Rare Events Logistic Regression for Dichotomous Dependent Variables
a symbolic representation of the model to be
estimated, in the form
the name of a statistical model to estimate. For a list of other supported models and their documentation see: http://docs.zeligproject.org/articles/.
the name of a data frame containing the variables
referenced in the formula or a list of multiply imputed data frames
each having the same variable names and row numbers (created by
additional arguments passed to
a factor variable contained in
If is set to 'TRUE' (default), the model citation will be printed to the console.
The relogit procedure supports four optional arguments in addition to the standard arguments for zelig(). You may additionally use:
tau: a vector containing either one or two values for
the true population fraction of ones. Use, for example, tau = c(0.05, 0.1) to specify
that the lower bound on tau is 0.05 and the upper bound is 0.1. If left unspecified, only
finite-sample bias correction is performed, not case-control correction.
case.control: if tau is specified, choose a method to correct for case-control
sampling design: "prior" (default) or "weighting".
bias.correct: a logical value of
TRUE (default) or
indicating whether the intercept should be corrected for finite sample (rare events) bias.
Additional parameters avaialable to many models include:
weights: vector of weight values or a name of a variable in the dataset
by which to weight the model. For more information see:
bootstrap: logical or numeric. If
FALSE don't use bootstraps to
robustly estimate uncertainty around model parameters due to sampling error.
If an integer is supplied, the number of boostraps to run.
For more information see:
Depending on the class of model selected,
zelig will return
an object with elements including
formula which may be summarized using
summary(z.out) or individually extracted using, for example,
http://docs.zeligproject.org/articles/getters.html for a list of
functions to extract model components. You can also extract whole fitted
model objects using
Transform model call formula.
zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)
The zelig function estimates a variety of statistical models
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