Sample Global Error Variance

Sample one iteration of the (inverse gamma) global variance model

This function is intended for advanced use cases in which users require detailed control of sampling algorithms and data structures. Minimal input validation and error checks are performed – users are responsible for providing the correct inputs. For tutorials on the "proper" usage of the stochtree's advanced workflow, we provide several vignettes at stochtree.ai

Usage

sampleGlobalErrorVarianceOneIteration(residual, dataset, rng, a, b)

Arguments

residual

Outcome class

dataset

ForestDataset class

rng

C++ random number generator

a

Global variance shape parameter

b

Global variance scale parameter

Value

None

Examples

X <- matrix(runif(10*100), ncol = 10)
y <- -5 + 10*(X[,1] > 0.5) + rnorm(100)
y_std <- (y-mean(y))/sd(y)
forest_dataset <- createForestDataset(X)
outcome <- createOutcome(y_std)
rng <- createCppRNG(1234)
a <- 1.0
b <- 1.0
sigma2 <- sampleGlobalErrorVarianceOneIteration(outcome, forest_dataset, rng, a, b)