compare_foot {footBayes} | R Documentation |
Compare Football Models using Various Metrics
Description
Compares multiple football models or directly provided probability matrices based on specified metrics (accuracy, Brier score, ranked probability score, Pseudo R^2
, average coverage probability), using a test dataset. Additionally, computes the confusion matrices. The function returns an object of class compareFoot
.
Usage
compare_foot(
source,
test_data,
metric = c("accuracy", "brier", "ACP", "pseudoR2", "RPS"),
conf_matrix = FALSE
)
Arguments
source |
A named list containing either:
|
test_data |
A data frame containing the test dataset, with columns:
|
metric |
A character vector specifying the metrics to use for comparison. Options are:
Default is |
conf_matrix |
A logical value indicating whether to generate a confusion matrix comparing predicted outcomes against actual outcomes for each model or probability matrix. Default is |
Details
The function extracts predictions from each model or directly uses the provided probability matrices and computes the chosen metrics on the test dataset. It also possible to compute confusion matrices.
Value
An object of class compare_foot_output
, which is a list containing:
-
metrics
: A data frame containing the metric values for each model or probability matrix. -
confusion_matrix
: Confusion matrices for each model or probability matrix.
Author(s)
Roberto Macrì Demartino roberto.macridemartino@phd.unipd.it
Examples
## Not run:
library(dplyr)
data("italy")
italy_2000 <- italy %>%
dplyr::select(Season, home, visitor, hgoal, vgoal) %>%
dplyr::filter(Season == "2000")
colnames(italy_2000) <- c("periods", "home_team", "away_team", "home_goals", "away_goals")
# Example with fitted models
fit_1 <- stan_foot(data = italy_2000,
model = "double_pois", predict = 18) # Double Poisson model
fit_2 <- stan_foot(data = italy_2000,
model = "biv_pois", predict = 18) # Bivariate Poisson model
italy_2000_test <- italy_2000[289:306, ]
compare_results_models <- compare_foot(
source = list(double_poisson = fit_1,
bivariate_poisson = fit_2),
test_data = italy_2000_test,
metric = c("accuracy", "brier", "ACP", "pseudoR2", "RPS"),
conf_matrix = TRUE
)
print(compare_results_models)
## End(Not run)