GOF {BayesianTools}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.

Author(s)

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)

[Package BayesianTools version 0.1.7 Index]