setx: Setting Explanatory Variable Values
In Zelig: Everyone's Statistical Software

Description Usage Arguments Details Value Author(s) See Also Examples

The setx function uses the variables identified in the formula generated by zelig and sets the values of the explanatory variables to the selected values. Use setx after zelig and before sim to simulate quantities of interest.

1	setx(obj, fn = NULL, data = NULL, cond = FALSE, ...)

`obj`	output object from `zelig`
`fn`	a list of functions to apply to the data frame
`data`	a new data frame used to set the values of explanatory variables. If `data = NULL` (the default), the data frame called in `zelig` is used
`cond`	a logical value indicating whether unconditional (default) or conditional (choose `cond = TRUE`) prediction should be performed. If you choose `cond = TRUE`, `setx` will coerce `fn = NULL` and ignore the additional arguments in `...`. If `cond = TRUE` and `data = NULL`, `setx` will prompt you for a data frame.
`...`	user-defined values of specific variables for overwriting the default values set by the function `fn`. For example, adding `var1 = mean(data\$var1)` or `x1 = 12` explicitly sets the value of `x1` to 12. In addition, you may specify one explanatory variable as a range of values, creating one observation for every unique value in the range of values

This documentation describes the setx Zelig 4 compatibility wrapper function.

The output is returned in a field to the Zelig object. For unconditional prediction, x.out is a model matrix based on the specified values for the explanatory variables. For multiple analyses (i.e., when choosing the by option in zelig, setx returns the selected values calculated over the entire data frame. If you wish to calculate values over just one subset of the data frame, the 5th subset for example, you may use: x.out <- setx(z.out[[5]])

Matt Owen, Olivia Lau and Kosuke Imai

The full Zelig manual may be accessed online at http://docs.zeligproject.org/articles/

# Unconditional prediction:
data(turnout)
z.out <- zelig(vote ~ race + educate, model = 'logit', data = turnout)
x.out <- setx(z.out)
s.out <- sim(z.out, x = x.out)