glmmbootFit {glmmML} | R Documentation |
Generalized Linear Models with fixed effects grouping
Description
'glmmbootFit' is the workhorse in the function glmmboot
. It is
suitable to call instead of 'glmmboot', e.g. in simulations.
Usage
glmmbootFit(X, Y, weights = rep(1, NROW(Y)),
start.coef = NULL, cluster = rep(1, length(Y)),
offset = rep(0, length(Y)), family = binomial(),
control = list(epsilon = 1.e-8, maxit = 200, trace
= FALSE), boot = 0)
Arguments
X |
The design matrix (n * p). |
Y |
The response vector of length n. |
weights |
Case weights. |
start.coef |
start values for the parameters in the linear predictor (except the intercept). |
cluster |
Factor indicating which items are correlated. |
offset |
this can be used to specify an a priori known component to be included in the linear predictor during fitting. |
family |
Currently, the only valid values are |
control |
A list. Controls the convergence criteria. See
|
boot |
number of bootstrap replicates. If equal to zero, no test of significance of the grouping factor is performed. If non-zero, it should be large, at least, say, 2000. |
Value
A list with components
coefficients |
Estimated regression coefficients (note: No intercept). |
logLik |
The maximised log likelihood. |
cluster.null.deviance |
deviance from a moddel without cluster. |
frail |
The estimated cluster effects. |
bootLog |
The maximised bootstrap log likelihood values. A vector
of length |
bootP |
The bootstrap p value. |
variance |
The variance-covariance matrix of the fixed effects (no intercept). |
sd |
The standard errors of the |
boot_rep |
The number of bootstrap replicates. |
Note
A profiling approach is used to estimate the cluster effects.
Author(s)
Göran Broström
See Also
Examples
## Not run
x <- matrix(rnorm(1000), ncol = 1)
id <- rep(1:100, rep(10, 100))
y <- rbinom(1000, size = 1, prob = 0.4)
fit <- glmmbootFit(x, y, cluster = id, boot = 200)
summary(fit)
## End(Not run)
## Should show no effects. And boot too small.