# R/BayesianBootstrap.R In LaplacesDemon: Complete Environment for Bayesian Inference

#### Documented in BayesianBootstrap

```###########################################################################
# BayesianBootstrap                                                       #
#                                                                         #
# The purpose of the BayesianBootstrap is to allow the user to produce    #
# either bootstrapped weights or statistics.                              #
###########################################################################

BayesianBootstrap <- function(X, n=1000, Method="weights", Status=NULL)
{
### Initial Checks
if(missing(X)) stop("X is a required argument.")
if(!is.matrix(X)) X <- as.matrix(X)
if(any(!is.finite(X))) stop("Non-finite values found in X.")
S <- round(abs(n))
if(S < 1) S <- 1
if(!(is.numeric(Status) & (length(Status) == 1))) Status <- S + 1
else {
Status <- round(abs(Status))
if(Status < 1 | Status > S) Status <- S + 1}
N <- nrow(X)
J <- ncol(X)
if(identical(Method, "weights")) {
BB <- replicate(S, diff(c(0, sort(runif(N-1)), 1)))
return(BB)}
### Bayesian Bootstrap: Statistics
BB <- vector("list", S)
for (s in 1:S) {
if(s %% Status == 0) cat("\nBootstrapped Samples:", s)
u <- c(0, sort(runif(N - 1)), 1)
g <- diff(u)
BB[[s]] <- Method(X, g)}
if(Status < S) cat("\n\nThe Bayesian Bootstrap has finished.\n\n")
### Output
BB <- lapply(BB, identity)
if(is.vector(BB[[1]]))
if(length(BB[[1]]) == 1) BB <- as.matrix(BB)
else {
B <- matrix(unlist(BB), S, length(BB[[1]]), byrow=TRUE)
colnames(B) <- names(BB[[1]])
BB <- B
}
else {
if(is.null(dim(BB[[1]])))
stop("Method must return a vector, matrix or array")
B <- array(NA, dim=c(S, dim(BB[[1]])))
for (s in 1:S) {B[s,,] <- BB[[s]]}
BB <- B
}
return(BB)
}

#End
```

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LaplacesDemon documentation built on July 9, 2021, 5:07 p.m.