Hi everybody,
I would like to estimate prevalence ratio and confidence intervals.
I tried to do a log-binomial regression, but there was a failure of
convergence.
Now, I would like to learn how to do a poisson regression with robust
variance.
I am trying to estimate coefficients with poisson regression and then get
standard errors that are adjusted for heteroskedasticity.
glm22<- svyglm(y~x1+x2+x3+offset(log(x4)), data = banco, family = poisson,
design= design_tarv)
# Y has a binomial distribution (0/1)
# X1, X2, X3 e X4 are categorical variables.
#I am using the library(survey) because it is an analysis of Complex Sample
Survey Data .
summary(glm22)
Call:
svyglm(y~x1+x2+x3+ offset(log(x4)),data = banco, family = poisson, design =
design_tarv)
Survey design:
svydesign(ids = ~conglomerado, strata = ~estrato, data = banco,
weights = ~peso)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.61224 0.07223 -77.699 < 2e-16 ***
x1 0.33847 0.07428 4.557 0.000155 ***
x2 0.17745 0.07059 2.514 0.019765 *
x3 0.33508 0.09447 3.547 0.001808 **
x4 0.24382 0.08808 2.768 0.011217 *
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
(Dispersion parameter for poisson family taken to be 0.7535822)
Number of Fisher Scoring iterations: 5
# Using family=quasipoisson, I found the same values.
library(sandwich)
vcovHAC(glm22)
(Intercept) x1 x2 x3 x4
(Intercept)1.060857e-12-1.306035e-13-5.139155e-13 -9.788354e-13 -3.428080e-13
x1 -1.306035e-13 7.237868e-13 -3.263182e-13 -1.620593e-13 1.704392e-13
x2 -5.139155e-13 -3.263182e-13 1.250564e-12 7.207572e-13 -9.350062e-13
x3 -9.788354e-13 -1.620593e-13 7.207572e-13 1.707176e-12 -2.244859e-13
x4 -3.428080e-13 1.704392e-13 -9.350062e-13 -2.244859e-13 2.031640e-12
sqrt(diag(vcovHAC(glm22)))
(Intercept) x1 x2 x3 x4
1.029979e-06 8.507566e-07 1.118286e-06 1.306589e-06 1.425356e-06
I think these standards errors are very small.
Is this the correct form to do poisson regression with robust variance?
Thank you for the help,
Roberta.
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