rmse | R Documentation |
Compute the root mean squared error of a greta model
rmse(y, pred, draws, summary = TRUE, probs = c(0.1, 0.9), norm = FALSE)
y |
a greta array, the response variables |
pred |
a greta array, the linear predictor |
draws |
a greta_mcmc_list object, posterior draws as returned from calling greta sampling algorithm (ie mcmc) |
summary |
a logical, if TRUE (default) the function output summary statistics (mean, sd, 80% credible intervals) for the R2, if FALSE the raw values are returned |
probs |
a vector of two numeric specifying the lower and upper limits for the credible intervals (default to 0.1, 0.9), only used if summary=TRUE |
norm |
a logical, whether to normalize the RMSE by the mean of the response variable |
Note that when normalized RMSE (norm=TRUE), is compute as follow: nRMSE = RMSE / mean(y)
If summary=TRUE a 1 x C matrix is returned (C = length(probs) + 2) containing summary statistics of Bayesian R-squared values. If summary = FALSE the posterior samples of the R-squared values are returned as a numeric vector of length S (S is the number of samples)
## Not run: intercept <- normal(0, 1) slope <- normal(0, 1) sd_resid <- cauchy(0, 1, truncation = c(0, 100)) x <- runif(100) y <- as_data(rnorm(100, 1 + 2 * x, 1)) pred <- intercept + slope * x distribution(y) <- normal(pred, sd_resid) m <- model(intercept, slope, sd_resid) drr <- mcmc(m) rmse(y, pred, drr) ## End(Not run)
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