From: Suresh Krishna <ssk2031_at_columbia.edu>

Date: Thu 31 Mar 2005 - 11:32:57 EST

Hi,

glm(formula = Y ~ X * Fac, family = "binomial", data = mat, subset = sactype < 3 & numstim == 16)

Date: Thu 31 Mar 2005 - 11:32:57 EST

Oops, I corrected some errors in the first paragraph; sorry for the repeated posting.

Suresh

Hi,

I am analyzing a data set with greater than 1000 independent cases

(collected in an unrestricted manner), where each case has 3 variables

associated with it: one, a factor variable with 0/1 levels (called Fac),
another factor variable with 8 levels (X) and a third response variable
with two levels (Y: 0/1). I am trying to see if Fac has an effect on the
relationship between X and the proportion of 1-s in Y.

I have three questions:

- I have never used glm-s for this or any other sort of analysis before today, so am I interpreting the output correctly ?

After setting options(contrasts=c("contr.treatment","contr.poly"))

I did:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Begin R output~~~~~~~~~~~~~~~~~~~~~~Call:

glm(formula = Y ~ X * Fac, family = "binomial", data = mat, subset = sactype < 3 & numstim == 16)

Deviance Residuals:

Min 1Q Median 3Q Max

-2.232 -0.901 0.416 0.985 1.656

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Intercept) 2.405 0.209 11.52 < 2e-16 ***

X2 -2.511 0.293 -8.57 < 2e-16 *** X3 -3.283 0.286 -11.47 < 2e-16 *** X4 -2.009 0.302 -6.65 3e-11 *** X5 -3.098 0.276 -11.22 < 2e-16 *** X6 -2.580 0.288 -8.97 < 2e-16 *** X7 -3.484 0.288 -12.09 < 2e-16 *** X8 -2.811 0.328 -8.56 < 2e-16 *** Fac -1.558 0.721 -2.16 0.03071 * X2:Fac 2.133 0.942 2.26 0.02351 * X3:Fac 1.848 0.932 1.98 0.04748 * X4:Fac 2.836 0.982 2.89 0.00386 ** X5:Fac 3.263 0.945 3.45 0.00056 *** X6:Fac 3.630 0.971 3.74 0.00018 *** X7:Fac 3.256 0.883 3.69 0.00023 *** X8:Fac 3.350 1.000 3.35 0.00081 ***

--- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1Received on Thu Mar 31 11:42:04 2005

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 1619.4 on 1178 degrees of freedom Residual deviance: 1271.2 on 1163 degrees of freedom AIC: 1303 Number of Fisher Scoring iterations: 5 ~~~~~~~~~~~~~~~~~~~~~~~~End R output~~~~~~~~~~~~~~~~~~~~~~~~~~~ I am reading this like this: each of the X2....X8 terms tell me whether the proportions associated with those factors at level 0 of Fac, are different from the proportion associated with factor X1 for level 0 of Fac. And each of the terms associated with Fac (X2:Fac,.......X8:Fac) is telling me whether the difference between X2...X8 and X1 is different for Fac=0 and Fac=1; and this is the same thing as whether the proportion associated with X2......X8 are different for the two levels of Fac. So these X2...X8:Fac terms are like performing a simple 2x2 analysis of the effect of Fac on Y, given X2 (....X8). How much of this is incorrect ? My other two questions are: b) Is this the right way to approach this analysis in R ? Or am I better off reading about multi-way contingency table analyses and using them ? and c) How do I incorporate a correction for multiple-testing into the above analysis ? The effect of Fac on the relationship between X and Y was planned. I would greatly, and respectfully appreciate all pointers, tips and admonitions. Thank you !!!! Suresh ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html

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