Prepare data for visualising model selection
model_selection_data.RdThe data preparation function for visualising model selection. The output of this
function can be passed to the model_selection_plot function for
showing a visual comparison between the information criteria for different models.
It is also possible to visualise a breakup of the information criteria into
deviance (goodness-of-fit) and penalty terms for each model.
Arguments
- models
List of statistical regression model objects.
- metric
Metric used for comparisons between models. Takes values from c("AIC", "BIC", "AICc", "BICc", "logLik"). Can choose a single or multiple metrics for comparing the different models.
- sort
A boolean value indicating whether to sort the model from highest to lowest value of chosen metric.
- breakup
A boolean value indicating whether to breakup the metric value into deviance (defined as -2*loglikelihood) and penalty components. Will work only if a single metric out of "AIC", "AICc", "BIC", or "BICc" is chosen to plot.
- model_names
A character string describing the names to display on X-axis for each model in order they appear in the models parameter.
Value
A data-frame with multiple columns containing values of several information criteria for each model specified in `models`.
- model_name
An identifier name for each model object to be shown on X-axis.
- deviance
The deviance values for each model object.
- logLik
The -2*Log-Likelihood values for each model object.
- AIC
The Akaike information criteria (AIC) values for each model object.
- BIC
The Bayesian information criteria (BIC) values for each model object.
- AICc
The corrected AIC (AICc) values for each model object.
- BICc
The corrected BIC (BICc) values for each model object.
- Component
The names of the components to be shown in the plot.
- Value
The values for the components to be shown in the plot.
Examples
## Fit different candidate models
mod1 <- lm(mpg ~ disp, data = mtcars)
mod2 <- lm(mpg ~ disp + hp, data = mtcars)
mod3 <- lm(mpg ~ disp + hp + wt, data = mtcars)
mod4 <- lm(mpg ~ disp + hp + wt + carb, data = mtcars)
## Group models into list
models_list <- list("Model 1" = mod1, "Model 2" = mod2,
"Model 3" = mod3, "Model 4" = mod4)
## Prepare data for visualisation
## Specific metric
model_selection_data(models = models_list,
metric = c("AIC"))
#> # A tibble: 4 × 9
#> model_name deviance logLik AIC BIC AICc BICc Component Value
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
#> 1 Model 1 317. 164. 170. 175. 171. 176. AIC 170.
#> 2 Model 2 283. 161. 169. 174. 170. 177. AIC 169.
#> 3 Model 3 195. 149. 159. 166. 161. 170. AIC 159.
#> 4 Model 4 194. 148. 161. 169. 164. 175. AIC 161.
## Multiple metrics can be plotted together as well
model_selection_data(models = models_list,
metric = c("AIC", "BIC"))
#> # A tibble: 8 × 9
#> model_name deviance logLik AIC BIC AICc BICc Component Value
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
#> 1 Model 1 317. 164. 170. 175. 171. 176. AIC 170.
#> 2 Model 1 317. 164. 170. 175. 171. 176. BIC 175.
#> 3 Model 2 283. 161. 169. 174. 170. 177. AIC 169.
#> 4 Model 2 283. 161. 169. 174. 170. 177. BIC 174.
#> 5 Model 3 195. 149. 159. 166. 161. 170. AIC 159.
#> 6 Model 3 195. 149. 159. 166. 161. 170. BIC 166.
#> 7 Model 4 194. 148. 161. 169. 164. 175. AIC 161.
#> 8 Model 4 194. 148. 161. 169. 164. 175. BIC 169.
## If single metric is specified then breakup of metric
## between goodness of fit and penalty can also be visualised
model_selection_data(models = models_list,
metric = c("AICc"),
breakup = TRUE)
#> # A tibble: 8 × 9
#> model_name deviance logLik AIC BIC AICc BICc Component Value
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 Model 1 317. 164. 170. 175. 171. 176. Goodness of fit 164.
#> 2 Model 1 317. 164. 170. 175. 171. 176. Penalty 6.87
#> 3 Model 2 283. 161. 169. 174. 170. 177. Goodness of fit 161.
#> 4 Model 2 283. 161. 169. 174. 170. 177. Penalty 9.48
#> 5 Model 3 195. 149. 159. 166. 161. 170. Goodness of fit 149.
#> 6 Model 3 195. 149. 159. 166. 161. 170. Penalty 12.3
#> 7 Model 4 194. 148. 161. 169. 164. 175. Goodness of fit 148.
#> 8 Model 4 194. 148. 161. 169. 164. 175. Penalty 15.4
## Sort models
model_selection_data(models = models_list,
metric = c("AICc"),
breakup = TRUE, sort = TRUE)
#> # A tibble: 8 × 9
#> model_name deviance logLik AIC BIC AICc BICc Component Value
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
#> 1 Model 3 195. 149. 159. 166. 161. 170. Goodness of fit 149.
#> 2 Model 3 195. 149. 159. 166. 161. 170. Penalty 12.3
#> 3 Model 4 194. 148. 161. 169. 164. 175. Goodness of fit 148.
#> 4 Model 4 194. 148. 161. 169. 164. 175. Penalty 15.4
#> 5 Model 2 283. 161. 169. 174. 170. 177. Goodness of fit 161.
#> 6 Model 2 283. 161. 169. 174. 170. 177. Penalty 9.48
#> 7 Model 1 317. 164. 170. 175. 171. 176. Goodness of fit 164.
#> 8 Model 1 317. 164. 170. 175. 171. 176. Penalty 6.87
## If multiple metrics are specified then sorting
## will be done on first metric specified in list (AIC in this case)
model_selection_data(models = models_list,
metric = c("AIC", "BIC", "AICc", "BICc"), sort = TRUE)
#> # A tibble: 16 × 9
#> model_name deviance logLik AIC BIC AICc BICc Component Value
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <dbl>
#> 1 Model 3 195. 149. 159. 166. 161. 170. AIC 159.
#> 2 Model 3 195. 149. 159. 166. 161. 170. BIC 166.
#> 3 Model 3 195. 149. 159. 166. 161. 170. AICc 161.
#> 4 Model 3 195. 149. 159. 166. 161. 170. BICc 170.
#> 5 Model 4 194. 148. 161. 169. 164. 175. AIC 161.
#> 6 Model 4 194. 148. 161. 169. 164. 175. BIC 169.
#> 7 Model 4 194. 148. 161. 169. 164. 175. AICc 164.
#> 8 Model 4 194. 148. 161. 169. 164. 175. BICc 175.
#> 9 Model 2 283. 161. 169. 174. 170. 177. AIC 169.
#> 10 Model 2 283. 161. 169. 174. 170. 177. BIC 174.
#> 11 Model 2 283. 161. 169. 174. 170. 177. AICc 170.
#> 12 Model 2 283. 161. 169. 174. 170. 177. BICc 177.
#> 13 Model 1 317. 164. 170. 175. 171. 176. AIC 170.
#> 14 Model 1 317. 164. 170. 175. 171. 176. BIC 175.
#> 15 Model 1 317. 164. 170. 175. 171. 176. AICc 171.
#> 16 Model 1 317. 164. 170. 175. 171. 176. BICc 176.