Zelig-exp-class: Exponential Regression for Duration Dependent Variables
In Zelig: Everyone's Statistical Software

Description Arguments Details Value Methods See Also Examples

Exponential Regression for Duration 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 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. For example, to run the same model on all fifty states, you could use: `z.out <- zelig(y ~ x1 + x2, data = mydata, model = 'ls', by = 'state')` 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.
`robust`	defaults to FALSE. If TRUE, zelig() computes robust standard errors based on sandwich estimators and the options selected in cluster.
`if`	robust = TRUE, you may select a variable to define groups of correlated observations. Let x3 be a variable that consists of either discrete numeric values, character strings, or factors that define strata. Then z.out <- zelig(y ~ x1 + x2, robust = TRUE, cluster = "x3",model = "exp", data = mydata) means that the observations can be correlated within the strata defined by the variable x3, and that robust standard errors should be calculated according to those clusters. If robust = TRUE but cluster is not specified, zelig() assumes that each observation falls into its own cluster.

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.

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.

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_exp.html

library(Zelig)
data(coalition)
library(survival)
z.out <- zelig(Surv(duration, ciep12) ~ fract + numst2, model = "exp",
               data = coalition)
summary(z.out)