# rmse: compute the (normalized) root mean squared error for a greta... In lionel68/greta.checks: Perform Checks on greta Models

## Description

Compute the root mean squared error of a greta model

## Usage

 `1` ```rmse(y, pred, draws, summary = TRUE, probs = c(0.1, 0.9), norm = FALSE) ```

## Arguments

 `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

## Details

Note that when normalized RMSE (norm=TRUE), is compute as follow: nRMSE = RMSE / mean(y)

## Value

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)

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```## 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) ```

lionel68/greta.checks documentation built on April 30, 2020, 7:10 p.m.