clogistic: Conditional logistic regression

Description

Estimates a logistic regression model by maximizing the conditional likelihood. The conditional likelihood calculations are exact, and scale efficiently to strata with large numbers of cases.

Usage

clogistic(formula, strata, data, subset, na.action, init,
model = TRUE, x = FALSE, y = TRUE, contrasts = NULL,
iter.max=20, eps=1e-6, toler.chol = sqrt(.Machine$double.eps))

Value

An object of class "clogistic". This is a list containing the following components:

coefficients: the estimates of the log-odds ratio parameters. If the model is over-determined there will be missing values in the vector corresponding to the redundant columns in the model matrix.
var: the variance matrix of the coefficients. Rows and columns corresponding to any missing coefficients are set to zero.
loglik: a vector of length 2 containing the log-likelihood with the initial values and with the final values of the coefficients.
iter: number of iterations used.
n: number of observations used. Observations may be dropped either because they are missing, or because they belong to a homogeneous stratum. For more details on which observations were used, see informative below.
informative: if model=TRUE, a logical vector of length equal to the number of rows in the model frame. This indicates whether an observation is informative. Strata that are homogeneous with respect to either the outcome variable or the predictor variables are uninformative, in the sense that they can be removed without modifying the estimates or standard errors. If model=FALSE, this is NULL.

The output will also contain the following, for documentation see the

glm object: terms, formula,

call, contrasts, xlevels, and, optionally,

x, y, and/or frame.

Arguments

formula: Model formula
strata: Factor describing membership of strata for conditioning
data: data frame containing the variables in the formula and strata arguments
subset: subset of records to use
na.action: missing value handling
init: initial values
model: a logical value indicating whether model frame should be included as a component of the returned value
x,y: logical values indicating whether the response vector and model matrix used in the fitting process should be returned as components of the returned value.
contrasts: an optional list. See the contrasts.arg of model.matrix.default
iter.max: maximum number of iterations
eps: Convergence tolerance. Iteration continues until the relative change in the conditional log likelihood is less than eps. Must be positive.
toler.chol: Tolerance used for detection of a singularity during a Cholesky decomposition of the variance matrix. This is used to detect redundant predictor variables. Must be less than eps.

Author

Martyn Plummer

Examples

Run this code

  data(bdendo)
  clogistic(d ~ cest + dur, strata=set, data=bdendo)