glmm {repeated} | R Documentation |
glmm
fits a generalized linear mixed model with a random
intercept using a normal mixing distribution computed by Gauss-Hermite
integration. For the normal, gamma, and inverse Gaussian
distributions, the deviances supplied are -2 log likelihood, not the
usual glm
deviance; the degrees of freedom take into
account estimation of the dispersion parameter.
If weights and/or offset are to be used or the formula transforms the
some variables, all of the data must be supplied in a dataframe.
Because the glm
function is such a hack, if this is not
done, weird error messages will result.
na.omit is not allowed.
glmm(formula, family=family, data=list(), weights=NULL, offset=NULL, nest, delta=1, maxiter=20, points=10, print.level=0, control=glm.control(epsilon=0.0001,maxit=10,trace=FALSE))
formula |
A symbolic description of the model to be fitted. If it contains transformations of the data, a dataframe must be supplied. |
family |
A description of the error distribution and link
function to be used in the model; see family for details. |
data |
An optional dataframe containing the variables in the model. |
weights |
An optional weight vector. If this is used, data must be supplied in a data.frame. |
offset |
The known component in the linear predictor. If this is used, data must be supplied in a data.frame. An offset cannot be specified in the model formula. |
nest |
The variable classifying observations by the unit (cluster) upon which they were observed. |
delta |
If the response variable has been transformed, this is the Jacobian of that transformation, so that AICs are comparable. |
maxiter |
The maximum number of iterations of the outer loop for numerical integration. |
points |
The number of points for Gauss-Hermite integration of the random effect. |
print.level |
If set equal to 2, the log probabilities are printed out when the underflow error is given. |
control |
A list of parameters for controlling the fitting process. |
glmm
returns a list of class glmm
J.K. Lindsey
family
, glm
, glm.control
,
gnlmm
, gnlr
, gnlr3
,
fmr
nest <- gl(5,4) y <- rpois(20,5+2*codes(nest)) # overdispersion model glmm(y~1, family=poisson, nest=gl(20,1), points=3) # clustered model glmm(y~1, family=poisson, nest=nest, points=3)