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Last data update: 2014.03.03
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R: Generalized Linear Model with random intercept
| glmmML.fit | R Documentation |
Generalized Linear Model with random intercept
Description
This function is called by glmmML, but it can also be called
directly by the user.
Usage
glmmML.fit(X, Y, weights = rep(1, NROW(Y)), cluster.weights = rep(1, NROW(Y)),
start.coef = NULL, start.sigma = NULL,
fix.sigma = FALSE,
cluster = NULL, offset = rep(0, nobs), family = binomial(),
method = 1, n.points = 1,
control = list(epsilon = 1.e-8, maxit = 200, trace = FALSE),
intercept = TRUE, boot = 0, prior = 0)
Arguments
X |
Design matrix of covariates.
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Y |
Response vector. Or two-column matrix.
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weights |
Case weights. Defaults to one.
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cluster.weights |
Cluster weights. Defaults to one.
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start.coef |
Starting values for the coefficients.
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start.sigma |
Starting value for the mixing standard deviation.
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fix.sigma |
Should sigma be fixed at start.sigma?
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cluster |
The clustering variable.
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offset |
The offset in the model.
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family |
Family of distributions. Defaults to binomial with logit
link. Other possibilities are binomial with cloglog link and poisson
with log link.
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method |
Laplace (1) or Gauss-hermite (0)?
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n.points |
Number of points in the Gauss-Hermite
quadrature. Default is n.points = 1, which is equivalent to
Laplace approximation.
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control |
Control of the iterations. See glm.control.
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intercept |
Logical. If TRUE, an intercept is fitted.
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boot |
Integer. If > 0, bootstrapping with boot
replicates.
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prior |
Which prior distribution? 0 for "gaussian", 1 for
"logistic", 2 for "cauchy".
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Details
In the optimisation, "vmmin" (in C code) is used.
Value
A list. For details, see the code, and glmmML.
Author(s)
Göran Broström
References
Broström (2003)
See Also
glmmML, glmmPQL, and
lmer.
Examples
x <- cbind(rep(1, 14), rnorm(14))
y <- rbinom(14, prob = 0.5, size = 1)
id <- rep(1:7, 2)
glmmML.fit(x, y, cluster = id)
Results
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providing 
.