[R] Systems of equations in glm?
jfox at mcmaster.ca
Thu May 30 23:34:27 CEST 2002
Dear Paul and David,
As far as I'm aware, the closest that you'll come to this model currently
in R is the sem or tsls function in the sem package, both of which assume
quantitative endogenous variables, however.
Several specialized structural-equations programs (e.g., LISREL) will fit
models of this form, with an approach very close to David's suggestion,
based on polychoric and point-polyserial correlations. The calis procedure
in SAS won't do this; I'm not sure about Stata.
I've been thinking about adding this kind of capability to the sem package,
but don't know if I'll do it.
I hope that this helps,
At 06:02 PM 5/30/2002 +0100, David Firth wrote:
>On Thursday, May 30, 2002, Paul Johnson wrote:
>>I have a student that I'm encouraging to use R rather than SAS or Stata
>>and within just 2 weeks he has come up with a question that stumps me.
>>What does a person do about endogeneity in generalized linear models?
>>Suppose Y1 and Y2 are 5 category ordinal dependent variables. I see that
>>MASS has polr for estimation of models like that, as long as they are
>>independent. But what if the models were to be written:
>> Y1.plr <- polr(Y1 ~ Y2 + X1 + X2)
>> Y2.plr <- polr(Y2 ~ Y1 + X3 + X4)
>>Are estimates of the coefficients for Y1 and Y2 biased, as they would be
>>in a linear model? I think yes. Do I need some equivalent of 2SLS or FIML?
>yes and yes, I believe. I presume that you *really* have in mind that Y1
>and Y2 are imperfect (ie, categorized) observations of underlying
>continuous variables (Z1 and Z2, say)? And that the equations whose
>coefficients you'd really like to estimate are (in your R style)
> lm(Z1 ~ Z2 + X1 + X2)
> lm(Z2 ~ Z1 + X1 + X2)
>-- in which case the likelihood, assuming bivariate normality of (Z1,Z2)
>given (X1,X2), involves bivariate normal integrals evaluated over
>rectangles with boundaries determined by category threshold parameters.
>I don't think this (ie, maximization of that likelihood) is programmed at
>present in R. From what you say, I infer that it's not in Stata or SAS either?
>A sensible first analysis might be simply to forget that Y1 and Y2 are
>multinomial, and fit the linear system using some suitable set(s) of
>numeric scores for the categories. Depending on the results, that might
>also be a sensible last analysis...
>>It is not entirely clear to me if, in this example, the input Y1 or Y2 is
>>conceptualized as the 5 point scale or rather if it is thought of as a
>>continuous variable which is observed with error.
>>Is there an email list besides r-help where I should be asking questions
>>like this? I understand it is not strictly R related and would gladly go
>>bother other people than you if you tell me where.
Department of Sociology
Hamilton, Ontario, Canada L8S 4M4
email: jfox at mcmaster.ca
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