Provides Wald test and working Wald and working likelihood ratio (Rao-Scott) test of the
hypothesis that all coefficients associated with a particular
regression term are zero (or have some other specified
values). Particularly useful as a substitute for
when not fitting by maximum likelihood.
regTermTest(model, test.terms, null=NULL,df=NULL, method=c("Wald","WorkingWald","LRT"), lrt.approximation="saddlepoint")
A model object with
Character string or one-sided formula giving name of term or terms to test
Null hypothesis values for parameters. Default is zeros
Denominator degrees of freedom for an F test. If
method for approximating the distribution of
the LRT and Working Wald statistic; see
The Wald test uses a chisquared or F distribution. The two
working-model tests come from the (misspecified) working model where the
observations are independent and the weights are frequency weights. For
categorical data, this is just the model fitted to the estimated
population crosstabulation. The Rao-Scott LRT statistic is the likelihood
ratio statistic in this model. The working Wald test statistic is the Wald statistic
in this model. The working-model tests do not have a chi-squared
sampling distribution: we use a linear combination of chi-squared or F
distributions as in
pchisqsum. I believe the working Wald
test is what SUDAAN refers to as a
"Satterthwaite adjusted Wald test".
To match other software you will typically need to use
An object of class
"LRT" method will not work if the model had starting values supplied for the regression coefficients. Instead, fit the two models separately and use
anova(model1, model2, force=TRUE)
Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contingency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60.
Lumley T, Scott A (2012) "Partial likelihood ratio tests for the Cox model under complex sampling" Statistics in Medicine 17 JUL 2012. DOI: 10.1002/sim.5492
Lumley T, Scott A (2014) "Tests for Regression Models Fitted to Survey Data" Australian and New Zealand Journal of Statistics 56:1-14 DOI: 10.1111/anzs.12065
data(esoph) model1 <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp * alcgp, data = esoph, family = binomial()) anova(model1) regTermTest(model1,"tobgp") regTermTest(model1,"tobgp:alcgp") regTermTest(model1, ~alcgp+tobgp:alcgp) data(api) dclus2<-svydesign(id=~dnum+snum, weights=~pw, data=apiclus2) model2<-svyglm(I(sch.wide=="Yes")~ell+meals+mobility, design=dclus2, family=quasibinomial()) regTermTest(model2, ~ell) regTermTest(model2, ~ell,df=NULL) regTermTest(model2, ~ell, method="LRT", df=Inf) regTermTest(model2, ~ell+meals, method="LRT", df=NULL) regTermTest(model2, ~ell+meals, method="WorkingWald", df=NULL)
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