On Nov 18, 2010, at 11:00 AM, Christine SINOQUET wrote:
Hello,
Performing a linear regression through the function glm ("yi ~ X$V1
+ X$V2 + X$V3 + X$V4 + X$V5 + X$V6 + X$V7 + X$V8 + X$V9 + X$V10"), I
then edit the information about the coefficients:
print(coefficients(summary(fit)))
I note that the number of coefficients (7) is lower than the number
of predictors (10).
In this case, I work on simulated data for which I forced yi to be a
linear function of the 10 predictors.
What code was used to make the simulation?
intercept: 0.0180752965003802
predictor 1: -0.0111046268531608
predictor 2: -0.0185366138753851
predictor 3: 0.107341157096227
predictor 4: 0.00162924662836275
predictor 5: 0.00162924629403743
predictor 6: -0.0171999854554059
predictor 7: -0.0171999856835917
predictor 8: -0.057207682945982
predictor 9: -0.0171999856239631
predictor 10: 0.134643228957395
"yi ~ X$V1 + X$V2 + X$V3 + X$V4 + X$V5 + X$V6 + X$V7 + X$V8 + X$V9 +
X$V10"
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.018062134 5.624517e-17 3.211322e+14 0
X$V1 -0.011104627 3.084989e-17 -3.599567e+14 0
X$V2 -0.018536614 3.241635e-17 -5.718291e+14 0
X$V3 0.107341157 4.884358e-17 2.197651e+15 0
X$V4 0.003258493 3.286878e-17 9.913643e+13 0
X$V6 -0.051599957 4.203840e-17 -1.227448e+15 0
X$V8 -0.057207683 3.049835e-17 -1.875763e+15 0
X$V10 0.134643229 3.849911e-17 3.497308e+15 0
I am sure to have regressed the right number of variables, since I
check that the formula is correct:
"yi ~ X$V1 + X$V2 + X$V3 + X$V4 + X$V5 + X$V6 + X$V7 + X$V8 + X$V9 +
X$V10"
Could somebody explain to me
1) why there are mismatches between the "true" coefficients for
predictors 4 and 6
and
Your std errors are incredibly small (effectively zero from a
numerical perspective) suggesting you have created a dataset with
extremely small amounts of noise. The coefficients are different (than
expected) because of the answer to the next question.
2) why there is no information edited for predictors 5, 7 and 9 ?
You most likely had each of those set up as a linear combination of
the retained predictors. Collinear variables are dropped and usually
there is a warning, bust since you have not given a console session I
cannot be sure.
--
David Winsemius, MD
West Hartford, CT
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