Helper functions for mvgam marginaleffects calculations

Functions needed for working with marginaleffects

Functions needed for getting data / objects with insight

Usage

# S3 method for mvgam
get_coef(model, trend_effects = FALSE, ...)

# S3 method for mvgam
set_coef(model, coefs, trend_effects = FALSE, ...)

# S3 method for mvgam
get_vcov(model, vcov = NULL, ...)

# S3 method for mvgam
get_predict(model, newdata, type = "response", process_error = FALSE, ...)

# S3 method for mvgam
get_data(x, source = "environment", verbose = TRUE, ...)

# S3 method for mvgam_prefit
get_data(x, source = "environment", verbose = TRUE, ...)

# S3 method for mvgam
find_predictors(
  x,
  effects = c("fixed", "random", "all"),
  component = c("all", "conditional", "zi", "zero_inflated", "dispersion", "instruments",
    "correlation", "smooth_terms"),
  flatten = FALSE,
  verbose = TRUE,
  ...
)

# S3 method for mvgam_prefit
find_predictors(
  x,
  effects = c("fixed", "random", "all"),
  component = c("all", "conditional", "zi", "zero_inflated", "dispersion", "instruments",
    "correlation", "smooth_terms"),
  flatten = FALSE,
  verbose = TRUE,
  ...
)

Arguments

model

Model object

trend_effects

logical, extract from the process model component (only applicable if a trend_formula was specified in the model)

...

Additional arguments are passed to the predict() method supplied by the modeling package.These arguments are particularly useful for mixed-effects or bayesian models (see the online vignettes on the marginaleffects website). Available arguments can vary from model to model, depending on the range of supported arguments by each modeling package. See the "Model-Specific Arguments" section of the ?slopes documentation for a non-exhaustive list of available arguments.

coefs

vector of coefficients to insert in the model object

vcov

Type of uncertainty estimates to report (e.g., for robust standard errors). Acceptable values:

FALSE: Do not compute standard errors. This can speed up computation considerably.
TRUE: Unit-level standard errors using the default vcov(model) variance-covariance matrix.
String which indicates the kind of uncertainty estimates to return.
- Heteroskedasticity-consistent: "HC", "HC0", "HC1", "HC2", "HC3", "HC4", "HC4m", "HC5". See ?sandwich::vcovHC
- Heteroskedasticity and autocorrelation consistent: "HAC"
- Mixed-Models degrees of freedom: "satterthwaite", "kenward-roger"
- Other: "NeweyWest", "KernHAC", "OPG". See the sandwich package documentation.
One-sided formula which indicates the name of cluster variables (e.g., ~unit_id). This formula is passed to the cluster argument of the sandwich::vcovCL function.
Square covariance matrix
Function which returns a covariance matrix (e.g., stats::vcov(model))

newdata

Grid of predictor values at which we evaluate the slopes.

Warning: Please avoid modifying your dataset between fitting the model and calling a marginaleffects function. This can sometimes lead to unexpected results.
NULL (default): Unit-level slopes for each observed value in the dataset (empirical distribution). The dataset is retrieved using insight::get_data(), which tries to extract data from the environment. This may produce unexpected results if the original data frame has been altered since fitting the model.
datagrid() call to specify a custom grid of regressors. For example:
- newdata = datagrid(cyl = c(4, 6)): cyl variable equal to 4 and 6 and other regressors fixed at their means or modes.
- See the Examples section and the datagrid() documentation.
subset() call with a single argument to select a subset of the dataset used to fit the model, ex: newdata = subset(treatment == 1)
dplyr::filter() call with a single argument to select a subset of the dataset used to fit the model, ex: newdata = filter(treatment == 1)
string:
- "mean": Marginal Effects at the Mean. Slopes when each predictor is held at its mean or mode.
- "median": Marginal Effects at the Median. Slopes when each predictor is held at its median or mode.
- "marginalmeans": Marginal Effects at Marginal Means. See Details section below.
- "tukey": Marginal Effects at Tukey's 5 numbers.
- "grid": Marginal Effects on a grid of representative numbers (Tukey's 5 numbers and unique values of categorical predictors).

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 type is NULL, the first entry in the error message is used by default.

process_error

logical. If TRUE, uncertainty in the latent process (or trend) model is incorporated in predictions

x

A fitted model.

source

String, indicating from where data should be recovered. If source = "environment" (default), data is recovered from the environment (e.g. if the data is in the workspace). This option is usually the fastest way of getting data and ensures that the original variables used for model fitting are returned. Note that always the current data is recovered from the environment. Hence, if the data was modified after model fitting (e.g., variables were recoded or rows filtered), the returned data may no longer equal the model data. If source = "frame" (or "mf"), the data is taken from the model frame. Any transformed variables are back-transformed, if possible. This option returns the data even if it is not available in the environment, however, in certain edge cases back-transforming to the original data may fail. If source = "environment" fails to recover the data, it tries to extract the data from the model frame; if source = "frame" and data cannot be extracted from the model frame, data will be recovered from the environment. Both ways only returns observations that have no missing data in the variables used for model fitting.

verbose

Toggle messages and warnings.

effects

Should model data for fixed effects ("fixed"), random effects ("random") or both ("all") be returned? Only applies to mixed or gee models.

component

Should all predictor variables, predictor variables for the conditional model, the zero-inflated part of the model, the dispersion term or the instrumental variables be returned? Applies to models with zero-inflated and/or dispersion formula, or to models with instrumental variable (so called fixed-effects regressions). May be abbreviated. Note that the conditional component is also called count or mean component, depending on the model.

flatten

Logical, if TRUE, the values are returned as character vector, not as list. Duplicated values are removed.

Value

Objects suitable for internal 'marginaleffects' functions to proceed. See marginaleffects::get_coef(), marginaleffects::set_coef(), marginaleffects::get_vcov(), marginaleffects::get_predict(), insight::get_data() and insight::find_predictors() for details

Author

Nicholas J Clark