On Apr 1, 2010, at 8:19 AM, Silvano wrote:
Hi,
I have a dichotomous variable (Q1) whose answers are Yes or No.
Also I have 2 categorical explanatory variables (V1 and V2) with two
levels each.
I used logistic regression to determine whether there is an effect
of V1, V2 or an interaction between them.
I used the R and SAS, just for the conference. It happens that there
is disagreement about the effect of the explanatory variables
between the two softwares.
Not really. You are incorrectly interpreting what SAS is reporting to
you, although in your defense I think it is SAS's fault, and that what
SA is reproting is nonsensical.
R:
q1 = glm(Q1~grau*genero, family=binomial, data=dados)
anova(q1, test="Chisq")
Df Deviance Resid. Df Resid. Dev P(>|Chi|)
NULL 202 277.82
grau 1 4.3537 201 273.46 0.03693 *
genero 1 1.4775 200 271.99 0.22417
grau:genero 1 0.0001 199 271.99 0.99031
SAS:
proc logistic data=psico;
class genero (param=ref ref='0') grau (param=ref ref='0');
model Q1 = grau genero grau*genero / expb;
run;
Type 3 Analysis of Effects
Wald
Effect DF Chi-Square Pr > ChiSq
grau 1 1.6835 0.1945
genero 1 0.7789 0.3775
genero*grau 1 0.0002 0.9902
I'm having difficulty figuring our how "type 3" analysis makes any
sense in this situation. Remember that "type 3" analysis supposedly
gives you an estimate for a covariate that is independent of its order
of entry. How could you sensible be adding either of those "main
effects" terms to a model that already had the interaction and the
other covariate in it already? The nested model perspective offered by
R seems much more sensible.
--
David
The parameters estimates are the same for both.
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.191055 0.310016 0.616 0.538
grau 0.562717 0.433615 1.298 0.194
genero -0.355358 0.402650 -0.883 0.377
grau:genero 0.007052 0.580837 0.012 0.990
What am I doing wrong?
Thanks,
--------------------------------------
Silvano Cesar da Costa
Departamento de EstatÃstica
Universidade Estadual de Londrina
Fone: 3371-4346
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