Empirical Goodnesses of Fit — empirical.GoF • pbcm

Fit models 1 and 2 to data and return the empirical goodnesses of fit as well as the difference in goodness of fit.

empirical.GoF(
  data,
  fun1,
  fun2,
  args1 = NULL,
  args2 = NULL,
  verbose = TRUE,
  GoFname = "GoF"
)

Arguments

data	Data frame
fun1	Modelling function 1
fun2	Modelling function 2
args1	List of arguments passed to `fun1`
args2	List of arguments passed to `fun2`
verbose	If `TRUE`, warnings are printed to the console
GoFname	Name of the element returned by `fun1` and `fun2` holding the goodness of fit

Value

A 1-row data frame of three columns:

GoF1

Goodness of fit for model 1

GoF2

Goodness of fit for model 2

DeltaGoF

Equal to GoF1 - GoF2

Details

Functions fun1 and fun2 must accept data as an argument in addition to any arguments specified in args1 and args2. They must return a list with an element carrying the calculated goodness of fit; by default the name of this element is taken to be the string "GoF" but this behaviour can be changed through the GoFname argument.

Examples

x <- seq(from=0, to=1, length.out=100)
mockdata <- data.frame(x=x, y=x + rnorm(100, 0, 0.5))

myfitfun <- function(data, p) {
  res <- nls(y~a*x^p, data, start=list(a=1.1))
  list(a=coef(res), GoF=deviance(res))
}

empirical.GoF(mockdata, fun1=myfitfun, fun2=myfitfun,
              args1=list(p=1), args2=list(p=2))
#>       GoF1     GoF2  DeltaGoF
#> 1 26.71353 27.77766 -1.064127