anova {ltm} | R Documentation |
Performs a Likelihood Ratio Test between two nested IRT models.
## S3 method for class 'grm': anova(object, object2, ...) ## S3 method for class 'ltm': anova(object, object2, ...) ## S3 method for class 'rasch': anova(object, object2, ...) ## S3 method for class 'tpm': anova(object, object2, ...)
object |
an object inheriting from either class grm , class ltm , class rasch
or class tpm , representing the model under the null hypothesis. |
object2 |
an object inheriting from either class grm , class ltm , class rasch ,
or class tpm , representing the model under the alternative hypothesis. |
... |
additional arguments; currently none is used. |
An object of either class aov.grm
, class aov.ltm
or class aov.rasch
with components,
nam0 |
the name of object . |
L0 |
the log-likelihood under the null hypothesis (object ). |
aic0 |
the AIC value for the model given by object . |
bic0 |
the BIC value for the model given by object . |
nam1 |
the name of object2 . |
L1 |
the log-likelihood under the alternative hypothesis (object2 ). |
aic1 |
the AIC value for the model given by object2 . |
bic1 |
the BIC value for the model given by object2 . |
LRT |
the value of the Likelihood Ratio Test statistic. |
df |
the degrees of freedom for the test (i.e., the difference in the number of parameters). |
p.value |
the p-value of the test. |
The code does not check if the models are nested! The user is responsible to supply nested models in order the LRT to be valid.
When object2
represents a three parameter model, note that the
null hypothesis in on the boundary of the parameter space for the guessing parameters. Thus, the Chi-squared reference
distribution used by these function might not be totally appropriate.
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
GoF.rasch
,
grm
,
ltm
,
rasch
,
tpm
## LRT between the constrained and unconstrained GRMs ## for the Science data: fit0 <- grm(Science[c(1,3,4,7)], constrained = TRUE) fit1 <- grm(Science[c(1,3,4,7)]) anova(fit0, fit1) ## LRT between the one- and two-factor models ## for the WIRS data: anova(ltm(WIRS ~ z1), ltm(WIRS ~ z1 + z2)) ## An LRT between the Rasch and a constrained ## two-parameter logistic model for the WIRS data: fit0 <- rasch(WIRS) fit1 <- ltm(WIRS ~ z1, constraint = cbind(c(1, 3, 5), 2, 1)) anova(fit0, fit1) ## An LRT between the constrained (discrimination ## parameter equals 1) and the unconstrained Rasch ## model for the LSAT data: fit0 <- rasch(LSAT, constraint = rbind(c(6, 1))) fit1 <- rasch(LSAT) anova(fit0, fit1) ## An LRT between the Rasch and the two-parameter ## logistic model for the LSAT data: anova(rasch(LSAT), ltm(LSAT ~ z1))