This package can be used to conduct *post hoc* analyses of resampling results generated by models.

For example, if two models are evaluated with the root mean squared error (RMSE) using 10-fold cross-validation, there are 10 paired statistics. These can be used to make comparisons between models without involving a test set.

There is a rich literature on the analysis of model resampling results such as McLachlan’s *Discriminant Analysis and Statistical Pattern Recognition* and the references therein. This package follows *the spirit* of Benavoli *et al* (2017).

tidyposterior uses Bayesian generalized linear models for this purpose and can be considered an upgraded version of the `caret::resamples()`

function. The package works with rsample objects natively but any results in a data frame can be used.

See Chapter 11 of *Tidy Models with R* for examples and more details.

## Installation

You can install the released version of tidyposterior from CRAN with:

`install.packages("tidyposterior")`

And the development version from GitHub with:

```
# install.packages("devtools")
devtools::install_github("tidymodels/tidyposterior")
```

## Example

To illustrate, here are some example objects using 10-fold cross-validation for a simple two-class problem:

```
library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 0.2.0 ──
#> ✔ broom 0.8.0 ✔ recipes 0.2.0
#> ✔ dials 1.0.0 ✔ rsample 0.1.1
#> ✔ dplyr 1.0.9 ✔ tibble 3.1.7
#> ✔ ggplot2 3.3.6 ✔ tidyr 1.2.0
#> ✔ infer 1.0.0 ✔ tune 0.2.0
#> ✔ modeldata 0.1.1 ✔ workflows 0.2.6
#> ✔ parsnip 1.0.0 ✔ workflowsets 0.2.1
#> ✔ purrr 0.3.4 ✔ yardstick 1.0.0
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ recipes::step() masks stats::step()
#> • Use suppressPackageStartupMessages() to eliminate package startup messages
library(tidyposterior)
data(two_class_dat, package = "modeldata")
set.seed(100)
folds <- vfold_cv(two_class_dat)
```

We can define two different models (for simplicity, with no tuning parameters).

```
logistic_reg_glm_spec <-
logistic_reg() %>%
set_engine('glm')
mars_earth_spec <-
mars(prod_degree = 1) %>%
set_engine('earth') %>%
set_mode('classification')
```

For tidymodels, the [tune::fit_resamples()] function can be used to estimate performance for each model/resample:

```
rs_ctrl <- control_resamples(save_workflow = TRUE)
logistic_reg_glm_res <-
logistic_reg_glm_spec %>%
fit_resamples(Class ~ ., resamples = folds, control = rs_ctrl)
mars_earth_res <-
mars_earth_spec %>%
fit_resamples(Class ~ ., resamples = folds, control = rs_ctrl)
```

From these, there are several ways to pass the results to the `perf_mod()`

function. The most general approach is to have a data frame with the resampling labels (i.e., one or more id columns) as well as columns for each model that you would like to compare.

For the model results above, [tune::collect_metrics()] can be used along with some basic data manipulation steps:

```
logistic_roc <-
collect_metrics(logistic_reg_glm_res, summarize = FALSE) %>%
dplyr::filter(.metric == "roc_auc") %>%
dplyr::select(id, logistic = .estimate)
mars_roc <-
collect_metrics(mars_earth_res, summarize = FALSE) %>%
dplyr::filter(.metric == "roc_auc") %>%
dplyr::select(id, mars = .estimate)
resamples_df <- full_join(logistic_roc, mars_roc, by = "id")
resamples_df
#> # A tibble: 10 × 3
#> id logistic mars
#> <chr> <dbl> <dbl>
#> 1 Fold01 0.856 0.845
#> 2 Fold02 0.933 0.951
#> 3 Fold03 0.934 0.937
#> 4 Fold04 0.864 0.858
#> 5 Fold05 0.847 0.854
#> 6 Fold06 0.911 0.840
#> 7 Fold07 0.867 0.858
#> 8 Fold08 0.886 0.876
#> 9 Fold09 0.898 0.898
#> 10 Fold10 0.906 0.894
```

We can then give this directly to `perf_mod()`

:

```
set.seed(101)
roc_model_via_df <- perf_mod(resamples_df, iter = 2000)
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 3.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.263937 seconds (Warm-up)
#> Chain 1: 0.086694 seconds (Sampling)
#> Chain 1: 0.350631 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 8e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.278896 seconds (Warm-up)
#> Chain 2: 0.122965 seconds (Sampling)
#> Chain 2: 0.401861 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 6e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.247041 seconds (Warm-up)
#> Chain 3: 0.084765 seconds (Sampling)
#> Chain 3: 0.331806 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 6e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.06 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.259138 seconds (Warm-up)
#> Chain 4: 0.074984 seconds (Sampling)
#> Chain 4: 0.334122 seconds (Total)
#> Chain 4:
```

From this, the posterior distributions for each model can be obtained from the `tidy()`

method:

```
roc_model_via_df %>%
tidy() %>%
ggplot(aes(x = posterior)) +
geom_histogram(bins = 40, col = "blue", fill = "blue", alpha = .4) +
facet_wrap(~ model, ncol = 1) +
xlab("Area Under the ROC Curve")
```

See `contrast_models()`

for how to analyze these distributions

## Contributing

This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.

If you think you have encountered a bug, please submit an issue.

Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.

Check out further details on contributing guidelines for tidymodels packages and how to get help.