setx1: Setting Explanatory Variable Values for First Differences
In Zelig: Everyone's Statistical Software

Description Usage Arguments Value Author(s) See Also Examples

This documentation describes the setx1 Zelig 4 compatibility wrapper function. The wrapper is primarily useful for setting fitted values for creating first differences in piped workflows.

1	setx1(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`, `setx1` will coerce `fn = NULL` and ignore the additional arguments in `...`. If `cond = TRUE` and `data = NULL`, `setx1` 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

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, setx1 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]])

Christopher Gandrud, Matt Owen, Olivia Lau, Kosuke Imai

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

library(dplyr) # contains pipe operator %>%
data(turnout)

# plot first differences
zelig(Fertility ~ Education, data = swiss, model = 'ls') %>%
      setx(z4, Education = 10) %>%
      setx1(z4, Education = 30) %>%
      sim() %>%
      plot()