Least Squares Regression for Continuous 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 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.
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
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:
zelig(formula, data, model = NULL, ..., weights = NULL, by, bootstrap = FALSE)
The zelig function estimates a variety of statistical models
data(macro) z.out1 <- zelig(unem ~ gdp + capmob + trade, model = "ls", data = macro)#> How to cite this model in Zelig: #> R Core Team. 2007. #> ls: Least Squares Regression for Continuous Dependent Variables #> in Christine Choirat, Christopher Gandrud, James Honaker, Kosuke Imai, Gary King, and Olivia Lau, #> "Zelig: Everyone's Statistical Software," http://zeligproject.org/