test.varcomp: Testing Zero Variance Components in Linear Mixed Models
In lmeVarComp: Testing for a Subset of Variance Components in Linear Mixed Models

Description Usage Arguments Value Author(s) References See Also Examples

View source: R/test.varcomp.R

test.varcomp tests whether certain variance components are zeros. This function provides a formula interface to the rlr.test function.

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test.varcomp(fixed, random, test, data = NULL, Sigma = NULL, 
  type = "RLR", nsim = 5000L, seed = 130623L, 
  keep.matrices = FALSE)

`fixed`	a two-sided formula specifying the response and the fixed effects.
`random`	a one-sided formula specifying the random effects (not including the error term).
`test`	an integer vector of the indices of random effects to be tested.
`data`	an optional data frame, list or environment containing the variables in the model.
`Sigma`	an optional list of symmetric and positive definite matrices specifying the correlation structures of random effects. If `NULL`, default to identity matrices.
`type`	`RLR` for restricted likelihood ratio test and generalized F-test
`nsim`	number of simulations from the null distribution.
`seed`	a seed to be set before simulating from the null distribution.
`keep.matrices`	whether the design matrices for fixed effects and random effects, as well as the response vector, will be returned.

A list containing the following components:

`RLRT`	A vector of the test statistic and the p-value of restricted likelihood ratio test.
`GFT`	A vector of the test statistic and the p-value of generalized F-test.
`Y`	If `keep.matrices` is `TRUE`, the response.
`X`	If `keep.matrices` is `TRUE`, the fixed effects design matrix.
`Z`	If `keep.matrices` is `TRUE`, a list of the random effects design matrices.
`Sigma`	If `keep.matrices` is `TRUE`, a list of the random effects correlation structures.

Yichi Zhang

Zhang, Y., Staicu, A.-M., and Maity, A. (2016). Testing for additivity in non-parametric regression. Canadian Journal of Statistics, 44: 445-462. doi: 10.1002/cjs.11295

rlr.test

n1 <- 5L
n2 <- 6L
n0 <- 4L
A <- gl(n1, n2 * n0)
B <- rep(gl(n2, n0), n1)
set.seed(1L)
Y <- 1 + rnorm(n1, 0, 0.7)[A] + rnorm(n2, 0, 0.3)[B] + 
  rnorm(n1 * n2, 0, 0.5)[A : B] + rnorm(n1 * n2 * n0, 0, 1)
test.varcomp(Y ~ 1, ~ -1 + A + B + A:B, test = c(2L, 3L), 
  nsim = 2000L, seed = 2L)