Integrate over Random Effects

Used to conduct Monte Carlo integration over Gaussian random effects. Not intended to be called directly by most users.

Usage

integratere(d, sd, L, k, yhat, backtrans)

integratereR(d, sd, L, k, yhat, backtrans)

Arguments

d

A list with model matrices for each random effect block.

sd

A list with standard deviation matrices for each random effect block where rows are different posterior draws.

L

A list with matrices for each random effect block containing the parts of the L matrix, the Cholesky decomposition of the random effect correlation matrix.

k

An integer, the number of samples for Monte Carlo integration.

yhat

A matrix of the fixed effects predictions

backtrans

An integer, indicating the type of back transformation. 0 indicates inverse logit (e.g., for logistic regression). 1 indicates exponential (e.g., for poisson or negative binomial regression or if outcome was natural log transformed). 2 indicates square (e.g., if outcome was square root transformed). 3 indicates inverse (e.g., if outcome was inverse transformed such as Gamma regression) Any other integer results in no transformation. -9 is recommended as the option for no transformation as any future transformations supported will be other, positive integers.

Value

A numeric matrix with the Monte Carlo integral calculated.

Functions

• integratereR: Pure R implementation of integratere

Examples

integratere(
  d = list(matrix(1, 1, 1)),
  sd = list(matrix(1, 2, 1)),
  L = list(matrix(1, 2, 1)),
  k = 10L,
  yhat = matrix(0, 2, 1),
  backtrans = 0L)
#>           [,1]
#> [1,] 0.5268868
#> [2,] 0.4307043