Analysis of Deviances for Rasch Models
Performs likelihood ratio tests against the model with the largest number of parameters.
## S3 method for class 'eRm' anova(object, ...) ## S3 method for class 'eRm_anova' print(x, ...)
object |
Gives the first object to be tested against others which follow, separated by commata. |
x |
An object of class |
... |
Further models to test with |
The anova method is quite flexible and, as long the used data are identical, every model except the LLRA can be tested against each other.
Regardless of the order that models are specified, they will always be sorted by the number of parameters in decreasing order.
If >= 3 models are passed to the method, all models will be tested against the first model (i.e., the one with the largest amount of parameters).
anova.eRm returns a list object of class eRm_anova containing:
calls |
function calls of the different models (character). |
statistics |
the analysis of deviances table (columns are |
Although, there is a check for identical data matrices used, the models have to be nested for the likelihood ratio test to work. You have to ensure that this is the case, otherwise results will be invalid.
LLRAs cannot be tested with other models (RM, LLTM, RSM, ...); for more information see anova.llra.
Marco J. Maier
### dichotomous data dmod1 <- RM(lltmdat1) dmod2 <- LLTM(lltmdat1, mpoints = 2) anova(dmod1, dmod2) ### polytomous data pmod1 <- RSM(rsmdat) pmod2 <- PCM(rsmdat) anova(pmod1, pmod2) W <- cbind(rep(c(1,0), each=9), rep(c(0,1), each=9)) W pmod3 <- LPCM(rsmdat, W) anova(pmod3, pmod1, pmod2) # note that models are sorted by npar
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