classification_summary_cv {bayesrules} | R Documentation |
Given 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)
model |
an rstanreg model object with binary y |
data |
data frame including the variables in the model, both response y (0 or 1) and predictors x |
group |
a character string representing the name of the factor grouping variable, ie. random effect (only used for hierarchical models) |
k |
the number of folds to use for cross validation |
cutoff |
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) classification_summary_cv(model = example_model, data = example_data, k = 2, cutoff = 0.5)