ClarkeTest makes doing tests of non-nested models easy and clear. The
main testing function currently supports models of class
`lm`

, `glm`

(binomial, poisson and negative
binomial), `polr`

, `clm`

, `multinom`

,
`mlogit`

.

The initial code came from the `games`

package
which worked with strategic game models as well as binomial GLMs and
linear models. The impetus for making this package was to extend the
classes of models that could be evaluated.

I re-wrote the function to call generic functions for the individual
log-likelihoods and the number of model parameters. This makes it easy
for others to extend the functionality by writing
`indivLogLiks`

and `nparams`

methods for a new
model class.

- The
`indivLogLiks`

function should take the model object as its only argument and return a vector of the individual log-likelihoods for each observation in the estimation sample. Here is an example for objects of class`clm`

.

```
<- function(model){
indivLogLiks.clm <- predict(model, type="prob")$fit
probs <- log(probs)
ans return(ans)
}
```

- The
`nparams`

function should take the model object as its only argument and return a scalar that gives the number of parameters in the model. Here is an example for models of class`clm`

.

```
<- function(model){
nparams.clm length(coef(model))
}
```

- Additionally, the function uses the
`nobs()`

generic to find the number of observations. If there is no`nobs()`

method for the current model class, the user would have to write one of those, too. Here is an example of the`nobs`

method for`mlogit`

objects.

```
<- function(object, ...){
nobs.mlogit length(object$fitted.values)
}
```

```
# Install release version from CRAN
install.packages("clarkeTest")
# Install development version from GitHub
::install_github("davidaarmstrong/ClarkeTest") remotes
```

Here is an example of how the function works:

```
library(clarkeTest)
data(conflictData)
<- lm(riots ~ log(rgdpna_pc) + log(pop*1000) +
lm1 data=conflictData)
polity2, <- lm(riots ~ rgdpna_pc + pop +
lm2 data=conflictData)
polity2, clarke_test(lm1, lm2)
#>
#> Clarke test for non-nested models
#>
#> Model 1 log-likelihood: -8446
#> Model 2 log-likelihood: -8433
#> Observations: 4381
#> Test statistic: 1830 (42%)
#>
#> Model 2 is preferred (p < 2e-16)
```