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 predict_response().
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. This argument works in
the same way as the terms argument in predict_response(). See also
this vignette.
Character vector, naming the function to be applied to the
covariates (non-focal terms) over which the effect is "averaged". The
default is "mean". Can be "mean", "weighted.mean", "median", "mode"
or "zero", which call the corresponding R functions (except "mode",
which calls an internal function to compute the most common value); "zero"
simply returns 0. By default, if the covariate is a factor, only "mode" is
applicable; for all other values (including the default, "mean") the
reference level is returned. For character vectors, only the mode is returned.
You can use a named vector to apply different functions to integer, numeric and
categorical covariates, e.g. typical = c(numeric = "median", factor = "mode").
If typical is "weighted.mean", weights from the model are used. If no
weights are available, the function falls back to "mean". Note that this
argument is ignored for predict_response(), because the margin argument
takes care of this.
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.
data(efc, package = "ggeffects")
fit <- lm(barthtot ~ c12hour + neg_c_7 + c161sex + c172code, data = efc)
new_data(fit, c("c12hour [meansd]", "c161sex"))
#> c12hour c161sex neg_c_7 c172code
#> 1 -8.3 1 11.83804 1.970552
#> 2 42.2 1 11.83804 1.970552
#> 3 92.7 1 11.83804 1.970552
#> 4 -8.3 2 11.83804 1.970552
#> 5 42.2 2 11.83804 1.970552
#> 6 92.7 2 11.83804 1.970552
nd <- new_data(fit, c("c12hour [meansd]", "c161sex"))
pr <- predict(fit, type = "response", newdata = nd)
nd$predicted <- pr
nd
#> c12hour c161sex neg_c_7 c172code predicted
#> 1 -8.3 1 11.83804 1.970552 76.75375
#> 2 42.2 1 11.83804 1.970552 63.96204
#> 3 92.7 1 11.83804 1.970552 51.17033
#> 4 -8.3 2 11.83804 1.970552 77.79518
#> 5 42.2 2 11.83804 1.970552 65.00347
#> 6 92.7 2 11.83804 1.970552 52.21175
# compare to
predict_response(fit, c("c12hour [meansd]", "c161sex"))
#> # Predicted values of Total score BARTHEL INDEX
#>
#> c161sex: Male
#>
#> c12hour | Predicted | 95% CI
#> ----------------------------------
#> -8.30 | 76.75 | 73.02, 80.49
#> 42.20 | 63.96 | 60.57, 67.35
#> 92.70 | 51.17 | 47.30, 55.04
#>
#> c161sex: Female
#>
#> c12hour | Predicted | 95% CI
#> ----------------------------------
#> -8.30 | 77.80 | 75.20, 80.39
#> 42.20 | 65.00 | 63.11, 66.90
#> 92.70 | 52.21 | 49.68, 54.74
#>
#> Adjusted for:
#> * neg_c_7 = 11.84
#> * c172code = 1.97