# R2nls: Goodness of fit for nonlinear regression In OnofriAndreaPG/aomisc: Statistical methods for the agricultural sciences

 R2nls R Documentation

## Goodness of fit for nonlinear regression

### Description

This function calculates measures of goodness of fit for nonlinear regression. It works with both 'nls' and 'drc' objects

### Usage

```R2nls(object)
```

### Arguments

 `object ` A nonlinear regression fit object. It can be either a 'nls' fit or 'drm' fit.

### Value

A list with the following slots:

 `R2 ` Traditional coefficient of determination, calculated as the ratio of model SS to total SS. Formula as in Schabenberger and Pierce, 5.23, pag 211. `PseudoR2 ` Pseudo-R2, more useful for nonlinear regression with no-intercept-models. Formula Formula as in Schabenberger and Pierce, 5.24, pag 212. `R2adj ` Adjusted R2, similar to R2 above, but penalised for higher number of parameters. `MSE ` Mean Squared Error `RMSE ` Root Means Squared Error `RRMSE ` Relative Root Means Squared Error

Andrea Onofri

### References

Schabenberger, O., Pierce, F.J., 2002. Contemporary statistical models for the plant and soil sciences. Taylor & Francis, CRC Press, Books.

### Examples

```data(beetGrowth)
mod3 <- nls(weightInf ~ NLS.L3(DAE, b, c, d), data = beetGrowth)
R2nls(mod3)

mod4 <- drm(weightInf ~ DAE, fct = L.3(), data = beetGrowth)
R2nls(mod4)
```

OnofriAndreaPG/aomisc documentation built on Feb. 2, 2023, 12:13 p.m.