```
library(marginaleffects)
## simulate data and fit a large model
N <- 1e5
dat <- data.frame(matrix(rnorm(N * 26), ncol = 26))
mod <- lm(X1 ~ ., dat)
results <- bench::mark(
# marginal effects at the mean; no standard error
slopes(mod, vcov = FALSE, newdata = "mean"),
# marginal effects at the mean
slopes(mod, newdata = "mean"),
# 1 variable; no standard error
slopes(mod, vcov = FALSE, variables = "X3"),
# 1 variable
slopes(mod, variables = "X3"),
# 26 variables; no standard error
slopes(mod, vcov = FALSE),
# 26 variables
slopes(mod),
iterations = 1, check = FALSE)
results[, c(1, 3, 5)]
# expression median mem_alloc
# "slopes(mod, vcov = FALSE, newdata = \"mean\")" 194.98ms 306.19MB
# "slopes(mod, newdata = \"mean\")" 345.38ms 311.45MB
# "slopes(mod, vcov = FALSE, variables = \"X3\")" 197.51ms 649.6MB
# "slopes(mod, variables = \"X3\")" 742.05ms 1.27GB
# "slopes(mod, vcov = FALSE)" 4.09s 13.87GB
# "slopes(mod)" 15.33s 26.83GB
```

# 35 Performance

##
35.1 What to do when `marginaleffects`

is slow?

Some options:

- Compute marginal effects and contrasts at the mean (or other representative value) instead of all observed rows of the original dataset: Use the
`newdata`

argument and the`datagrid()`

function. - Compute marginal effects for a subset of variables, paying special attention to exclude factor variables which can be particularly costly to process: Use the
`variables`

argument. - Do not compute standard errors: Use the
`vcov = FALSE`

argument. - Use parallel processing to speed up the computation of standard errors. See next section.

This simulation illustrates how computation time varies for a model with 25 regressors and 100,000 observations:

The benchmarks above were conducted using the development version of `marginaleffects`

on 2023-12-09.

## 35.2 Parallel computation

As noted above, the most costly operation in `marginaleffects`

, because that involves calling `predict()`

at least twice *for every coefficient* in the model. This operation can be conducted in parallel to speed things up.

However, when the dataset is very large, there can be considerable cost to passing it between different cores or forked processes. Unfortunately, this means that the range of cases where parallelization is beneficial is pretty small, and that the gains will generally not be proportional to the number of cores used.

The class of models where parallelization is likely to yield the most gains is where:

- The model includes
*many*parameters (see`get_coef(model)`

) - The data is not very large.

In this example, we use the `future`

package to specify a parallization plan and compute standard errors in parallel. The key parts of that example are: (a) set a global option to tell `marginaleffects`

that we want to compute standard errors in parallel, and (b) use `future`

to specify the parallelization plan and number of workers.

```
library(mgcv)
library(tictoc)
library(future)
library(nycflights13)
library(marginaleffects)
data("flights")
packageVersion("marginaleffects")
cores <- 8
plan(multicore, workers = cores, number_of_workers = 8)
flights <- flights |>
transform(date = as.Date(paste(year, month, day, sep = "/"))) |>
transform(date.num = as.numeric(date - min(date))) |>
transform(wday = as.POSIXlt(date)$wday) |>
transform(time = as.POSIXct(paste(hour, minute, sep = ":"), format = "%H:%M")) |>
transform(time.dt = difftime(time, as.POSIXct('00:00', format = '%H:%M'), units = 'min')) |>
transform(time.num = as.numeric(time.dt)) |>
transform(dep_delay = ifelse(dep_delay < 0, 0, dep_delay)) |>
transform(dep_delay = ifelse(is.na(dep_delay), 0, dep_delay)) |>
transform(carrier = factor(carrier)) |>
transform(dest = factor(dest)) |>
transform(origin = factor(origin))
model <- bam(dep_delay ~ s(date.num, bs = "cr") +
s(wday, bs = "cc", k = 3) +
s(time.num, bs = "cr") +
s(carrier, bs = "re") +
origin +
s(distance, bs = "cr") +
s(dest, bs = "re"),
data = flights,
family = poisson,
discrete = TRUE,
nthreads = cores)
```

Note that this is a good use-case, because the model in question has *a lot* of parameters:

No standard errors is very fast:

```
tic()
p1 <- predictions(model, vcov = FALSE)
toc()
```

With parallelization:

```
options("marginaleffects_parallel" = TRUE)
tic()
p1 <- predictions(model)
toc()
```

Without parallelization:

```
options("marginaleffects_parallel" = FALSE)
tic()
p2 <- predictions(model)
toc()
```

Now we make sure the results are equivalent:

The gains are interesting,

## 35.3 Speed comparison

The `slopes`

function is relatively fast. This simulation was conducted using the development version of the package on 2023-12-09:

`marginaleffects`

can be 3 times faster and use 3 times less memory than `margins`

when unit-level standard errors are *not* computed:

`marginaleffects`

can be up to 1000x times faster and use 32x less memory than `margins`

when unit-level standard errors are computed:

Models estimated on larger datasets (> 1000 observations) can be impossible to process using the `margins`

package, because of memory and time constraints. In contrast, `marginaleffects`

can work well on much larger datasets.

Note that, in some specific cases, `marginaleffects`

will be considerably slower than packages like `emmeans`

or `modmarg`

. This is because these packages make extensive use of hard-coded analytical derivatives, or reimplement their own fast prediction functions.