`varsel_stats`

can be used to obtain summary statistics related to
variable selection. The same statistics can be plotted with
`varsel_plot`

.

varsel_plot(object, nv_max = NULL, stats = "elpd", deltas = F,
alpha = 0.32, baseline = NULL, ...)
varsel_stats(object, nv_max = NULL, stats = "elpd", type = c("mean",
"se"), deltas = F, alpha = 0.32, baseline = NULL, ...)

## Arguments

object |
The object returned by varsel or
cv_varsel. |

nv_max |
Maximum submodel size for which the statistics are calculated. |

stats |
One or several strings determining which statistics to calculate. Available
statistics are:
elpd: (Expected) sum of log predictive densities
mlpd: Mean log predictive density, that is, elpd divided by the number of datapoints.
mse: Mean squared error (gaussian family only)
rmse: Root mean squared error (gaussian family only)
acc/pctcorr: Classification accuracy (binomial family only)
Default is elpd. |

deltas |
If `TRUE` , the submodel statistics are estimated relative to the baseline model
(see argument `baseline` ) instead of estimating the actual values of the statistics.
Defaults to `FALSE` . |

alpha |
A number indicating the desired coverage of the credible
intervals. For example `alpha=0.32` corresponds to 68% probability mass
within the intervals, that is, one standard error intervals. |

baseline |
Either 'ref' or 'best' indicating whether the baseline is the reference model or
the best submodel found. Default is 'ref' when the reference model exists, and 'best' otherwise. |

... |
Currently ignored. |

type |
One or more items from 'mean', 'se', 'lower' and 'upper' indicating which of these to
compute (mean, standard error, and lower and upper credible bounds). The credible bounds are determined so
that `1-alpha` percent of the mass falls between them. |

## Examples

### Usage with stanreg objects
fit <- stan_glm(y~x, binomial())

#> Error in stan_glm(y ~ x, binomial()): could not find function "stan_glm"

#> Error in get_refmodel(fit, ...): object 'fit' not found

varsel_plot(vs)

#> Error in "vsel" %in% class(object): object 'vs' not found

# print out some stats
varsel_stats(vs, stats=c('acc'), type = c('mean','se'))

#> Error in "vsel" %in% class(object): object 'vs' not found