# Compute R2s and other performance indices for all your models!

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Indices of model **performance** (*i.e.*, model quality, goodness of fit, predictive accuracy etc.) are very important, both for model *comparison* and model *description* purposes. However, their computation or extraction for a wide variety of models can be complex.

*To address this, please let us introduce the *`performance`

*package!*

## performance

We have recently decided to collaborate around the new **easystats project**, a set of packages designed to make your life *easier* (currently WIP). This project encompasses several packages, devoted for instance to model access or Bayesian analysis, as well as indices of model performance.

The goal of `performance`

is to provide lightweight tools to **assess and check the quality of your model**. It includes functions such as `R2`

for many models (including logistic, mixed and Bayesian models), `ICC`

or helpers to check `convergence`

, `overdipsersion`

or `zero-inflation`

. See the **list of functions** here.

`performance`

can be installed as follows:

install.packages("performance") # Install the package library(performance) # Load it

## Examples

### Mixed Models

First, we calculate the r-squared value and intra-class correlation coefficient (ICC) for a mixed model, using **r2()** and **icc()**. `r2()`

internally calls the appropriate function for the given model. In case of mixed models this will be **r2_nakagawa()**. `r2_nakagawa()`

computes the marginal and conditional r-squared values, while `icc()`

calculates an adjusted and conditional ICC, both based on the proposals from *Nakagawa et al. 2017*. For more details on the computation of the variances, see **get_variance()**.

# Load the lme4 package library(lme4) # Fit a mixed model model <- lmer(Sepal.Width ~ Petal.Length + (1|Species), data = iris) # compute R2, based on Nakagawa et al. 2017 r2(model) > # R2 for mixed models > > Conditional R2: 0.913 > Marginal R2: 0.216 # compute intra-class correlation coefficient (ICC) icc(model) > # Intraclass Correlation Coefficient > > Adjusted ICC: 0.889 > Conditional ICC: 0.697

Now let’s compute **all available** indices of performance appropriate for a given model. This can be done via the **model_performance()**.

# Compute all performance indices model_performance(model) > AIC BIC R2_conditional R2_marginal ICC_adjusted ICC_conditional RMSE > 1 107 119 0.91 0.22 0.89 0.7 0.31

### Bayesian Mixed Models

For Bayesian mixed models, we have the same features available (r-squared, ICC, …). In this example, we focus on the output from `model_performance()`

only.

# Load the rstanarm package library(rstanarm) # Fit a Bayesian mixed model model <- stan_glmer(Sepal.Width ~ Petal.Length + (1|Species), data = iris) # Compute performance indices model_performance(model) > ELPD ELPD_SE LOOIC LOOIC_SE R2_Median R2_MAD R2_Mean R2_SD R2_MAP > 1 -44 10 87 20 0.47 0.045 0.47 0.046 0.47 > R2_CI_low R2_CI_high R2_marginal_Median R2_marginal_MAD R2_marginal_Mean > 1 0.4 0.55 0.27 0.045 0.27 > R2_marginal_SD R2_marginal_MAP R2_marginal_CI_low R2_marginal_CI_high > 1 0.048 0.29 0.19 0.34 > R2_LOO_adjusted > 1 0.45

**Don’t forget to check out the****documentation here****for more!**

More details about `performance`

’s features are comming soon, stay tuned 😉

## Get Involved

There is definitely room for improvement, and some new exciting features are already planned. Feel free to let us know how we could further improve this package!

To conclude, note that *easystats* is a new project in active development. Thus, do not hesitate to contact us if **you want to get involved 🙂**

**Check out our other blog posts**!*here*

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