glmm: Generalized Linear Mixed Models

Description

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.

Usage

glmm(
  formula,
  family = gaussian,
  data = list(),
  weights = NULL,
  offset = NULL,
  nest,
  delta = 1,
  maxiter = 20,
  points = 10,
  print.level = 0,
  control = glm.control(epsilon = 1e-04, maxit = 10, trace = FALSE)
)

Value

glmm returns a list of class glmm

Arguments

formula: A symbolic description of the model to be fitted. If it contains transformations of the data, including cbind for binomial 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.; A dataframe containing the variables in the model, that is optional in simple cases, but required in certain situations as specified elsewhere in this help page.
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.; 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.

Author

J.K. Lindsey

Details

If weights and/or offset are to be used or the formula transforms 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.

Examples

Run this code


# Poisson counts
nest <- gl(5,4)
y <- rpois(20,5+2*as.integer(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)