sim_setx: Compute predictions and first differences at set values
In clarify: Simulation-Based Inference for Regression Models

sim_setx

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 `clarify_sim` object; the output of a call to `sim()` or `misim()`.
`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., `list(v1 = 1:4, v2 = c("A", "B"))`. Any omitted predictors are fixed at a "typical" value. See Details. When `x1` is specified, `x` should identify a single reference unit. For `print()`, a `clarify_setx` object.
`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 `x`. `x1` can only identify a single unit. See Details.
`outcome`	a string containing the name of the outcome or outcome level for multivariate (multiple outcomes) or multi-category 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 `marginaleffects::get_predict()` and eventually to `predict()` in most cases. The default and allowable option depend on the type of model supplied, but almost always corresponds to the response scale (e.g., predicted probabilities for binomial models).
`verbose`	`logical`; whether to display a text progress bar indicating progress and estimated time remaining for the procedure. Default is `TRUE`.
`cl`	a cluster object created by `parallel::makeCluster()`, or an integer to indicate the number of child-processes (integer values are ignored on Windows) for parallel evaluations. See `pbapply::pblapply()` for details. If `NULL`, no parallelization will take place.
`digits`	the minimum number of significant digits to be used; passed to `print.data.frame()`.
`max.ests`	the maximum number of estimates to display.
`...`	optional arguments passed to `FUN`.

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 to TRUE if x1 is specified and FALSE otherwise

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)

clarify documentation built on June 22, 2024, 12:09 p.m.