loo_predictive_metric {loo}

Estimate leave-one-out predictive performance..

Description

The loo_predictive_metric() function computes estimates of leave-one-out predictive metrics given a set of predictions and observations. Currently supported metrics are mean absolute error, mean squared error and root mean squared error for continuous predictions and accuracy and balanced accuracy for binary classification. Predictions are passed on to the E_loo() function, so this function assumes that the PSIS approximation is working well.

Usage

loo_predictive_metric(x, ...)

## S3 method for class 'matrix'
loo_predictive_metric(
  x,
  y,
  log_lik,
  ...,
  metric = c("mae", "rmse", "mse", "acc", "balanced_acc"),
  r_eff = 1,
  cores = getOption("mc.cores", 1)
)

Arguments

x

A numeric matrix of predictions.

...

Additional arguments passed on to E_loo()

y

A numeric vector of observations. Length should be equal to the number of rows in x.

log_lik

A matrix of pointwise log-likelihoods. Should be of same dimension as x.

metric

The type of predictive metric to be used. Currently supported options are "mae", "rmse" and "mse" for regression and for binary classification "acc" and "balanced_acc".

"mae"

Mean absolute error.

"mse"

Mean squared error.

"rmse"

Root mean squared error, given by as the square root of MSE.

"acc"

The proportion of predictions indicating the correct outcome.

"balanced_acc"

Balanced accuracy is given by the average of true positive and true negative rates.

r_eff

A Vector of relative effective sample size estimates containing one element per observation. See psis() for more details.

cores

The number of cores to use for parallelization of ⁠[psis()]⁠. See psis() for details.

Value

A list with the following components:

estimate

Estimate of the given metric.

se

Standard error of the estimate.

Examples

if (requireNamespace("rstanarm", quietly = TRUE)) {
# Use rstanarm package to quickly fit a model and get both a log-likelihood
# matrix and draws from the posterior predictive distribution
library("rstanarm")

# data from help("lm")
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
d <- data.frame(
  weight = c(ctl, trt),
  group = gl(2, 10, 20, labels = c("Ctl","Trt"))
)
fit <- stan_glm(weight ~ group, data = d, refresh = 0)
ll <- log_lik(fit)
r_eff <- relative_eff(exp(-ll), chain_id = rep(1:4, each = 1000))

mu_pred <- posterior_epred(fit)
# Leave-one-out mean absolute error of predictions
mae <- loo_predictive_metric(x = mu_pred, y = d$weight, log_lik = ll,
                            pred_error = 'mae', r_eff = r_eff)
# Leave-one-out 90%-quantile of mean absolute error
mae_90q <- loo_predictive_metric(x = mu_pred, y = d$weight, log_lik = ll,
                                pred_error = 'mae', r_eff = r_eff,
                                type = 'quantile', probs = 0.9)
}