# Make new data for generating predicted data from regression models.

Source:`R/make_new_data.R`

`make_new_data.Rd`

This is a convenience function that helps automate the process
of generating predicted data from regression model from a predictor(s). It
is designed to give you the data frame for the `predict`

method's `newdata`

argument.

## Usage

```
make_new_data(
model,
pred,
pred.values = NULL,
at = NULL,
data = NULL,
center = TRUE,
set.offset = NULL,
num.preds = 100,
...
)
```

## Arguments

- model
The model (e.g.,

`lm`

,`glm`

,`merMod`

,`svyglm`

)- pred
The name of the focal predictor as a string. This is the variable for which, if you are plotting, you'd likely have along the x-axis (with the dependent variable as the y-axis).

- pred.values
The values of

`pred`

you want to include. Default is NULL, which means a sequence of equi-spaced values over the range of a numeric predictor or each level of a non-numeric predictor.- at
If you want to manually set the values of other variables in the model, do so by providing a named list where the names are the variables and the list values are vectors of the values. This can be useful especially when you are exploring interactions or other conditional predictions.

- data
The data frame used in fitting the model. Default is NULL, in which case the data will be retrieved via

`model.frame`

or, if there are variable transformations in the formula, by looking in the environment for the data.- center
Set numeric covariates to their mean? Default is TRUE. You may also just provide a vector of names (as strings) of covariates to center. Note that for

`svyglm`

models, the survey-weighted means are used. For models with weights, these are weighted means.- set.offset
If the model has an offset, the value to use for the offset variable. Default is NULL, in which case the median of the offset variable is used.

- num.preds
The number of predictions to generate. Default is 100. Ignored if

`pred.values`

is not`NULL`

.- ...
Extra arguments passed to

`get_formula()`

## Details

Please bear in mind that this does not generate the predictions. You will
need to do that with a `predict`

function for your model or another
interface, such as the `prediction`

package's titular function.

## Examples

```
fit <- lm(Income ~ Frost + Illiteracy + Murder, data = as.data.frame(state.x77))
# Basic use
new_data <- make_new_data(fit, pred = "Frost")
# Set covariate to specific value
new_data <- make_new_data(fit, pred = "Frost", at = list(Murder = 5))
# Set covariate to several specific values
new_data <- make_new_data(fit, pred = "Frost", at = list(Murder = c(5, 10, 15)))
```