This vignette shows how to add support for new models and add new functionality for supported models.

## Support a new model type

It is very easy to add support for new models in marginaleffects. All we need is to set a global option and define 4 very simple functions.

If you add support for a class of models produced by a CRAN package, please consider submitting your code for inclusion in the package: https://github.com/vincentarelbundock/marginaleffects

If you add support for a class of models produced by a package hosted elsewhere than CRAN, you can submit it for inclusion in the unsupported user-submitted library of extensions: Currently

The rest of this section illustrates how to add support for a very simple lm_manual model.

### Fit function

To begin, we define a function which fits a model. Normally, this function will be supplied by a modeling package published on CRAN. Here, we create a function called lm_manual(), which estimates a linear regression model using simple linear algebra operates:

lm_manual <- function(f, data, ...) {
# design matrix
X <- model.matrix(f, data = data)
# response matrix
Y <- data[[as.character(f[2])]]
# coefficients
b <- solve(crossprod(X)) %*% crossprod(X, Y)
Yhat <- X %*% b
# variance-covariance matrix
e <- Y - Yhat
df <- nrow(X) - ncol(X)
s2 <- sum(e^2) / df
V <- s2 * solve(crossprod(X))
# model object
out <- list(
d = data,
f = f,
X = X,
Y = Y,
V = V,
b = b)
# class name: lm_manual
class(out) <- c("lm_manual", "list")
return(out)
}

Important: The custom fit function must assign a new class name to the object it returns. In the example above, the model is assigned to be of class lm_manual (see the penultimate line of code in the function).

Our new function replicates the results of lm():

model <- lm_manual(mpg ~ hp + drat, data = mtcars)
model$b #> [,1] #> (Intercept) 10.78986122 #> hp -0.05178665 #> drat 4.69815776 model_lm <- lm(mpg ~ hp + drat, data = mtcars) coef(model_lm) #> (Intercept) hp drat #> 10.78986122 -0.05178665 4.69815776 ### marginaleffects extension To extend support in marginaleffects, the first step is to tell the package that our new class is supported. We do this by defining a global option: library(marginaleffects) options("marginaleffects_model_classes" = "lm_manual") Then, we define 4 methods: 1. get_coef() • Mandatory arguments: model, ... • Returns: named vector of parameters (coefficients). 2. set_coef() • Mandatory arguments: model, coefs (named vector of coefficients), ... • Returns: A new model object in which the original coefficients were replaced by the new vector. • Example 3. get_vcov() • Mandatory arguments: model, .... • Optional arguments: vcov • Returns: A named square variance-covariance matrix. 4. get_predict() • Mandatory arguments: model, newdata (data frame), ... • Option arguments: type and other model-specific arguments. • Returns: A data frame with two columns: a unique rowid and a column of estimate values. Note that each of these methods will be named with the suffix .lm_manual to indicate that they should be used whenever marginaleffects needs to process an object of class lm_manual. get_coef.lm_manual <- function(model, ...) { b <- model$b
b <- setNames(as.vector(b), row.names(b))
return(b)
}

set_coef.lm_manual <- function(model, coefs, ...) {
out <- model
out$b <- coefs return(out) } get_vcov.lm_manual <- function(model, ...) { return(model$V)
}

get_predict.lm_manual <- function(model, newdata, ...) {
newX <- model.matrix(model$f, data = newdata) Yhat <- newX %*% model$b
out <- data.frame(
rowid = seq_len(nrow(Yhat)),
estimate = as.vector(Yhat))
return(out)
}

The methods we just defined work as expected:

get_coef(model)
#> (Intercept)          hp        drat
#> 10.78986122 -0.05178665  4.69815776

get_vcov(model)
#>             (Intercept)            hp         drat
#> (Intercept) 25.78356135 -3.054007e-02 -5.836030687
#> hp          -0.03054007  8.635615e-05  0.004969385
#> drat        -5.83603069  4.969385e-03  1.419990359

#>   rowid estimate
#> 1     1 23.41614
#> 2     2 23.41614
#> 3     3 24.06161
#> 4     4 19.56366
#> 5     5 16.52639
#> 6     6 18.31918

Now we can use the avg_slopes function:

avg_slopes(model, newdata = mtcars, variables = c("hp", "drat"))
#>
#>  Term Estimate Std. Error     z Pr(>|z|) 2.5 %  97.5 %
#>  hp    -0.0518    0.00929 -5.57   <0.001 -0.07 -0.0336
#>  drat   4.6982    1.19163  3.94   <0.001  2.36  7.0337
#>
#> Columns: term, estimate, std.error, statistic, p.value, conf.low, conf.high

predictions(model, newdata = mtcars) |> head()
#>
#>  Estimate Std. Error    z Pr(>|z|) 2.5 % 97.5 %  mpg cyl disp  hp drat   wt qsec vs am gear carb
#>      23.4      0.671 34.9   <0.001  22.1   24.7 21.0   6  160 110 3.90 2.62 16.5  0  1    4    4
#>      23.4      0.671 34.9   <0.001  22.1   24.7 21.0   6  160 110 3.90 2.88 17.0  0  1    4    4
#>      24.1      0.720 33.4   <0.001  22.6   25.5 22.8   4  108  93 3.85 2.32 18.6  1  1    4    1
#>      19.6      0.999 19.6   <0.001  17.6   21.5 21.4   6  258 110 3.08 3.21 19.4  1  0    3    1
#>      16.5      0.735 22.5   <0.001  15.1   18.0 18.7   8  360 175 3.15 3.44 17.0  0  0    3    2
#>      18.3      1.343 13.6   <0.001  15.7   21.0 18.1   6  225 105 2.76 3.46 20.2  1  0    3    1
#>
#> Columns: rowid, estimate, std.error, statistic, p.value, conf.low, conf.high, mpg, cyl, disp, hp, drat, wt, qsec, vs, am, gear, carb

Note that, for custom model, we typically have to supply values for the newdata and variables arguments explicitly.

## Modify or extend supported models

Let’s say you want to estimate a model using the mclogit::mblogit function. That package is already supported by marginaleffects, but you want to use a type (scale) of predictions that is not currently supported: a “centered link scale.”

To achieve this, we would need to override the get_predict.mblogit() method. However, it can be unsafe to reassign methods supplied by a package that we loaded with library. To be safe, we assign a new model class to our object (“customclass”) which will inherit from mblogit. Then, we define a get_predict.customclass method to make our new kinds of predictions.

library(mclogit)
library(data.table)

model <- mblogit(
factor(gear) ~ am + mpg,
data = mtcars,
trace = FALSE)

Tell marginaleffects that we are adding support for a new class model models, and assign a new inherited class name to a duplicate of the model object:

options("marginaleffects_model_classes" = "customclass")

model_custom <- model

class(model_custom) <- c("customclass", class(model))

Define a new get_predict.customclass method. We use the default predict() function to obtain predictions. Since this is a multinomial model, predict() returns a matrix of predictions with one column per level of the response variable.

Our new get_predict.customclass method takes this matrix of predictions, modifies it, and reshapes it to return a data frame with three columns: rowid, group, and estimate:

get_predict.customclass <- function(model, newdata, ...) {
out <- predict(model, newdata = newdata, type = "link")
out <- cbind(0, out)
colnames(out)[1] <- dimnames(model$D)[[1]][[1]] out <- out - rowMeans(out) out <- as.data.frame(out) out$rowid <- seq_len(nrow(out))
out <- data.table(out)
out <- melt(
out,
id.vars = "rowid",
value.name = "estimate",
variable.name = "group")
}

Finally, we can call any slopes function and obtain results. Notice that our object of class customclass now produces different results than the default mblogit object:

avg_predictions(model)
#>
#>  Group Estimate Std. Error     z Pr(>|z|)  2.5 % 97.5 %
#>      3    0.469     0.0444 10.56  < 0.001 0.3817  0.556
#>      4    0.375     0.0671  5.59  < 0.001 0.2436  0.506
#>      5    0.156     0.0503  3.11  0.00188 0.0577  0.255
#>
#> Columns: group, estimate, std.error, statistic, p.value, conf.low, conf.high

avg_predictions(model_custom)
#>
#>  Group Estimate Std. Error         z Pr(>|z|) 2.5 % 97.5 %
#>      3    -1.42       2525 -0.000561    1.000 -4950   4947
#>      4     6.36       1779  0.003578    0.997 -3480   3493
#>      5    -4.95       3074 -0.001609    0.999 -6030   6020
#>
#> Columns: group, estimate, std.error, statistic, p.value, conf.low, conf.high