comparisons {marginaleffects} | R Documentation |
Predict the outcome variable at different regressor values (e.g., college
graduates vs. others), and compare those predictions by computing a difference,
ratio, or some other function. comparisons()
can return many quantities of
interest, such as contrasts, differences, risk ratios, changes in log odds,
slopes, elasticities, etc.
comparisons()
: unit-level (conditional) estimates.
avg_comparisons()
: average (marginal) estimates.
The newdata
argument and the datagrid()
function can be used to control where statistics are evaluated in the predictor space: "at observed values", "at the mean", "at representative values", etc.
See the comparisons vignette and package website for worked examples and case studies:
comparisons(
model,
newdata = NULL,
variables = NULL,
type = NULL,
vcov = TRUE,
by = FALSE,
conf_level = 0.95,
transform_pre = "difference",
transform_post = NULL,
cross = FALSE,
wts = NULL,
hypothesis = NULL,
df = Inf,
eps = NULL,
...
)
avg_comparisons(
model,
newdata = NULL,
variables = NULL,
type = NULL,
vcov = TRUE,
by = TRUE,
conf_level = 0.95,
transform_pre = "difference",
transform_post = NULL,
cross = FALSE,
wts = NULL,
hypothesis = NULL,
df = Inf,
eps = NULL,
...
)
model |
Model object |
newdata |
Grid of predictor values at which we evaluate the comparisons.
|
variables |
Focal variables
|
type |
string indicates the type (scale) of the predictions used to
compute contrasts or slopes. This can differ based on the model
type, but will typically be a string such as: "response", "link", "probs",
or "zero". When an unsupported string is entered, the model-specific list of
acceptable values is returned in an error message. When |
vcov |
Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values:
|
by |
Aggregate unit-level estimates (aka, marginalize, average over). Valid inputs:
|
conf_level |
numeric value between 0 and 1. Confidence level to use to build a confidence interval. |
transform_pre |
string or function. How should pairs of adjusted predictions be contrasted?
|
transform_post |
string or function. Transformation applied to unit-level estimates and confidence intervals just before the function returns results. Functions must accept a vector and return a vector of the same length. Support string shortcuts: "exp", "ln" |
cross |
TRUE or FALSE
|
wts |
string or numeric: weights to use when computing average
contrasts or slopes. These weights only affect the averaging in
|
hypothesis |
specify a hypothesis test or custom contrast using a numeric value, vector, or matrix, a string, or a string formula.
|
df |
Degrees of freedom used to compute p values and confidence intervals. A single numeric value between 1 and |
eps |
NULL or numeric value which determines the step size to use when
calculating numerical derivatives: (f(x+eps)-f(x))/eps. When |
... |
Additional arguments are passed to the |
A data.frame
with one row per observation (per term/group) and several columns:
rowid
: row number of the newdata
data frame
type
: prediction type, as defined by the type
argument
group
: (optional) value of the grouped outcome (e.g., categorical outcome models)
term
: the variable whose marginal effect is computed
dydx
: slope of the outcome with respect to the term, for a given combination of predictor values
std.error
: standard errors computed by via the delta method.
See ?print.marginaleffects
for printing options.
avg_comparisons()
: Average comparisons
Standard errors for all quantities estimated by marginaleffects
can be obtained via the delta method. This requires differentiating a function with respect to the coefficients in the model using a finite difference approach. In some models, the delta method standard errors can be sensitive to various aspects of the numeric differentiation strategy, including the step size. By default, the step size is set to 1e-8
, or to 1e-4
times the smallest absolute model coefficient, whichever is largest.
marginaleffects
can delegate numeric differentiation to the numDeriv
package, which allows more flexibility. To do this, users can pass arguments to the numDeriv::jacobian
function through a global option. For example:
options(marginaleffects_numDeriv = list(method = "simple", method.args = list(eps = 1e-6)))
options(marginaleffects_numDeriv = list(method = "Richardson", method.args = list(eps = 1e-5)))
options(marginaleffects_numDeriv = NULL)
See the "Standard Errors and Confidence Intervals" vignette on the marginaleffects
website for more details on the computation of standard errors:
https://vincentarelbundock.github.io/marginaleffects/articles/uncertainty.html
Note that the inferences()
function can be used to compute uncertainty estimates using a bootstrap or simulation-based inference. See the vignette:
https://vincentarelbundock.github.io/marginaleffects/articles/bootstrap.html
Some model types allow model-specific arguments to modify the nature of marginal effects, predictions, marginal means, and contrasts.
Package | Class | Argument | Documentation |
brms | brmsfit | ndraws | brms::posterior_predict |
re_formula | |||
lme4 | merMod | include_random | insight::get_predicted |
re.form | lme4::predict.merMod | ||
allow.new.levels | lme4::predict.merMod | ||
glmmTMB | glmmTMB | re.form | glmmTMB::predict.glmmTMB |
allow.new.levels | glmmTMB::predict.glmmTMB | ||
zitype | glmmTMB::predict.glmmTMB | ||
mgcv | bam | exclude | mgcv::predict.bam |
robustlmm | rlmerMod | re.form | robustlmm::predict.rlmerMod |
allow.new.levels | robustlmm::predict.rlmerMod | ||
MCMCglmm | MCMCglmm | ndraws | |
The following transformations can be applied by supplying one of the shortcut strings to the
transform_pre
argument.
hi
is a vector of adjusted predictions for the "high" side of the
contrast. lo
is a vector of adjusted predictions for the "low" side of the
contrast. y
is a vector of adjusted predictions for the original data. x
is the predictor in the original data. eps
is the step size to use to
compute derivatives and elasticities.
Shortcut | Function |
difference | \(hi, lo) hi - lo |
differenceavg | \(hi, lo) mean(hi) - mean(lo) |
differenceavgwts | \(hi, lo, w) wmean(hi, w) - wmean(lo, w) |
dydx | \(hi, lo, eps) (hi - lo)/eps |
eyex | \(hi, lo, eps, y, x) (hi - lo)/eps * (x/y) |
eydx | \(hi, lo, eps, y, x) ((hi - lo)/eps)/y |
dyex | \(hi, lo, eps, x) ((hi - lo)/eps) * x |
dydxavg | \(hi, lo, eps) mean((hi - lo)/eps) |
eyexavg | \(hi, lo, eps, y, x) mean((hi - lo)/eps * (x/y)) |
eydxavg | \(hi, lo, eps, y, x) mean(((hi - lo)/eps)/y) |
dyexavg | \(hi, lo, eps, x) mean(((hi - lo)/eps) * x) |
dydxavgwts | \(hi, lo, eps, w) wmean((hi - lo)/eps, w) |
eyexavgwts | \(hi, lo, eps, y, x, w) wmean((hi - lo)/eps * (x/y), w) |
eydxavgwts | \(hi, lo, eps, y, x, w) wmean(((hi - lo)/eps)/y, w) |
dyexavgwts | \(hi, lo, eps, x, w) wmean(((hi - lo)/eps) * x, w) |
ratio | \(hi, lo) hi/lo |
ratioavg | \(hi, lo) mean(hi)/mean(lo) |
ratioavgwts | \(hi, lo) wmean(hi)/wmean(lo) |
lnratio | \(hi, lo) log(hi/lo) |
lnratioavg | \(hi, lo) log(mean(hi)/mean(lo)) |
lnratioavgwts | \(hi, lo) log(wmean(hi)/wmean(lo)) |
lnor | \(hi, lo) log((hi/(1 - hi))/(lo/(1 - lo))) |
lnoravg | \(hi, lo) log((mean(hi)/(1 - mean(hi)))/(mean(lo)/(1 - mean(lo)))) |
lnoravgwts | \(hi, lo, w) log((wmean(hi, w)/(1 - wmean(hi, w)))/(wmean(lo, w)/(1 - wmean(lo, w)))) |
expdydx | \(hi, lo, eps) ((exp(hi) - exp(lo))/exp(eps))/eps |
expdydxavg | \(hi, lo, eps) mean(((exp(hi) - exp(lo))/exp(eps))/eps) |
expdydxavgwts | \(hi, lo, eps, w) wmean(((exp(hi) - exp(lo))/exp(eps))/eps, w) |
By default, credible intervals in bayesian models are built as equal-tailed intervals. This can be changed to a highest density interval by setting a global option:
options("marginaleffects_posterior_interval" = "eti")
options("marginaleffects_posterior_interval" = "hdi")
By default, the center of the posterior distribution in bayesian models is identified by the median. Users can use a different summary function by setting a global option:
options("marginaleffects_posterior_center" = "mean")
options("marginaleffects_posterior_center" = "median")
When estimates are averaged using the by
argument, the tidy()
function, or
the summary()
function, the posterior distribution is marginalized twice over.
First, we take the average across units but within each iteration of the
MCMC chain, according to what the user requested in by
argument or
tidy()/summary()
functions. Then, we identify the center of the resulting
posterior using the function supplied to the
"marginaleffects_posterior_center"
option (the median by default).
library(marginaleffects)
# Linear model
tmp <- mtcars
tmp$am <- as.logical(tmp$am)
mod <- lm(mpg ~ am + factor(cyl), tmp)
comparisons(mod, variables = list(cyl = "reference")) |> tidy()
comparisons(mod, variables = list(cyl = "sequential")) |> tidy()
comparisons(mod, variables = list(cyl = "pairwise")) |> tidy()
# GLM with different scale types
mod <- glm(am ~ factor(gear), data = mtcars)
comparisons(mod, type = "response") |> tidy()
comparisons(mod, type = "link") |> tidy()
# Contrasts at the mean
comparisons(mod, newdata = "mean")
# Contrasts between marginal means
comparisons(mod, newdata = "marginalmeans")
# Contrasts at user-specified values
comparisons(mod, newdata = datagrid(am = 0, gear = tmp$gear))
comparisons(mod, newdata = datagrid(am = unique, gear = max))
m <- lm(mpg ~ hp + drat + factor(cyl) + factor(am), data = mtcars)
comparisons(m, variables = "hp", newdata = datagrid(FUN_factor = unique, FUN_numeric = median))
# Numeric contrasts
mod <- lm(mpg ~ hp, data = mtcars)
comparisons(mod, variables = list(hp = 1)) |> tidy()
comparisons(mod, variables = list(hp = 5)) |> tidy()
comparisons(mod, variables = list(hp = c(90, 100))) |> tidy()
comparisons(mod, variables = list(hp = "iqr")) |> tidy()
comparisons(mod, variables = list(hp = "sd")) |> tidy()
comparisons(mod, variables = list(hp = "minmax")) |> tidy()
# using a function to specify a custom difference in one regressor
dat <- mtcars
dat$new_hp <- 49 * (dat$hp - min(dat$hp)) / (max(dat$hp) - min(dat$hp)) + 1
modlog <- lm(mpg ~ log(new_hp) + factor(cyl), data = dat)
fdiff <- \(x) data.frame(x, x + 10)
avg_comparisons(modlog, variables = list(new_hp = fdiff))
# Adjusted Risk Ratio: see the contrasts vignette
mod <- glm(vs ~ mpg, data = mtcars, family = binomial)
avg_comparisons(mod, transform_pre = "lnratioavg", transform_post = exp)
# Adjusted Risk Ratio: Manual specification of the `transform_pre`
avg_comparisons(
mod,
transform_pre = function(hi, lo) log(mean(hi) / mean(lo)),
transform_post = exp)
# cross contrasts
mod <- lm(mpg ~ factor(cyl) * factor(gear) + hp, data = mtcars)
avg_comparisons(mod, variables = c("cyl", "gear"), cross = TRUE)
# variable-specific contrasts
avg_comparisons(mod, variables = list(gear = "sequential", hp = 10))
# hypothesis test: is the `hp` marginal effect at the mean equal to the `drat` marginal effect
mod <- lm(mpg ~ wt + drat, data = mtcars)
comparisons(
mod,
newdata = "mean",
hypothesis = "wt = drat")
# same hypothesis test using row indices
comparisons(
mod,
newdata = "mean",
hypothesis = "b1 - b2 = 0")
# same hypothesis test using numeric vector of weights
comparisons(
mod,
newdata = "mean",
hypothesis = c(1, -1))
# two custom contrasts using a matrix of weights
lc <- matrix(c(
1, -1,
2, 3),
ncol = 2)
comparisons(
mod,
newdata = "mean",
hypothesis = lc)
# `by` argument
mod <- lm(mpg ~ hp * am * vs, data = mtcars)
comparisons(mod, by = TRUE)
mod <- lm(mpg ~ hp * am * vs, data = mtcars)
avg_comparisons(mod, variables = "hp", by = c("vs", "am"))
library(nnet)
mod <- multinom(factor(gear) ~ mpg + am * vs, data = mtcars, trace = FALSE)
by <- data.frame(
group = c("3", "4", "5"),
by = c("3,4", "3,4", "5"))
comparisons(mod, type = "probs", by = by)