Given a set of observed data including a quantitative response variable y and an rstanreg model of y, this function returns 4 measures of the posterior prediction quality. Median absolute prediction error (mae) measures the typical difference between the observed y values and their posterior predictive medians (stable = TRUE) or means (stable = FALSE). Scaled mae (mae_scaled) measures the typical number of absolute deviations (stable = TRUE) or standard deviations (stable = FALSE) that observed y values fall from their predictive medians (stable = TRUE) or means (stable = FALSE). within_50 and within_90 report the proportion of observed y values that fall within their posterior prediction intervals, the probability levels of which are set by the user.
prediction_summary(
model,
data,
prob_inner = 0.5,
prob_outer = 0.95,
stable = FALSE
)an rstanreg model object with quantitative y
data frame including the variables in the model, both response y and predictors x
posterior predictive interval probability (a value between 0 and 1)
posterior predictive interval probability (a value between 0 and 1)
TRUE returns the number of absolute deviations and FALSE returns the standard deviations that observed y values fall from their predictive medians
a tibble
example_data <- data.frame(x = sample(1:100, 20))
example_data$y <- example_data$x*3 + rnorm(20, 0, 5)
example_model <- rstanarm::stan_glm(y ~ x, data = example_data)
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prediction_summary(example_model, example_data, prob_inner = 0.6, prob_outer = 0.80, stable = TRUE)
#> mae mae_scaled within_60 within_80
#> 1 2.23135 0.6607481 0.7 0.85