R help - Jan 2012 - difference of the multinomial logistic regression results between multinom() function in R and SPSS

Dear all,

I have found some difference of the results between multinom() function in
R and multinomial logistic regression in SPSS software.

The input data, model and parameters are below:

choles <- c(94, 158, 133, 164, 162, 182, 140, 157, 146, 182);
sbp <- c(105, 121, 128, 149, 132, 103, 97, 128, 114, 129);
case <- c(1, 3, 3, 2, 1, 2, 3, 1, 2, 2);

result <- multinom(case ~ choles + sbp + choles:sbp, abstol=1.0e-20,
reltol=1.0e-20, MaxNWts=10000);

However, the estimated coeffcients and standard errors of the coefficeints
are different from the SPSS.

For instance,

the estimated coefficients of the variable "choles" are 0.1946555 and
0.6244513 from the above result, but the SPSS result are  0.213120 and
0.662575.

Standard errors are much more different.

Why these kind of discrepancies occur?

I'll be very appreciated to whom explain the above matter.

Thanks.

Jinho Yoo.

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On Jan 4, 2012, at 8:10 PM, Yoo Jinho wrote:
> Dear all,
>
> I have found some difference of the results between multinom()  
> function in
> R and multinomial logistic regression in SPSS software.
>
> The input data, model and parameters are below:
>
> choles <- c(94, 158, 133, 164, 162, 182, 140, 157, 146, 182);
> sbp <- c(105, 121, 128, 149, 132, 103, 97, 128, 114, 129);
> case <- c(1, 3, 3, 2, 1, 2, 3, 1, 2, 2);
>
> result <- multinom(case ~ choles + sbp + choles:sbp, abstol=1.0e-20,
> reltol=1.0e-20, MaxNWts=10000);
>
> However, the estimated coeffcients and standard errors of the  
> coefficeints
> are different from the SPSS.
>
> For instance,
>
> the estimated coefficients of the variable "choles" are 0.1946555
and
> 0.6244513 from the above result, but the SPSS result are  0.213120 and
> 0.662575.
>
> Standard errors are much more different.
>
> Why these kind of discrepancies occur?
I was taught as a fundamental priciple that interpretation of  
coefficients could not be done until you have both the data and the  
internal coding. You have provided only half of those requirements. So  
back to you.

-- 


David Winsemius, MD
West Hartford, CT

On Jan 5, 2012, at 02:10 , Yoo Jinho wrote:
> Dear all,
> 
> I have found some difference of the results between multinom() function in
> R and multinomial logistic regression in SPSS software.
> 
> The input data, model and parameters are below:
> 
> choles <- c(94, 158, 133, 164, 162, 182, 140, 157, 146, 182);
> sbp <- c(105, 121, 128, 149, 132, 103, 97, 128, 114, 129);
> case <- c(1, 3, 3, 2, 1, 2, 3, 1, 2, 2);
> 
> result <- multinom(case ~ choles + sbp + choles:sbp, abstol=1.0e-20,
> reltol=1.0e-20, MaxNWts=10000);
> 
> However, the estimated coeffcients and standard errors of the coefficeints
> are different from the SPSS.
> 
> For instance,
> 
> the estimated coefficients of the variable "choles" are 0.1946555
and
> 0.6244513 from the above result, but the SPSS result are  0.213120 and
> 0.662575.
> 
> Standard errors are much more different.
> 
> Why these kind of discrepancies occur?
Usually because the parametrizations differ and/or one of the programs (not
always R) has convergence problems.

In the present case, I suspect that you missed the point in ?multinom about
scaling variables on the rhs.
> colSums(fitted(result))
       1        2        3 
2.999702 4.001649 2.998649 

suggests that convergence accuracy is not the greatest (the likelihood equations
should set these equal to the observed counts: 3-4-3).

Also notice that the interaction means that the interpretation of coefficients
for choles are that they are effects for sbp==0, way outside of your data, so
smaller discrepancies may be getting multiplied.

Fuller output from SPSS is needed to say anything more. Is the deviance smaller,
e.g.?



-- 
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Email: pd.mes at cbs.dk  Priv: PDalgd at gmail.com