`cv_error()`

computes the root mean squared error from a model fitted
to kfold cross-validated test-training-data. `cv_compare()`

does the same, for multiple formulas at once (by calling `cv_error()`

for each formula).

```
cv_error(data, formula, k = 5)
cv_compare(data, formulas, k = 5)
```

data | A data frame. |
---|---|

formula | The formula to fit the linear model for the test and training data. |

k | The number of folds for the kfold-crossvalidation. |

formulas | A list of formulas, to fit linear models for the test and training data. |

A data frame with the root mean squared errors for the training and test data.

`cv_error()`

first generates cross-validated test-training pairs, using
`crossv_kfold`

and then fits a linear model, which
is described in `formula`

, to the training data. Then, predictions
for the test data are computed, based on the trained models.
The *training error* is the mean value of the `rmse`

for
all *trained* models; the *test error* is the rmse based on all
residuals from the test data.

```
data(efc)
cv_error(efc, neg_c_7 ~ barthtot + c161sex)
#> Warning: unnest() has a new interface. See ?unnest for details.
#> Try `df %>% unnest(c(predicted, residuals))`, with `mutate()` if needed
#> model train.error test.error
#> 1 neg_c_7 ~ barthtot + c161sex 3.5047 3.5307
cv_compare(efc, formulas = list(
neg_c_7 ~ barthtot + c161sex,
neg_c_7 ~ barthtot + c161sex + e42dep,
neg_c_7 ~ barthtot + c12hour
))
#> Warning: unnest() has a new interface. See ?unnest for details.
#> Try `df %>% unnest(c(predicted, residuals))`, with `mutate()` if needed
#> Warning: unnest() has a new interface. See ?unnest for details.
#> Try `df %>% unnest(c(predicted, residuals))`, with `mutate()` if needed
#> Warning: unnest() has a new interface. See ?unnest for details.
#> Try `df %>% unnest(c(predicted, residuals))`, with `mutate()` if needed
#> model train.error test.error
#> 1 neg_c_7 ~ barthtot + c161sex 3.5069 3.5198
#> 2 neg_c_7 ~ barthtot + c161sex + e42dep 3.4848 3.5157
#> 3 neg_c_7 ~ barthtot + c12hour 3.5031 3.5162
```