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

 GOF R Documentation

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

### 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

```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

```
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 Feb. 16, 2023, 8:44 p.m.