View source: R/anova_related.R

Significance testing for the coefficients of Quasi binomial or the quasi Poisson regression | R Documentation |

Significance testing for the coefficients of Quasi binomial or the quasi Poisson regression.

anova_propreg(mod, poia = NULL) anova_qpois.reg(mod, poia = NULL)

`mod` |
An object as returned by the "prop.reg" or the "qpois.reg" function. |

`poia` |
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.

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@uoc.gr> and Manos Papadakis <papadakm95@gmail.com>.

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.

```
prop.reg, qpois.reg, univglms, score.glms, logistic_only
```

## 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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