R/classification_summary_cv.R
classification_summary_cv.RdGiven a set of observed data including a binary response variable y and an rstanreg model of y, this function returns cross validated estimates of the model's posterior classification quality: sensitivity, specificity, and overall accuracy. For hierarchical models of class lmerMod, the folds are comprised by collections of groups, not individual observations.
classification_summary_cv(model, data, group, k, cutoff = 0.5)an rstanreg model object with binary y
data frame including the variables in the model, both response y (0 or 1) and predictors x
a character string representing the name of the factor grouping variable, ie. random effect (only used for hierarchical models)
the number of folds to use for cross validation
probability cutoff to classify a new case as positive
a list
x <- rnorm(20)
z <- 3*x
prob <- 1/(1+exp(-z))
y <- rbinom(20, 1, prob)
example_data <- data.frame(x = x, y = y)
example_model <- rstanarm::stan_glm(y ~ x, data = example_data, family = binomial)
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classification_summary_cv(model = example_model, data = example_data, k = 2, cutoff = 0.5)
#> $folds
#> fold sensitivity specificity overall_accuracy
#> 1 1 0.8333333 0.75 0.8
#> 2 2 0.8000000 0.80 0.8
#>
#> $cv
#> sensitivity specificity overall_accuracy
#> 1 0.8166667 0.775 0.8
#>