I am using the glm function in R to fit robust poisson regression models.
The confidence interval this produces is not consistent with the p-value from the model: confidence intervals that do not overlap 0 have p-values greater than .05. With confint.glm
I obtain this CI: (0.078, 2.480 )(=2*pnorm(z, lower.tail=F))
Call:
glm2(formula = paste("base~", var, sep = ""), family = poisson(link = log),
data = base2)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.8712 -0.8712 -0.8712 0.8347 1.5279
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.0369 0.5772 -3.529 0.000418 ***
augm_alcoolpas augm 1.0680 0.5908 1.808 0.070656 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 138.88 on 188 degrees of freedom
Residual deviance: 134.30 on 187 degrees of freedom
AIC: 270.3
Number of Fisher Scoring iterations: 5
And when I process anova(mod)
I obtain an another pvalue, which is significant (pvalue=0.03)
Analysis of Deviance Table
Model: poisson, link: log
Response: base
Terms added sequentially (first to last)
Df Deviance Resid. Df Resid. Dev Pr(>Chi)
NULL 188 138.88
augm_alcool 1 4.5794 187 134.30 0.03236 *
I understand that pvalue of anova()
is different of summary.glm()
because the p-value in ANOVA is calculated with a chi-square and the p-value in summary.glm with Wald.
I have two questions:
- In summary results, why is the p-value not significant (at the same level as was used to calculate the CI) ?
- What could I perform differently so that inference agrees with the CI?