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strengejacke committed Jul 11, 2024
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6 changes: 3 additions & 3 deletions README.Rmd
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Expand Up @@ -26,7 +26,7 @@ library(performance)
```

[![DOI](https://joss.theoj.org/papers/10.21105/joss.03139/status.svg)](https://doi.org/10.21105/joss.03139)
[![downloads](http://cranlogs.r-pkg.org/badges/performance)](https://cran.r-project.org/package=performance) [![total](https://cranlogs.r-pkg.org/badges/grand-total/performance)](https://cranlogs.r-pkg.org/) [![status](https://tinyverse.netlify.com/badge/performance)](https://CRAN.R-project.org/package=performance)
[![downloads](http://cranlogs.r-pkg.org/badges/performance)](https://cran.r-project.org/package=performance) [![total](https://cranlogs.r-pkg.org/badges/grand-total/performance)](https://cranlogs.r-pkg.org/)

***Test if your model is a good model!***

Expand Down Expand Up @@ -145,7 +145,7 @@ icc(model)
```

...and models of class `brmsfit`.

```{r, echo=FALSE, eval=curl::has_internet()}
model <- insight::download_model("brms_mixed_1")
```
Expand Down Expand Up @@ -224,7 +224,7 @@ model <- lmer(
check_singularity(model)
```

Remedies to cure issues with singular fits can be found [here](https://easystats.github.io/performance/reference/check_singularity.html).
Remedies to cure issues with singular fits can be found [here](https://easystats.github.io/performance/reference/check_singularity.html).

#### Check for heteroskedasticity

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47 changes: 23 additions & 24 deletions README.md
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Expand Up @@ -4,7 +4,6 @@
[![DOI](https://joss.theoj.org/papers/10.21105/joss.03139/status.svg)](https://doi.org/10.21105/joss.03139)
[![downloads](http://cranlogs.r-pkg.org/badges/performance)](https://cran.r-project.org/package=performance)
[![total](https://cranlogs.r-pkg.org/badges/grand-total/performance)](https://cranlogs.r-pkg.org/)
[![status](https://tinyverse.netlify.com/badge/performance)](https://CRAN.R-project.org/package=performance)

***Test if your model is a good model!***

Expand Down Expand Up @@ -58,13 +57,13 @@ To cite performance in publications use:
``` r
citation("performance")
#> To cite package 'performance' in publications use:
#>
#>
#> Lüdecke et al., (2021). performance: An R Package for Assessment, Comparison and
#> Testing of Statistical Models. Journal of Open Source Software, 6(60), 3139.
#> https://doi.org/10.21105/joss.03139
#>
#>
#> A BibTeX entry for LaTeX users is
#>
#>
#> @Article{,
#> title = {{performance}: An {R} Package for Assessment, Comparison and Testing of Statistical Models},
#> author = {Daniel Lüdecke and Mattan S. Ben-Shachar and Indrajeet Patil and Philip Waggoner and Dominique Makowski},
Expand Down Expand Up @@ -146,15 +145,15 @@ model <- stan_glmer(

r2(model)
#> # Bayesian R2 with Compatibility Interval
#>
#>
#> Conditional R2: 0.953 (95% CI [0.942, 0.964])
#> Marginal R2: 0.824 (95% CI [0.720, 0.898])
#> Marginal R2: 0.826 (95% CI [0.724, 0.900])

library(lme4)
model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
r2(model)
#> # R2 for Mixed Models
#>
#>
#> Conditional R2: 0.799
#> Marginal R2: 0.279
```
Expand All @@ -173,7 +172,7 @@ library(lme4)
model <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
icc(model)
#> # Intraclass Correlation Coefficient
#>
#>
#> Adjusted ICC: 0.722
#> Unadjusted ICC: 0.521
```
Expand All @@ -189,9 +188,9 @@ model <- brm(mpg ~ wt + (1 | cyl) + (1 + wt | gear), data = mtcars)
``` r
icc(model)
#> # Intraclass Correlation Coefficient
#>
#> Adjusted ICC: 0.930
#> Unadjusted ICC: 0.771
#>
#> Adjusted ICC: 0.941
#> Unadjusted ICC: 0.779
```

### Model diagnostics
Expand All @@ -210,7 +209,7 @@ data(Salamanders)
model <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
check_overdispersion(model)
#> # Overdispersion test
#>
#>
#> dispersion ratio = 2.946
#> Pearson's Chi-Squared = 1873.710
#> p-value = < 0.001
Expand All @@ -235,7 +234,7 @@ fitted model.
model <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
check_zeroinflation(model)
#> # Check for zero-inflation
#>
#>
#> Observed zeros: 387
#> Predicted zeros: 298
#> Ratio: 0.77
Expand Down Expand Up @@ -323,7 +322,7 @@ be r-squared, AIC, BIC, RMSE, ICC or LOOIC.
m1 <- lm(mpg ~ wt + cyl, data = mtcars)
model_performance(m1)
#> # Indices of model performance
#>
#>
#> AIC | AICc | BIC | R2 | R2 (adj.) | RMSE | Sigma
#> ---------------------------------------------------------------
#> 156.010 | 157.492 | 161.873 | 0.830 | 0.819 | 2.444 | 2.568
Expand All @@ -335,7 +334,7 @@ model_performance(m1)
m2 <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
model_performance(m2)
#> # Indices of model performance
#>
#>
#> AIC | AICc | BIC | Tjur's R2 | RMSE | Sigma | Log_loss | Score_log | Score_spherical | PCP
#> -----------------------------------------------------------------------------------------------------
#> 31.298 | 32.155 | 35.695 | 0.478 | 0.359 | 1.000 | 0.395 | -14.903 | 0.095 | 0.743
Expand All @@ -348,7 +347,7 @@ library(lme4)
m3 <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
model_performance(m3)
#> # Indices of model performance
#>
#>
#> AIC | AICc | BIC | R2 (cond.) | R2 (marg.) | ICC | RMSE | Sigma
#> ----------------------------------------------------------------------------------
#> 1755.628 | 1756.114 | 1774.786 | 0.799 | 0.279 | 0.722 | 23.438 | 25.592
Expand All @@ -368,12 +367,12 @@ m4 <- glm(counts ~ outcome + treatment, family = poisson())

compare_performance(m1, m2, m3, m4, verbose = FALSE)
#> # Comparison of Model Performance Indices
#>
#>
#> Name | Model | AIC (weights) | AICc (weights) | BIC (weights) | RMSE | Sigma | Score_log | Score_spherical | R2 | R2 (adj.) | Tjur's R2 | Log_loss | PCP | R2 (cond.) | R2 (marg.) | ICC | Nagelkerke's R2
#> ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#> m1 | lm | 156.0 (<.001) | 157.5 (<.001) | 161.9 (<.001) | 2.444 | 2.568 | | | 0.830 | 0.819 | | | | | | |
#> m2 | glm | 31.3 (>.999) | 32.2 (>.999) | 35.7 (>.999) | 0.359 | 1.000 | -14.903 | 0.095 | | | 0.478 | 0.395 | 0.743 | | | |
#> m3 | lmerMod | 1764.0 (<.001) | 1764.5 (<.001) | 1783.1 (<.001) | 23.438 | 25.592 | | | | | | | | 0.799 | 0.279 | 0.722 |
#> m1 | lm | 156.0 (<.001) | 157.5 (<.001) | 161.9 (<.001) | 2.444 | 2.568 | | | 0.830 | 0.819 | | | | | | |
#> m2 | glm | 31.3 (>.999) | 32.2 (>.999) | 35.7 (>.999) | 0.359 | 1.000 | -14.903 | 0.095 | | | 0.478 | 0.395 | 0.743 | | | |
#> m3 | lmerMod | 1764.0 (<.001) | 1764.5 (<.001) | 1783.1 (<.001) | 23.438 | 25.592 | | | | | | | | 0.799 | 0.279 | 0.722 |
#> m4 | glm | 56.8 (<.001) | 76.8 (<.001) | 57.7 (<.001) | 3.043 | 1.000 | -2.598 | 0.324 | | | | | | | | | 0.657
```

Expand All @@ -386,7 +385,7 @@ of model performance and sort the models from the best one to the worse.
``` r
compare_performance(m1, m2, m3, m4, rank = TRUE, verbose = FALSE)
#> # Comparison of Model Performance Indices
#>
#>
#> Name | Model | RMSE | Sigma | AIC weights | AICc weights | BIC weights | Performance-Score
#> -----------------------------------------------------------------------------------------------
#> m2 | glm | 0.359 | 1.000 | 1.000 | 1.000 | 1.000 | 100.00%
Expand Down Expand Up @@ -424,20 +423,20 @@ lm4 <- lm(Sepal.Length ~ Species * Sepal.Width + Petal.Length + Petal.Width, dat
test_performance(lm1, lm2, lm3, lm4)
#> Name | Model | BF | Omega2 | p (Omega2) | LR | p (LR)
#> ------------------------------------------------------------
#> lm1 | lm | | | | |
#> lm1 | lm | | | | |
#> lm2 | lm | > 1000 | 0.69 | < .001 | -6.25 | < .001
#> lm3 | lm | > 1000 | 0.36 | < .001 | -3.44 | < .001
#> lm4 | lm | > 1000 | 0.73 | < .001 | -7.77 | < .001
#> Each model is compared to lm1.

test_bf(lm1, lm2, lm3, lm4)
#> Bayes Factors for Model Comparison
#>
#>
#> Model BF
#> [lm2] Species + Petal.Length 3.45e+26
#> [lm3] Species * Sepal.Width 4.69e+07
#> [lm4] Species * Sepal.Width + Petal.Length + Petal.Width 7.58e+29
#>
#>
#> * Against Denominator: [lm1] Species
#> * Bayes Factor Type: BIC approximation
```
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