lrt {hglm} | R Documentation |
Likelihood-ratio test for variance components in hglm
Description
Likelihood-ratio test for the estimated variance components (or other dipersion parameters) in hglm
.
Usage
lrt(hglm.obj1, hglm.obj2 = NULL)
Arguments
hglm.obj1 |
a fitted |
hglm.obj2 |
optional, another fitted |
Details
When hglm.obj2 = NULL
, all the random effects variance components in hglm.obj1
are tested against
the null model with only fixed effects. The degree of freedom is determined by comparing the number of random
effects terms in hglm.obj1
and hglm.obj2
or the null fixed-effects-only model. Note that the likelihood-
ratio test statistic for variance estimates, which are bounded above zero, follows a 50:50 mixture distribution
of chi-square with 0 and 1 degree of freedom (Self and Liang 1987 JASA).
Value
Printout summary of the likelihood-ratio test results. Test statistic, p-value, etc. are returned.
References
Self, S. G., & Liang, K.-Y. (1987). Asymptotic Properties of Maximum Likelihood Estimators and Likelihood Ratio Tests Under Nonstandard Conditions. Journal of the American Statistical Association, 82(398), 605-610.
Examples
require(hglm)
## Not run:
set.seed(911)
x1 <- rnorm(100)
x2 <- rnorm(100)
x3 <- rnorm(100)
z1 <- factor(rep(LETTERS[1:10], rep(10, 10)))
z2 <- factor(rep(letters[1:5], rep(20, 5)))
Z1 <- model.matrix(~ 0 + z1)
Z2 <- model.matrix(~ 0 + z2)
u1 <- rnorm(10, 0, sqrt(2))
u2 <- rnorm(5, 0, sqrt(3))
y <- 1 + 2*x1 + 3*x2 + Z1%*%u1 + Z2%*%u2 + rnorm(100, 0, sqrt(exp(x3)))
dd <- data.frame(x1 = x1, x2 = x2, x3 = x3, z1 = z1, z2 = z2, y = y)
m20 <- hglm(X = cbind(rep(1, 100), x1, x2), y = y, Z = Z1,
calc.like = TRUE)
lrt(m20)
m21 <- hglm(X = cbind(rep(1, 100), x1, x2), y = y, Z = cbind(Z1, Z2),
RandC = c(10, 5), calc.like = TRUE)
lrt(m20, m21)
## End(Not run)