mixed_solve_cpp: Mixed Model Solver using Rcpp and roptim

In bbuchsbaum/fmrireg: R package for the anlysis of fmri data

Mixed Model Solver using Rcpp and roptim

Description

This function solves a mixed model using Rcpp and roptim for optimization. It estimates variance components in a mixed model, potentially speeding up computations compared to the pure R implementation.

Usage

mixed_solve_cpp(
  y,
  Z = NULL,
  K = NULL,
  X = NULL,
  method = "REML",
  bounds = c(1e-09, 1e+09),
  SE = FALSE,
  return_Hinv = FALSE
)

Arguments

y	Response vector.
Z	Design matrix for random effects (default: identity matrix of size n).
K	Kinship matrix (default: NULL).
X	Design matrix for fixed effects (default: vector of ones).
method	Optimization method, either "REML" or "ML" (default: "REML").
bounds	Bounds for the optimizer (default: c(1e-9, 1e9)).
SE	Logical, whether to return standard errors (default: FALSE).
return_Hinv	Logical, whether to return the inverse of H (default: FALSE).

Value

A list containing:

Vu	Estimated variance component for random effects.
Ve	Estimated variance component for residuals.
beta	Estimated fixed effects coefficients.
u	Estimated random effects coefficients.
LL	Log-likelihood of the model.
beta.SE	Standard errors of fixed effects coefficients (if SE = TRUE).
u.SE	Standard errors of random effects coefficients (if SE = TRUE).
Hinv	Inverse of H (if return_Hinv = TRUE).

Examples

## Not run: 
# Example usage with random data
set.seed(123)
n <- 100
y <- rnorm(n)
Z <- matrix(rnorm(n * 5), n, 5)
K <- diag(5)
X <- matrix(1, n, 1)
result <- mixed_solve_cpp(y, Z, K, X)

## End(Not run)

bbuchsbaum/fmrireg documentation built on March 1, 2025, 11:20 a.m.