On Fri, 22 Jan 2010, Jason Morgan wrote:
Hello Jean-Baptiste,
On 2010.01.22 16:32:53, Jean-Baptiste Combes wrote:
Hello,
I am learning R and I am fluent in Stata and I try to translate part of my
Stata code to R to check the reliability of the data under R. I have a
proportion variable as a dependent variable pQSfteHT . Independent variables
are dummies for two categorical variables called dQSvacrateHTQuali3 and
cluster_3. I am fitting a model with the Stata command below:
glm pQSfteHT dQSvacrateHTQuali3_2 dQSvacrateHTQuali3_3 dQSvacrateHTQuali3_4
dQSvacrateHTQuali3_5 cluster_32 cluster_33 cluster_34, link(probit)
family(binomial) robust
and the same (I expect) model with R with the command below:
nurse.model<-glm(pQSfteHT~dQSvacrateHTQuali3_2 + dQSvacrateHTQuali3_3 +
dQSvacrateHTQuali3_4 + dQSvacrateHTQuali3_5 + cluster_32 + cluster_33 +
cluster_34 ,family=binomial(link = "logit"))
I found some differences in the parameters, could it come from the "robust"
option in the Stata command? It sounds strange that a variance option would
lead to changes in parameters estimation but I am not an econometrician.
I noticed this same thing about a year ago when comparing STATA and R
results (though, I was comparing simple linear models). It seems that,
for whatever reason, STATA was reporting slight differences in the
coefficients when applying robust. In R, on the other hand, one
typically gets robust standard errors by applying, e.g., a sandwich
estimator on the variance-covariance matrix of a model previously
estimated. I am not sure what STATA is doing, and I haven't cared enough
to check, but my understanding was also that the estimated coefficients
should not have been affected by rubust (at least in the context of a
strictly linear model).
I haven't seen differences in coefficient estimates with ,robust, and as you note, there
shouldn't be (and this is true generally, not just for linear models). The difference
here is more likely to be that the Stata code estimates a probit model and the R code
estimates a logit model. For probit in R use family=binomial("probit").
-thomas
Thomas Lumley Assoc. Professor, Biostatistics
tlum...@u.washington.edu University of Washington, Seattle
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