Weibull Regression for Duration 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.
In addition to the standard inputs, zelig() takes the following additional options for weibull regression:
robust: defaults to FALSE. If TRUE, zelig() computes
robust standard errors based on sandwich estimators based on the options in cluster.
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=TRUErobust=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:
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
zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)
The zelig function estimates a variety of statistical models
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