Zelig-relogit-class: Rare Events Logistic Regression for Dichotomous Dependent...
In Zelig: Everyone's Statistical Software

Description Arguments Details Value Methods See Also Examples

Rare Events Logistic Regression for Dichotomous Dependent Variables

`formula`	a symbolic representation of the model to be estimated, in the form `y ~ x1 + x2`, where `y` is the dependent variable and `x1` and `x2` are the explanatory variables, and `y`, `x1`, and `x2` are contained in the same dataset. (You may include more than two explanatory variables, of course.) The `+` symbol means “inclusion” not “addition.” You may also include interaction terms and main effects in the form `x1x2` without computing them in prior steps; `I(x1x2)` to include only the interaction term and exclude the main effects; and quadratic terms in the form `I(x1^2)`.
`model`	the name of a statistical model to estimate. For a list of other supported models and their documentation see: http://docs.zeligproject.org/articles/.
`data`	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 `Amelia` or `to_zelig_mi`).
`...`	additional arguments passed to `zelig`, relevant for the model to be estimated.
`by`	a factor variable contained in `data`. If supplied, `zelig` will subset the data frame based on the levels in the `by` variable, and estimate a model for each subset. This can save a considerable amount of effort. You may also use `by` to run models using MatchIt subclasses.
`cite`	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 tau, 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 FALSE 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: http://docs.zeligproject.org/articles/weights.html.
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: http://docs.zeligproject.org/articles/bootstraps.html.

Depending on the class of model selected, zelig will return an object with elements including coefficients, residuals, and formula which may be summarized using summary(z.out) or individually extracted using, for example, coef(z.out). See 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 from_zelig_model.

modcall_formula_transformer(): Transform model call formula.
show(signif.stars = FALSE, subset = NULL, bagging = FALSE): Display a Zelig object
zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE): The zelig function estimates a variety of statistical models

Vignette: http://docs.zeligproject.org/articles/zelig_relogit.html

library(Zelig)
data(mid)
z.out1 <- zelig(conflict ~ major + contig + power + maxdem + mindem + years,
              data = mid, model = "relogit", tau = 1042/303772)
summary(z.out1)