch [aut],
Mark A. van de Wiel [aut],
Vincent Dorie [ctb] (Author of 'dbarts' 'R' package and auxiliary
function getDepth),
Hanarth Fonds [fnd] |
| Maintainer: | Jeroen M. Goedhart <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.1 |
| Built: | 2025-02-15 05:25:40 UTC |
| Source: | https://github.com/jeroengoedhart/ebcobart |
Contains training data and test data to predict 2 year progression free survival (yes/no) #' based on four types of variables: copy number variation, point mutations, translocations, #' and clinical. For the variables, auxiliary information (co-data) is available which may be used to give more weight to certain variables in the prediction model. This data set is used in the manuscript "Co-data Learning for Bayesian Additive Regression Trees"
data(dat)data(dat)
A list object with five data sets:
Dataframe with 101 rows (samples) and 140 columns (variables. Explanatory variables used for fitting BART. Variable names are anonymized.
Numeric of length 101. Binary training response (0: 2 year progression free survival, 1: disease came back within 2 years)
Dataframe with 83 rows (samples) and 140 columns (variables). Explanatory variables used for fitting BART. Variable names are anonymized.
Numeric of length 83 Binary training response (0: 2 year progression free survival, 1: disease came back within 2 years)
Dataframe with 140 rows and 2 columns. Auxiliary information on the 140 variables. Contains a grouping structure indicating which type a variable is (copy number variation (CNV), mutation, translocation, or clinical), and p values (logit scale) for each variable obtained from a previous study
Jeroen M. Goedhart, [email protected]
Jurriaan Janssen
Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen, Mark A. van de Wiel. "Co-data Learning for Bayesian Additive Regression Trees." arXiv preprint arXiv:2311.09997. 2023 Nov 16.
The R package dbarts uses dummy encoding for factor variables so the co-data matrix should contain co-data information for each dummy. If co-data #' is only available for the factor as a whole (e.g. factor belongs to a group), #' use this function to set-up the co-data in the right-format #' for the EBcoBART function.
Dat_EBcoBART(X, CoData)Dat_EBcoBART(X, CoData)
X |
Explanatory variables. Should be a data.frame. The function is only useful when X contains factor variables. |
CoData |
The co-data model matrix with co-data information on explanatory variables in X. Should be a matrix, so not a data.frame. If grouping information is present, please encode this yourself using dummies with dummies representing which group a explanatory variable belongs to. The number of rows of the co-data matrix should equal the number of columns of X. |
A list object with X: the explanatory variables with factors encoded as dummies and CoData: the co-data matrix with now co-data for all dummies.
Jeroen M. Goedhart, [email protected]
p <- 15 n <- 30 X <- matrix(runif(n*p),nrow = n, ncol = p) #all continuous variables Fact <- factor(sample(1:3,n,replace = TRUE)) # factor variables X <- cbind.data.frame(X,Fact) G <- 4 #number of groups for co-data CoDat <- rep(1:G, rep(ncol(X)/G,G)) # first 4 covariates in group 1, #2nd 4 covariates in group 2, etc.. CoDat <- data.frame(factor(CoDat)) CoDat <- stats::model.matrix(~0+., CoDat) # encode the grouping structure # with dummies Dat <- Dat_EBcoBART(X = X, CoData = CoDat) # X <- Dat$X CoData <- Dat$CoDatap <- 15 n <- 30 X <- matrix(runif(n*p),nrow = n, ncol = p) #all continuous variables Fact <- factor(sample(1:3,n,replace = TRUE)) # factor variables X <- cbind.data.frame(X,Fact) G <- 4 #number of groups for co-data CoDat <- rep(1:G, rep(ncol(X)/G,G)) # first 4 covariates in group 1, #2nd 4 covariates in group 2, etc.. CoDat <- data.frame(factor(CoDat)) CoDat <- stats::model.matrix(~0+., CoDat) # encode the grouping structure # with dummies Dat <- Dat_EBcoBART(X = X, CoData = CoDat) # X <- Dat$X CoData <- Dat$CoData
Function that estimates the prior probabilities of variables being selected in the splitting rules of Bayesian Additive Regression Trees (BART). Estimation is performed using empirical Bayes and co-data, i.e. external information on the explanatory variables.
EBcoBART( Y, X, model, CoData, nIter = 10, EB_k = FALSE, EB_alpha = FALSE, EB_sigma = FALSE, Prob_Init = c(rep(1/ncol(X), ncol(X))), verbose = FALSE, ndpost = 5000, nskip = 5000, nchain = 5, keepevery = 1, ntree = 50, alpha = 0.95, beta = 2, k = 2, sigest = stats::sd(Y) * 0.667, sigdf = 10, sigquant = 0.75 )EBcoBART( Y, X, model, CoData, nIter = 10, EB_k = FALSE, EB_alpha = FALSE, EB_sigma = FALSE, Prob_Init = c(rep(1/ncol(X), ncol(X))), verbose = FALSE, ndpost = 5000, nskip = 5000, nchain = 5, keepevery = 1, ntree = 50, alpha = 0.95, beta = 2, k = 2, sigest = stats::sd(Y) * 0.667, sigdf = 10, sigquant = 0.75 )
Y |
Response variable that can be either continuous or binary. Should be a numeric. |
X |
Explanatory variables. Should be a matrix. If X is a data.frame and contains factors, you may consider the function Dat_EBcoBART |
model |
What type of response variable Y. Can be either continuous or binary |
CoData |
The co-data model matrix with co-data information on explanatory variables in X. Should be a matrix, so not a data.frame. If grouping information is present, please encode this yourself using dummies with dummies representing which group a explanatory variable belongs to. The number of rows of the co-data matrix should equal the number of columns of X. If no CoData is available, but one aims to estimate either prior para- meter k, alpha or sigma, please specify CoData == NULL. |
nIter |
Number of iterations of the EM algorithm |
EB_k |
Logical (T/F). If true, the EM algorithm also estimates prior parameter k (of leaf node parameter prior). Defaults to False. Setting to true increases computational time. |
EB_alpha |
Logical (T/F). If true, the EM algorithm also estimates prior parameter alpha (of tree structure prior). Defaults to False. Setting to true increases computational time. |
EB_sigma |
Logical (T/F). If true, the EM algorithm also estimates prior parameters of the error variance. To do so, the algorithm estimates the degrees of freedom (sigdf) and the quantile (sigest) at which sigquant of the probability mass is placed. Thus, the specified sigquant is kept fixed and sigdf and sigest are updated. Defaults to False. |
Prob_Init |
Initial vector of splitting probabilities for explanatory variables X. #' Length should equal number of columns of X (and number of rows in CoData). Defaults to 1/p, i.e. equal weight for each variable. |
verbose |
Logical. Asks whether algorithm progress should be printed. Defaults to FALSE. |
ndpost |
Number of posterior samples returned by dbarts after burn-in. Same as in dbarts. Defaults to 5000. |
nskip |
Number of burn-in samples. Same as in dbarts. Defaults to 5000. |
nchain |
Number of independent mcmc chains. Same as in dbarts. Defaults to 5. |
keepevery |
Thinning. Same as in dbarts. Defaults to 1. |
ntree |
Number of trees in the BART model. Same as in dbarts. Defaults to 50. |
alpha |
Alpha parameter of tree structure prior. Called base in dbarts. Defaults to 0.95. If EB_alpha is TRUE, this parameter will be the starting value. |
beta |
Beta parameter of tree structure prior. Called power in dbarts. Defaults to 2. |
k |
Parameter for leaf node parameter prior. Same as in dbarts. Defaults to 2. If EB_k is TRUE, this parameter will be the starting value. |
sigest |
Only for continuous response. Estimate of error variance used to set scaled inverse Chi^2 prior on error variance. Same as in dbarts. Defaults to 0.667*var(Y). #' If EB_sigma is TRUE, this parameter will be the starting value. |
sigdf |
Only for continuous response. Degrees of freedom for error variance prior. Same as in dbarts. Defaults to 10. If EB_sigma is TRUE, this parameter will be the starting value. |
sigquant |
Only for continuous response. Quantile at which sigest is placed Same as in dbarts. Defaults to 0.75. If EB_sigma is TRUE, this parameter will be fixed, only sigdf and sigest will be updated. |
A list object with the estimated variable weights, i.e the probabilities #' that variables are selected in the splitting rules. Additionally, the final co-data model is returned. If EB is set to TRUE, estimates of k and/or alpha and/or (sigdf, sigest) are also returned. The prior parameter estimates can then be used in your favorite BART R package that supports #' manually setting the splitting variable probability vector (dbarts and BARTMachine).
Jeroen M. Goedhart, [email protected]
Jerome H. Friedman. "Multivariate Adaptive Regression Splines." The Annals of Statistics, 19(1) 1-67 March, 1991.
Hugh A. Chipman, Edward I. George, Robert E. McCulloch. "BART: Bayesian additive regression trees." The Annals of Applied Statistics, 4(1) 266-298 March 2010.
Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen, Mark A. van de Wiel. "Co-data Learning for Bayesian Additive Regression Trees." arXiv preprint arXiv:2311.09997. 2023 Nov 16.
################################### ### Binary response example ###### ################################### # For continuous response example, see README. # Use data set provided in R package # We set EB=T indicating that we also estimate # tree structure prior parameter alpha # and leaf node prior parameter k data(dat) Xtr <- as.matrix(dat$Xtrain) # Xtr should be matrix object Ytr <- dat$Ytrain Xte <- as.matrix(dat$Xtest) # Xte should be matrix object Yte <- dat$Ytest CoDat <- dat$CoData CoDat <- stats::model.matrix(~., CoDat) # encode grouping by dummies #(include intercept) set.seed(4) # for reproducible results Fit <- EBcoBART(Y = Ytr, X = Xtr, CoData = CoDat, nIter = 2, # Low! Only for illustration model = "binary", EB_k = TRUE, EB_alpha = TRUE, EB_sigma = FALSE, verbose = TRUE, ntree = 5, # Low! Only for illustration nchain = 3, nskip = 500, # Low! Only for illustration ndpost = 500, # Low! Only for illustration Prob_Init = rep(1/ncol(Xtr), ncol(Xtr)), k = 2, alpha = .95, beta = 2) EstProbs <- Fit$SplitProbs # estimated prior weights of variables alpha_EB <- Fit$alpha_est k_EB <- Fit$k_est print(Fit) summary(Fit) # The prior parameter estimates EstProbs, alpha_EB, # and k_EB can then be used in your favorite BART fitting package # We use dbarts: FinalFit <- dbarts::bart(x.train = Xtr, y.train = Ytr, x.test = Xte, ntree = 5, # Low! Only for illustration nchain = 3, # Low! Only for illustration nskip = 200, # Low! Only for illustration ndpost = 200, # Low! Only for illustration k = k_EB, base = alpha_EB, power = 2, splitprobs = EstProbs, combinechains = TRUE, verbose = FALSE)################################### ### Binary response example ###### ################################### # For continuous response example, see README. # Use data set provided in R package # We set EB=T indicating that we also estimate # tree structure prior parameter alpha # and leaf node prior parameter k data(dat) Xtr <- as.matrix(dat$Xtrain) # Xtr should be matrix object Ytr <- dat$Ytrain Xte <- as.matrix(dat$Xtest) # Xte should be matrix object Yte <- dat$Ytest CoDat <- dat$CoData CoDat <- stats::model.matrix(~., CoDat) # encode grouping by dummies #(include intercept) set.seed(4) # for reproducible results Fit <- EBcoBART(Y = Ytr, X = Xtr, CoData = CoDat, nIter = 2, # Low! Only for illustration model = "binary", EB_k = TRUE, EB_alpha = TRUE, EB_sigma = FALSE, verbose = TRUE, ntree = 5, # Low! Only for illustration nchain = 3, nskip = 500, # Low! Only for illustration ndpost = 500, # Low! Only for illustration Prob_Init = rep(1/ncol(Xtr), ncol(Xtr)), k = 2, alpha = .95, beta = 2) EstProbs <- Fit$SplitProbs # estimated prior weights of variables alpha_EB <- Fit$alpha_est k_EB <- Fit$k_est print(Fit) summary(Fit) # The prior parameter estimates EstProbs, alpha_EB, # and k_EB can then be used in your favorite BART fitting package # We use dbarts: FinalFit <- dbarts::bart(x.train = Xtr, y.train = Ytr, x.test = Xte, ntree = 5, # Low! Only for illustration nchain = 3, # Low! Only for illustration nskip = 200, # Low! Only for illustration ndpost = 200, # Low! Only for illustration k = k_EB, base = alpha_EB, power = 2, splitprobs = EstProbs, combinechains = TRUE, verbose = FALSE)