This function is designed to help calculate average marginal effects (AMEs) from brms models. Currently, only one of at or add can be specified. It is hoped to relax this in the future. TODO add more documentation and technical definitions.

brmsmargins(
object,
at = NULL,
add = NULL,
newdata = model.frame(object),
CI = 0.99,
CIType = "HDI",
contrasts = NULL,
ROPE = NULL,
MID = NULL,
subset = NULL,
dpar = NULL,
seed,
...
)

## Arguments

object A fitted brms model object. Required. An optional object inheriting from data frame indicating the values to hold specific variables at when calculating average predictions. This is intended for AMEs from categorical variables. An optional object inheriting from data frame indicating the values to add to specific variables at when calculating average predictions. This is intended for AMEs for continuous variables. An object inheriting from data frame indicating the baseline values to use for predictions and AMEs. Defaults to be the model frame. A numeric value specifying the width of the credible interval. Defaults to 0.99. A character string specifying the type of credible interval (e.g., highest density interval). It is passed down to bsummary which in turn passes it to ci. Defaults to “HDI”. An optional contrast matrix. The posterior predictions matrix is post multiplied by the contrast matrix, so they must be conformable. The posterior predictions matrix has a separate column for each row in the at or add object, so the contrast matrix should have the same number of rows. It can have multiple columns, if you desire multiple specific contrasts. Either left as NULL, the default, or a numeric vector of length 2, specifying the lower and upper thresholds for the Region of Practical Equivalence (ROPE). Either left as NULL, the default, or a numeric vector of length 2, specifying the lower and upper thresholds for a Minimally Important Difference (MID). Unlike the ROPE, percentages for the MID are calculated as at or exceeding the bounds specified by this argument, whereas the ROPE is the percentage of the posterior at or inside the bounds specified. A character string that is a valid R expression used to subset the dataset passed in newdata, prior to analysis. Defaults to NULL. Parameter passed on the dpar argument of fitted() in brms. Defaults to NULL indicating the mean or location parameter typically. Argument that controls whether (and if so what) random seed to use. This does not matter when using fixed effects only. However, when using Monte Carlo integration to integrate out random effects from mixed effects models, it is critical if you are looking at a continuous marginal effect with some small offset value as otherwise the Monte Carlo error from one set of predictions to another may exceed the true predicted difference. If seed is left missing, the default, than a single, random integer between +\- 1e7 is chosen and used to set the seed before each prediction. If manually chosen (recommended for reproducibility), the seed should either be a single value, in which case this single value is used to set the seed before each prediction. Alternately, it can be a vector of seeds with either the same length as the number of rows in at or add, whichever was specified. This is probably generally not what you want, as it means that even for the same input data, you would get slightly different predictions (when integrating out random effects) due to Monte Carlo variation. Finally, rather than being missing, you can explicitly set seed = NULL, if you do not want any seed to be set. This would be fine, for instance, when only using fixed effects, or if you know what you are doing and intend that behavior when integrating out random effects. Additional arguments passed on to .predict.

## Value

A list. TODO describe more.

## Examples

if (FALSE) {
#### Testing ####
## sample data and logistic model with brms
set.seed(1234)
Tx <- rep(0:1, each = 50)
ybin <- c(rep(0:1, c(40,10)), rep(0:1, c(10,40)))
logitd <- data.frame(Tx = Tx, ybin = ybin)
logitd$x <- rnorm(100, mean = logitd$ybin, sd = 2)

mbin <- brms::brm(ybin ~ Tx + x, data = logitd, family = brms::bernoulli())

summary(mbin)

## predictions + summary
test1 <- brmsmargins:::.predict(mbin,
model.frame(mbin),
summarize = TRUE, posterior = FALSE, dpar = NULL,
resample = 0L)
test1

## check that bootstrapping the sample / population assumed as part of the
## AME indeed increases the uncertainty interval
## TODO: point estimates (M, Mdn) should be based on the actual data
## not the bootstrapped
test2 <- brmsmargins:::.predict(mbin,
model.frame(mbin),
summarize = TRUE, posterior = FALSE, dpar = NULL,
resample = 100L, seed = 1234)
test2

## now check AME for Tx
tmp <- brmsmargins(
object = mbin,
at = data.table::data.table(Tx = 0:1),
contrasts = matrix(c(-1, 1), nrow = 2),
ROPE = c(-.05, +.05),
MID = c(-.10, +.10))

tmp$Summary tmp$ContrastSummary ## Tx AME

## now check AME for Tx with bootstrapping the AME population
tmpalt <- brmsmargins(
object = mbin,
at = data.table::data.table(Tx = 0:1),
contrasts = matrix(c(-1, 1), nrow = 2),
ROPE = c(-.05, +.05),
MID = c(-.10, +.10),
resample = 100L)

tmpalt$Summary tmpalt$ContrastSummary ## Tx AME

## now check AME for continuous predictor, x
## use .01 as an approximation for first derivative
## 1 / .01 in the contrast matrix to get back to a one unit change metric
tmp2 <- brmsmargins(
object = mbin,
add = data.table::data.table(x = c(0, .01)),
contrasts = matrix(c(-1/.01, 1/.01), nrow = 2),
ROPE = c(-.05, +.05),
MID = c(-.10, +.10))

tmp2$ContrastSummary ## x AME if (FALSE) { library(lme4) data(sleepstudy) fit <- brms::brm(Reaction ~ 1 + Days + (1+ Days | Subject), data = sleepstudy, cores = 4) summary(fit, prob = 0.99) tmp <- brmsmargins( object = fit, at = data.table::data.table(Days = 0:1), contrasts = matrix(c(-1, 1), nrow = 2), ROPE = c(-.05, +.05), MID = c(-.10, +.10), CIType = "ETI", effects = "integrateoutRE", k = 5L) tmp$Summary
tmp\$ContrastSummary
}
}