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()`

.

## Usage

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

## Arguments

- model
A fitted model object.

- terms
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`ggpredict()`

. See also this vignette.- typical
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"`

.- condition
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.

## Examples

```
data(efc)
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
ggpredict(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
```