[R] genralized linear regression - function glm - number of
Christine SINOQUET
christine.sinoquet at univ-nantes.fr
Thu Nov 18 17:00:38 CET 2010
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.
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
2) why there is no information edited for predictors 5, 7 and 9 ?
Thanks in advance for your kind help.
C.S.
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