get_predict: Get predicted values from a model object (internal function)

Description

Get predicted values from a model object (internal function)

Usage

get_predict(model, newdata, type, ...)
# S3 method for default
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for polr
get_predict(model, newdata = insight::get_data(model), type = "probs", ...)
# S3 method for glmmPQL
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for MCMCglmm
get_predict(model, newdata, type = "response", ndraws = 1000, ...)
# S3 method for afex_aov
get_predict(model, newdata = NULL, ...)
# S3 method for glimML
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for betareg
get_predict(model, newdata, type = "response", ...)
# S3 method for bife
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for biglm
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for multinom
get_predict(model, newdata = insight::get_data(model), type = "probs", ...)
# S3 method for brmultinom
get_predict(model, newdata = insight::get_data(model), type = "probs", ...)
# S3 method for brmsfit
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for crch
get_predict(model, newdata = NULL, type = "location", ...)
# S3 method for bart
get_predict(model, newdata = NULL, ...)
# S3 method for fixest
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for flexsurvreg
get_predict(model, newdata, type, ...)
# S3 method for gamlss
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for glmmTMB
get_predict(
  model,
  newdata = insight::get_data(model),
  type = "response",
  newparams = NULL,
  ...
)
# S3 method for glmgee
get_predict(model, newdata, ...)
# S3 method for merMod
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for lmerModLmerTest
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for lmerMod
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for mblogit
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for mhurdle
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for Learner
get_predict(model, newdata, type = NULL, ...)
# S3 method for clm
get_predict(model, newdata = insight::get_data(model), type = "prob", ...)
# S3 method for rq
get_predict(model, newdata = insight::get_data(model), type = NULL, ...)
# S3 method for rms
get_predict(model, newdata = insight::get_data(model), type = NULL, ...)
# S3 method for orm
get_predict(model, newdata = insight::get_data(model), type = NULL, ...)
# S3 method for lrm
get_predict(model, newdata = insight::get_data(model), type = NULL, ...)
# S3 method for ols
get_predict(model, newdata = insight::get_data(model), type = NULL, ...)
# S3 method for rlmerMod
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for stanreg
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for stpm2
get_predict(model, newdata = NULL, ...)
# S3 method for pstpm2
get_predict(model, newdata = NULL, ...)
# S3 method for gsm
get_predict(model, newdata = NULL, ...)
# S3 method for aft
get_predict(model, newdata = NULL, ...)
# S3 method for lm
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for glm
get_predict(model, newdata = insight::get_data(model), type = "response", ...)
# S3 method for svyolr
get_predict(model, newdata = insight::get_data(model), type = "probs", ...)
# S3 method for svyglm
get_predict(
  model,
  newdata = insight::get_data(model),
  type = "response",
  se.fit = FALSE,
  ...
)
# S3 method for coxph
get_predict(model, newdata = insight::get_data(model), type = "lp", ...)
# S3 method for model_fit
get_predict(model, newdata, type = NULL, ...)
# S3 method for workflow
get_predict(model, newdata, type = NULL, ...)
# S3 method for tobit1
get_predict(model, newdata = insight::get_data(model), type = "response", ...)

Value

A data.frame of predicted values with a number of rows equal to the number of rows in newdata and columns "rowid" and "estimate". A "group" column is added for multivariate models or models with categorical outcomes.

Arguments

model

Model object

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": Slopes evaluated when each predictor is held at its mean or mode.
- "median": Slopes evaluated when each predictor is held at its median or mode.
- "balanced": Slopes evaluated on a balanced grid with every combination of categories and numeric variables held at their means.
- "tukey": Slopes evaluated at Tukey's 5 numbers.
- "grid": Slopes evaluated 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.

...

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.