Extending or Modifying marginaleffects

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

• countreg package. Thanks to Olivier Beaumais.

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

get_predict(model, newdata = head(mtcars))
#>   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 -0.05179   0.009293 -5.573 2.5050e-08 -0.070 -0.03357
#>  drat  4.69816   1.191633  3.943 8.0596e-05  2.363  7.03372
#> 
#> Prediction type:  response 
#> Columns: type, 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 %
#>     23.42     0.6711 34.89 < 2.22e-16 22.10  24.73
#>     23.42     0.6711 34.89 < 2.22e-16 22.10  24.73
#>     24.06     0.7204 33.40 < 2.22e-16 22.65  25.47
#>     19.56     0.9988 19.59 < 2.22e-16 17.61  21.52
#>     16.53     0.7354 22.47 < 2.22e-16 15.09  17.97
#>     18.32     1.3433 13.64 < 2.22e-16 15.69  20.95
#> 
#> Prediction type:  response 
#> Columns: rowid, type, 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 preditions.

Load libraries, estimate a model:

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   0.4688    0.04440 10.558 < 2.22e-16 0.38173 0.5558
#>      4   0.3750    0.06764  5.544 2.9505e-08 0.24244 0.5076
#>      5   0.1563    0.05103  3.062  0.0021976 0.05624 0.2563
#> 
#> Prediction type:  response 
#> Columns: type, 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   -1.418       2525 -0.0005614  0.99955 -4950   4947
#>      4    6.365       1779  0.0035776  0.99715 -3480   3493
#>      5   -4.947       3074 -0.0016094  0.99872 -6030   6020
#> 
#> Prediction type:  response 
#> Columns: type, group, estimate, std.error, statistic, p.value, conf.low, conf.high