View source: R/LogisticRegression.R

cv.lr | R Documentation |

Implementation of cross-validation for a `lr`

object, calculation of error across
a number of subsets of the inputted data set.

```
cv.lr(
lrfit,
metric = "mse",
leave_out = nrow(lrfit$data)/10,
verbose = TRUE,
seed = 1
)
```

`lrfit` |
an object of class " |

`metric` |
which metric to calculate, one of "mse", "auc" or "both". See 'Details'. |

`leave_out` |
number of points to leave out for cross-validation. |

`verbose` |
logical; whether to print information about number of iterations completed. |

`seed` |
optional; number to be passed to |

`k`

-fold cross-validation, where `k`

is the input to the `leave_out`

argument.
This can be used to judge the out-of-sample predictive power of the model by subsetting the original
data set into two partitions; fitting the model for the (usually larger) one, and testing the
predictions of that model on the (usually smaller) partition. The position of the `k`

points separated from the data set are selected uniformly at random.

The error metrics available are that of mean squared error, AUC, or log score; selected by the `metric`

argument being one of "mse", "auc", "log" or "all". See `roc.lr`

for details on AUC.
If `metric`

is "all", then a vector will be output containing all three metrics.

Note that the output from `metric = "auc"`

has non-deterministic elements due to the shuffling
of the data set. To mitigate this, include a number to the `seed`

argument.

error value or vector consisting of the average of the chosen `metric`

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