# GOF: Standard GOF metrics Startvalues for sampling with nrChains >... In BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

## Description

Standard GOF metrics Startvalues for sampling with nrChains > 1 : if you want to provide different start values for the different chains, provide a list

## Usage

 `1` ```GOF(observed, predicted, plot = F, centered = T) ```

## Arguments

 `observed` observed values `predicted` predicted values `plot` should a plot be created `centered` if T, variables are centered to the mean of the observations, i.e. the intercept is for the mean value of the observation

## Details

The function considers observed ~ predicted and calculates

1) rmse = root mean squared error 2) mae = mean absolute errorr 3) a linear regression with slope, intercept and coefficient of determination R2

For the linear regression, centered = T means that variables will be centered around the mean value of the observation. This setting avoids a correlation between slope and intercept (that the intercept is != 0 as soon as the slope is !=0)

## Value

A list with the following entries: rmse = root mean squared error, mae = mean absolute error, slope = slope of regression, offset = intercept of regression, R2 = R2 of regression

## Note

In principle, it is possible to plot observed ~ predicted and predicted ~ observed. However, if we assume that the error is mainly on the y axis (observations), i.e. that observations scatter around the true (ideal) value, we should plot observed ~ predicted. See Pineiro et al. (2008). How to evaluate models: observed vs. predicted or predicted vs. observed?. Ecological Modelling, 216(3-4), 316-322.

Florian Hartig

## Examples

 ```1 2 3 4 5 6 7 8``` ```x = runif(500,-1,1) y = 0.2 + 0.9 *x + rnorm(500, sd = 0.5) summary(lm(y ~ x)) GOF(x,y) GOF(x,y, plot = TRUE) ```

BayesianTools documentation built on Dec. 10, 2019, 1:08 a.m.