Get predicted values from a model object (internal function)
get_predict(model, newdata, vcov, conf_level, type, ...)# S3 method for default
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for polr
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "probs",
...
)
# S3 method for glmmPQL
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for afex_aov
get_predict(model, newdata = NULL, ...)
# S3 method for glimML
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for betareg
get_predict(model, newdata, ...)
# S3 method for bife
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for biglm
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for multinom
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "probs",
...
)
# S3 method for brmultinom
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "probs",
...
)
# S3 method for brmsfit
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for crch
get_predict(
model,
newdata = NULL,
vcov = FALSE,
conf_level = 0.95,
type = "location",
...
)
# S3 method for fixest
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for gamlss
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for glmmTMB
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for merMod
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for lmerModLmerTest
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for lmerMod
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for mblogit
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for mhurdle
get_predict(
model,
newdata = insight::get_data(model),
vcov = NULL,
conf_level = 0.95,
type = "response",
...
)
# S3 method for mlogit
get_predict(model, newdata, ...)
# S3 method for clm
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for rq
get_predict(
model,
newdata = insight::get_data(model),
vcov = NULL,
conf_level = 0.95,
type = NULL,
...
)
# S3 method for rlmerMod
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for stanreg
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "response",
...
)
# S3 method for coxph
get_predict(
model,
newdata = insight::get_data(model),
vcov = FALSE,
conf_level = 0.95,
type = "lp",
...
)
# S3 method for tobit1
get_predict(
model,
newdata = insight::get_data(model),
vcov = NULL,
conf_level = 0.95,
type = "response",
...
)
A data.frame of predicted values with a number of rows equal to the
number of rows in newdata and columns "rowid" and "predicted". A "group"
column is added for multivariate models or models with categorical outcomes.
Model object
NULL, data frame, string, or datagrid() call. Determines the predictor values for which to compute marginal effects.
NULL (default): Unit-level marginal effects for each observed value in the original dataset.
data frame: Unit-level marginal effects for each row of the newdata data frame.
string:
"mean": Marginal Effects at the Mean. Marginal effects when each predictor is held at its mean or mode.
"median": Marginal Effects at the Median. Marginal effects 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).
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.
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))
numeric value between 0 and 1. Confidence level to use to build a confidence interval.
string indicates the type (scale) of the predictions used to
compute marginal effects or contrasts. 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
default value is used. This default is the first model-related row in
the marginaleffects:::type_dictionary dataframe.
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
?marginaleffects documentation for a non-exhaustive list of available
arguments.