new_data: Create a data frame from all combinations of predictor values

View source: R/new_data.R

new_dataR Documentation

Create a data frame from all combinations of predictor values


Create a data frame for the "newdata"-argument that contains all combinations of values from the terms in questions. Similar to expand.grid(). The terms-argument accepts all shortcuts for representative values as in ggpredict().


new_data(model, terms, typical = "mean", condition = NULL, ...)

data_grid(model, terms, typical = "mean", condition = NULL, ...)



A fitted model object.


Character vector with the names of those terms from model for which all combinations of values should be created.


Character vector, naming the function to be applied to the covariates over which the effect is "averaged". The default is "mean". See ?sjmisc::typical_value for options.


Named character vector, which indicates covariates that should be held constant at specific values. Unlike typical, which applies a function to the covariates to determine the value that is used to hold these covariates constant, condition can be used to define exact values, for instance condition = c(covariate1 = 20, covariate2 = 5). See 'Examples'.


Currently not used.


A data frame containing one row for each combination of values of the supplied variables.


fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
new_data(fit, c("c12hour [meansd]", "c161sex"))

nd <- new_data(fit, c("c12hour [meansd]", "c161sex"))
pr <- predict(fit, type = "response", newdata = nd)
nd$predicted <- pr

# compare to
ggpredict(fit, c("c12hour [meansd]", "c161sex"))

strengejacke/ggeffects documentation built on March 18, 2023, 10:31 p.m.