[R] Stata and R user GLM method

[ONKELINX, Thierry]

Jean-Baptiste,

You are not doing the same thing in R as in Stata. In stata you used the
probit link, in R the logit link.

HTH,

Thierry 


------------------------------------------------------------------------
----
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie & Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium

Research Institute for Nature and Forest
team Biometrics & Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium

tel. + 32 54/436 185
Thierry.Onkelinx op inbo.be
www.inbo.be

To call in the statistician after the experiment is done may be no more
than asking him to perform a post-mortem examination: he may be able to
say what the experiment died of.
~ Sir Ronald Aylmer Fisher

The plural of anecdote is not data.
~ Roger Brinner

The combination of some data and an aching desire for an answer does not
ensure that a reasonable answer can be extracted from a given body of
data.
~ John Tukey

-----Oorspronkelijk bericht-----
Van: r-help-bounces op r-project.org [mailto:r-help-bounces op r-project.org]
Namens Jean-Baptiste Combes
Verzonden: vrijdag 22 januari 2010 15:25
Aan: r-help op r-project.org
Onderwerp: [R] Stata and R user GLM method

Hello people,

I am in the process of migrating from Stata to R and I would like to
check if my results are similar under the two softwares:

Here is my GLM command under R
nurse.model<-glm(pQSfteHT~dQSvacrateHTQuali3_2 + dQSvacrateHTQuali3_3 +
dQSvacrateHTQuali3_4 + dQSvacrateHTQuali3_5 + cluster_32 + cluster_33 +
cluster_34 ,family=binomial(link = "logit"))


and below the stata command
glm pQSfteHT dQSvacrateHTQuali3_2 dQSvacrateHTQuali3_3
dQSvacrateHTQuali3_4
dQSvacrateHTQuali3_5 cluster_32 cluster_33 cluster_34, link(probit)
family(binomial) robust

Apart from the robust option, it seems to me from what I understand that
I should get the same things.
Stata output:



*Second model (N=690*



*Coef.*

*p-value*

Constant**

0.241***

0.000

QV>SV>0

0.076***

0.001

SV>QV>0

0.071**

0.027

QV>SV=0

0.051**

0.019

SV>QV=0

0.042

0.368

Mental Health HTs

-0.226***

0.000

Acute Teaching HTs

0.159***

0.000

Other HTs

0.084

0.200


R output (Sorry for the presentation, but I am not able at the moment to
produce nice tables, the variables are in the same order as above)
Call:
glm(formula = pQSfteHT ~ dQSvacrateHTQuali3_2 + dQSvacrateHTQuali3_3 +
    dQSvacrateHTQuali3_4 + dQSvacrateHTQuali3_5 + cluster_32 +
    cluster_33 + cluster_34, family = binomial(link = "logit"))

Deviance Residuals:
       Min          1Q      Median          3Q         Max
-2.297e+00   2.107e-08   2.107e-08   6.275e-06   3.850e-01

Coefficients:
                       Estimate Std. Error   z value Pr(>|z|)
(Intercept)           4.476e+01  1.950e+04     0.002    0.998
dQSvacrateHTQuali3_2 -1.112e+00  2.136e+04 -5.21e-05    1.000
dQSvacrateHTQuali3_3 -5.365e-01  2.576e+04 -2.08e-05    1.000
dQSvacrateHTQuali3_4 -2.011e+01  1.693e+04    -0.001    0.999
dQSvacrateHTQuali3_5 -6.509e-01  4.040e+04 -1.61e-05    1.000
cluster_32           -3.194e-01  1.788e+04 -1.79e-05    1.000
cluster_33           -2.857e-02  2.475e+04 -1.15e-06    1.000
cluster_34           -2.209e+01  9.666e+03    -0.002    0.998

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 15.0690  on 688  degrees of freedom Residual
deviance:  7.2049  on 681  degrees of freedom
AIC: 23.205

Number of Fisher Scoring iterations: 24



My suggestion is that I have something wrong with my data under R (I am
confident with the Stata results). What do you think? I am not expecting
you to solve my problem as I reckon it is a bit difficult for you as you
do not know the data, I just would like an opinion on the differences
found between the two softwares, do you agree that there is something
wrong?

Thank you for reading this e-mail.

I would like to thank you in advance and alos the people who answered my
previous e-mail that was very kind of you.

Jean-Baptiste

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