I am running glms in R (generalised linear models). I thought I knew pvalues - until I saw that calling up a summary for a glm does not give you an overriding pvalue representative of the model as a whole - at least not in the place where linear models do.
I am wondering if this is given as the pvalue for the Intercept, at the top of the table of coefficients. So in the following example, while Wind.speed..knots and canopy_density may be significant to the model, how do we know whether the model itself is significant? How do I know whether to trust these values? Am I right to wonder that the Pr(>|z|) for (Intercept) represents the significance of the model? Is this model significant folks??? Thanks!
I should note running an F-test will not give a pvalue as I get an error message saying that running F-tests on binomial family is inappropriate.
Call: glm(formula = Empetrum_bin ~ Wind.speed..knots. + canopy_density, family = binomial, data = CAIRNGORM) Deviance Residuals: Min 1Q Median 3Q Max -1.2327 -0.7167 -0.4302 -0.1855 2.3194 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 1.8226 1.2030 1.515 0.1298 Wind.speed..knots. -0.5791 0.2628 -2.203 0.0276 * canopy_density -2.5733 1.1346 -2.268 0.0233 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 59.598 on 58 degrees of freedom Residual deviance: 50.611 on 56 degrees of freedom (1 observation deleted due to missingness) AIC: 56.611