Zelig-poisson-class: Poisson Regression for Event Count Dependent Variables

In Zelig: Everyone's Statistical Software

Description

Poisson Regression for Event Count Dependent Variables

Arguments

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 x1*x2 without computing them in prior steps; I(x1*x2) 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.
id:	where id is a variable which identifies the clusters. The data should be sorted by id and should be ordered within each cluster when appropriate
corstr:	character string specifying the correlation structure: "independence", "exchangeable", "ar1", "unstructured" and "userdefined"
geeglm:	See geeglm in package geepack for other function arguments

Details

Additional parameters avaialable to this model 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.

Value

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.

See Also

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

Examples

library(Zelig)
data(sanction)
z.out <- zelig(num ~ target + coop, model = "poisson", data = sanction)
summary(z.out)

Zelig documentation built on Jan. 8, 2021, 2:26 a.m.