sim_ame {clarify} | R Documentation |
Compute average marginal effects
Description
sim_ame()
is a wrapper for sim_apply()
that computes average marginal effects, the average effect of changing a single variable from one value to another (i.e., from one category to another for categorical variables or a tiny change for continuous variables).
Usage
sim_ame(
sim,
var,
subset = NULL,
by = NULL,
contrast = NULL,
outcome = NULL,
type = NULL,
eps = 1e-05,
verbose = TRUE,
cl = NULL
)
## S3 method for class 'clarify_ame'
print(x, digits = NULL, max.ests = 6, ...)
Arguments
sim |
a |
var |
either the name of a variable for which marginal effects are to be computed or a named list of length one containing the values the variable should take. If a list is supplied or the named variables is categorical (factor, character, or having two values), categorical calculations will be triggered. Otherwise, continuous calculations will be triggered. See Details. |
subset |
optional; a vector used to subset the data used to compute the marginal effects. This will be evaluated within the original dataset used to fit the model using |
by |
a one-sided formula or character vector containing the names of variables for which to stratify the estimates. Each quantity will be computed within each level of the complete cross of the variables specified in |
contrast |
a string containing the name of a contrast between the average marginal means when the variable named in |
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 |
eps |
when the variable named in |
verbose |
|
cl |
a cluster object created by |
x |
a |
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
sim_ame()
operates differently depending on whether continuous or categorical calculations are triggered. To trigger categorical calculations, var
should be a string naming a factor, character, or binary variable or a named list with specific values given (e.g., var = list(x1 = c(1, 2 ,3))
). Otherwise, continuous calculations are triggered.
Categorical calculations involve computing average marginal means at each level of var
. The average marginal mean is the average predicted outcome value after setting all units' value of var
to one level. (This quantity has several names, including the average potential outcome, average adjusted prediction, and standardized mean). When var
only takes on two levels (or it is supplied as a list and only two values are specified), a contrast between the average marginal means can be computed by supplying an argument to contrast
. Contrasts can be manually computed using transform()
afterward as well.
Continuous calculations involve computing the average of marginal effects of var
across units. A marginal effect is the instantaneous rate of change corresponding to changing a unit's observed value of var
by a tiny amount and considering to what degree the predicted outcome changes. The ratio of the change in the predicted outcome to the change in the value of var
is the marginal effect; these are averaged across the sample to arrive at an average marginal effect. The "tiny amount" used is eps
times the standard deviation of the focal variable.
If unit-level weights are included in the model fit (and discoverable using insight::get_weights()
), all means will be computed as weighted means.
Effect measures
The effect measures specified in contrast
are defined below. Typically only "diff"
is appropriate for continuous outcomes and "diff"
or "irr"
are appropriate for count outcomes; the rest are appropriate for binary outcomes. For a focal variable with two levels, 0
and 1
, and an outcome Y
, the average marginal means will be denoted in the below formulas as E[Y(0)]
and E[Y(1)]
, respectively.
contrast | Formula |
"diff" | E[Y(1)] - E[Y(0)] |
"rr" | E[Y(1)] / E[Y(0)] |
"sr" | (1 - E[Y(1)]) / (1 - E[Y(0)]) |
"srr" | 1 - sr if E[Y(1)] > E[Y(0)] |
rr - 1 if E[Y(1)] < E[Y(0)] |
|
0 otherwise |
|
"or" | O[Y(1)] / O[Y(0)] |
where O[Y(.)] = E[Y(.)] / (1 - E[Y(.)]) |
|
"nnt" | 1 / (E[Y(1)] - E[Y(0)]) |
The log(.)
versions are defined by taking the log()
(natural log) of the corresponding effect measure.
Value
A clarify_ame
object, which inherits from clarify_est
and is similar to
the output of sim_apply()
, with the additional attributes "var"
containing
the variable named in var
and "by"
containing the names of the variables specified in by
(if any). The average adjusted predictions will be named
E[Y({v})]
, where {v}
is replaced with the values the focal variable
(var
) takes on. The average marginal effect for a continuous var
will be
named E[dY/d({x})]
where {x}
is replaced with var
. When by
is specified, the average adjusted predictions will be named E[Y({v})|{b}]
and the average marginel effect E[dY/d({x})|{b}]
where {b}
is a comma-separated list of of values of the by
variables at which the quantity is computed. See examples.
See Also
sim_apply()
, which provides a general interface to computing any
quantities for simulation-based inference; plot.clarify_est()
for plotting the
output of a call to sim_ame()
; summary.clarify_est()
for computing
p-values and confidence intervals for the estimated quantities.
marginaleffects::marginaleffects()
, marginaleffects::comparisons()
, and margins::margins()
for delta method-based implementations of computing average marginal effects.
Examples
data("lalonde", package = "MatchIt")
# Fit the model
fit <- glm(I(re78 > 0) ~ treat + age + race +
married + re74,
data = lalonde, family = binomial)
# Simulate coefficients
set.seed(123)
s <- sim(fit, n = 100)
# Average marginal effect of `age`
est <- sim_ame(s, var = "age", verbose = FALSE)
summary(est)
# Contrast between average adjusted predictions
# for `treat`
est <- sim_ame(s, var = "treat", contrast = "rr",
verbose = FALSE)
summary(est)
# Average adjusted predictions for `race`; need to follow up
# with contrasts for specific levels
est <- sim_ame(s, var = "race", verbose = FALSE)
est <- transform(est,
`RR(h,b)` = `E[Y(hispan)]` / `E[Y(black)]`)
summary(est)
# Average adjusted predictions for `treat` within levels of
# `married`, first using `subset` and then using `by`
est0 <- sim_ame(s, var = "treat", subset = married == 0,
contrast = "rd", verbose = FALSE)
names(est0) <- paste0(names(est0), "|married=0")
est1 <- sim_ame(s, var = "treat", subset = married == 1,
contrast = "rd", verbose = FALSE)
names(est1) <- paste0(names(est1), "|married=1")
summary(cbind(est0, est1))
est <- sim_ame(s, var = "treat", by = ~married,
contrast = "rd", verbose = FALSE)
est
summary(est)
# Average marginal effect of `re74` within levels of
# married*race
est <- sim_ame(s, var = "age", by = ~married + race,
verbose = FALSE)
est
summary(est, null = 0)
# Comparing AMEs between married and unmarried for
# each level of `race`
est_diff <- est[4:6] - est[1:3]
names(est_diff) <- paste0("AME_diff|", levels(lalonde$race))
summary(est_diff)