Epi (version 2.56)

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

See Also

glm

Examples

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