`stats::lm()`

uses ordinary least squares to fit models with numeric outcomes.

## Details

For this engine, there is a single mode: regression

### Translation from parsnip to the original package

```
linear_reg() %>%
set_engine("lm") %>%
translate()
```

```
## Linear Regression Model Specification (regression)
##
## Computational engine: lm
##
## Model fit template:
## stats::lm(formula = missing_arg(), data = missing_arg(), weights = missing_arg())
```

### Preprocessing requirements

Factor/categorical predictors need to be converted to numeric values
(e.g., dummy or indicator variables) for this engine. When using the
formula method via `fit()`

, parsnip will
convert factor columns to indicators.

### Case weights

This model can utilize case weights during model fitting. To use them,
see the documentation in case_weights and the examples
on `tidymodels.org`

.

The `fit()`

and `fit_xy()`

arguments have arguments called
`case_weights`

that expect vectors of case weights.

*However*, the documentation in `stats::lm()`

assumes
that is specific type of case weights are being used: “Non-NULL weights
can be used to indicate that different observations have different
variances (with the values in weights being inversely proportional to
the variances); or equivalently, when the elements of weights are
positive integers `w_i`

, that each response `y_i`

is the mean of `w_i`

unit-weight observations (including the case that there are w_i
observations equal to `y_i`

and the data have been summarized). However,
in the latter case, notice that within-group variation is not used.
Therefore, the sigma estimate and residual degrees of freedom may be
suboptimal; in the case of replication weights, **even wrong**. Hence,
standard errors and analysis of variance tables should be treated with
care” (emphasis added)

Depending on your application, the degrees of freedown for the model (and other statistics) might be incorrect.

### Saving fitted model objects

This model object contains data that are not required to make predictions. When saving the model for the purpose of prediction, the size of the saved object might be substantially reduced by using functions from the butcher package.

### Examples

The “Fitting and Predicting with parsnip” article contains
examples
for `linear_reg()`

with the `"lm"`

engine.