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.
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
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 Estat?stica
Universidade Estadual de Londrina
Fone: 3371-4346
About logistic regression
3 messages · Silvano, Eik Vettorazzi, David Winsemius
Hi Silvano, this is FAQ 7.17 http://cran.r-project.org/doc/FAQ/R-FAQ.html#Why-does-the-output-from-anova_0028_0029-depend-on-the-order-of-factors-in-the-model_003f hth. Silvano schrieb:
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.
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
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 Estat?stica
Universidade Estadual de Londrina
Fone: 3371-4346
______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Eik Vettorazzi Institut f?r Medizinische Biometrie und Epidemiologie Universit?tsklinikum Hamburg-Eppendorf Martinistr. 52 20246 Hamburg T ++49/40/7410-58243 F ++49/40/7410-57790
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 Estat?stica > Universidade Estadual de Londrina > Fone: 3371-4346 > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code.