sim_setx {clarify}  R Documentation 
Compute predictions and first differences at set values
Description
sim_setx()
is a wrapper for sim_apply()
that computes predicted values of
the outcome at specified values of the predictors, sometimes called marginal
predictions. One can also compute the difference between two marginal
predictions (the "first difference"). Although any function that accepted
clarify_est
objects can be used with sim_setx()
output objects, a
special plotting function, plot.clarify_setx()
, can be used to plot marginal
predictions.
Usage
sim_setx(
sim,
x = list(),
x1 = list(),
outcome = NULL,
type = NULL,
verbose = TRUE,
cl = NULL
)
## S3 method for class 'clarify_setx'
print(x, digits = NULL, max.ests = 6, ...)
Arguments
sim 
a 
x 
a data.frame containing a reference grid of predictor values or a named list of values each predictor should take defining such a
reference grid, e.g., For 
x1 
a data.frame or named list of the value each predictor should take to compute the
first difference from the predictor combination specified in 
outcome 
a string containing the name of the outcome or outcome level for multivariate (multiple outcomes) or multicategory outcomes. Ignored for univariate (single outcome) and binary outcomes. 
type 
a string containing the type of predicted values (e.g., the link or the response). Passed to 
verbose 

cl 
a cluster object created by 
digits 
the minimum number of significant digits to be used; passed to 
max.ests 
the maximum number of estimates to display. 
... 
optional arguments passed to 
Details
When x
is a named list of predictor values, they will be crossed
to form a reference grid for the marginal predictions. Any predictors not
set in x
are assigned their "typical" value, which, for factor,
character, logical, and binary variables is the mode, for numeric variables
is the mean, and for ordered variables is the median. These values can be
seen in the "setx"
attribute of the output object. If x
is empty, a
prediction will be made at a point corresponding to the typical value of
every predictor. Estimates are identified (in summary()
, etc.) only by
the variables that differ across predictions.
When x1
is supplied, the first difference is computed, which here is
considered as the difference between two marginal predictions. One marginal
prediction must be specified in x
and another, ideally with a single
predictor changed, specified in x1
.
Value
a clarify_setx
object, which inherits from clarify_est
and is similar to the output of sim_apply()
, with the following additional attributes:

"setx"
 a data frame containing the values at which predictions are to be made 
"fd"
 whether or not the first difference is to be computed; set toTRUE
ifx1
is specified andFALSE
otherwise
See Also
sim_apply()
, which provides a general interface to computing any
quantities for simulationbased inference; plot.clarify_setx()
for plotting the
output of a call to sim_setx()
; summary.clarify_est()
for computing
pvalues and confidence intervals for the estimated quantities.
Examples
data("lalonde", package = "MatchIt")
fit < lm(re78 ~ treat + age + educ + married + race + re74,
data = lalonde)
# Simulate coefficients
set.seed(123)
s < sim(fit, n = 100)
# Predicted values at specified values of values, typical
# values for other predictors
est < sim_setx(s, x = list(treat = 0:1,
re74 = c(0, 10000)),
verbose = FALSE)
summary(est)
plot(est)
# Predicted values at specified grid of values, typical
# values for other predictors
est < sim_setx(s, x = list(age = c(20, 25, 30, 35),
married = 0:1),
verbose = FALSE)
summary(est)
plot(est)
# First differences of treat at specified value of
# race, typical values for other predictors
est < sim_setx(s, x = data.frame(treat = 0, race = "hispan"),
x1 = data.frame(treat = 1, race = "hispan"),
verbose = FALSE)
summary(est)
plot(est)