it's likely that the weights you're using
aren't the right ones for the
zelig oprobit. you might try working with the survey package with the
svyolr() function to see if you can get better results.
2009/3/23 Kyle Jaros <kajaros at fas.harvard.edu>
Hey folks,
Don't know if anyone else is trying to use the "weights" argument in
Zelig. Sam and I are using weighted survey data to try to estimate an
oprobit model. For one of the models we are estimating, when we run Zelig
with a vector of observation weights entered, we get coefficient estimates
that are very close to our author's, but our standard errors are extremely
small (far smaller than the author's). We aren't sure why this is or how to
correct the problem. For another very similar model, including a vector of
weights just results in the following error message:
zelig.us2 <- zelig(as.factor(jobcomm) ~ demid + repid + hhunion + female
+ nonwhite + income + educ + educinc + age + hhlayoffs + ncentral + south +
west, model="oprobit", weights=starting2$weight, data=starting2)
Error in function (formula, data, weights, start, ..., subset, na.action,
:
attempt to find suitable starting values failed
In addition: Warning messages:
1: In glm.fit(X, y1, wt, family = binomial("probit"), offset = offset) :
algorithm did not converge
2: In glm.fit(X, y1, wt, family = binomial("probit"), offset = offset) :
fitted probabilities numerically 0 or 1 occurred
If anyone has any thoughts as to what might be going on, we would love to
hear them.
Thanks for your help,
Kyle
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Patrick Lam
Department of Government and Institute for Quantitative Social Science,
Harvard University
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