Significance testing for the coefficients of Quasi binomial or the quasi Poisson regression.
An object as returned by the "prop.reg" or the "qpois.reg" function.
If you want to test the significance of a single coefficient this must be a number. In this case, the "prop.reg" or the "qpois.reg" function contains this information. If you want more coefficients to be testes simultaneously, e.g. for a categorical predictor, then this must contain the positions of the coefficients. If you want to see if all coefficients are zero, like an overall F-test, leave this NULL.
Even though the name of this function starts with anova it is not an ANOVA type significance testing, but a Wald type.
A vector with three elements, the test statistic value, its associated p-value and the relevant degrees of freedom.
R implementation and documentation: Michail Tsagris <email@example.com> and Manos Papadakis <firstname.lastname@example.org>.
Papke L. E. & Wooldridge J. (1996). Econometric methods for fractional response variables with an application to 401(K) plan participation rates. Journal of Applied Econometrics, 11(6): 619-632.
McCullagh, Peter, and John A. Nelder. Generalized linear models. CRC press, USA, 2nd edition, 1989.
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## Not run: y <- rbeta(1000, 1, 4) x <- matrix(rnorm(1000 * 3), ncol = 3) a <- prop.reg(y, x) ## all coefficients are tested res<-anova_propreg(a) ## the first predictor variable is tested res<-anova_propreg(a, 2) a ## this information is already included in the model output ## the first and the second predictor variables are tested res<-anova_propreg(a, 2:3) ## End(Not run)
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