Hi,
I would like to estimate coefficients using poisson regression and then get
standard errors that are adjusted for heteroskedasticity, using a complex
sample survey data. Then I will calculate prevalence ratio and confidence
intervals.
Can sandwich estimator of variance be used when observations arent
independent? In my case, observations are independent across groups
(clusters), but not necessarily within groups. Can I calculate the standard
errors with robust variance, in complex sample survey data using R?
Outputs:
design_tarv<-svydesign(ids=~X2, strata=~X3, data=banco, weights=~X4)
banco.glm7 <- svyglm(y ~x1, data = banco, family = poisson (link= "log"),
design= design_tarv)
summary(banco.glm7)
Call:
svyglm(y ~ x1, data = banco, family = poisson(link = "log"),
design = design_tarv)
Survey design:
svydesign(ids = ~X2, strata = ~X3, data = banco,
weights = ~X4)
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.91893 0.04696 -19.570 < 2e-16 ***
x1 0.19710 0.06568 3.001 0.00603 **
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
(Dispersion parameter for poisson family taken to be 0.5722583)
Number of Fisher Scoring iterations: 5
library(sandwich)
vcovHC(banco.glm7)
(Intercept) x1
(Intercept) 4.806945e-13 -4.771409e-13
x1 -4.771409e-13 7.127168e-13
sqrt(diag(vcovHC(banco.glm7, type="HC0")))
(Intercept) x1
6.923295e-07 8.426314e-07
# I think this result isnt correct, because standard errors are so small.
Thank you for the help,
Roberta Niquini.
--
ENSP - Fiocruz
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