I am applying a logistic regression on the effect of dose, age, PS, menopausal and pairID on the response variable. The data come from a case-control study where controls were matched/paired based on age, PS and menopausal. The pair ID is then recorded as PairID and is also a covariate in the model.
The model is:
mod2 <- glm(response ~ dose + Age + PS + menopausal + PairID,
family = binomial(link = "logit"), data=dat)
The model output shows the estimated coefficient:
summary(mod2)
Deviance Residuals:
Min 1Q Median 3Q Max
-0.89847 -0.36858 -0.05885 -0.02615 3.00978
Coefficients: (2 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) -32.73273 11.42767 -2.864 0.004179 **
MaxDose 0.07984 0.02287 3.491 0.000482 ***
Age 0.62496 0.27268 2.292 0.021910 *
PS1 -0.39054 0.89232 -0.438 0.661628
PS2 17.09581 1268.45566 0.013 0.989247
PS3 15.76341 1833.21516 0.009 0.993139
menopausalpost -12.55850 5.28287 -2.377 0.017444 *
menopausalmale -23.87464 10.51685 -2.270 0.023200 *
PairID2 5.14332 3.02191 1.702 0.088754 .
PairID3 3.60030 2.20299 1.634 0.102200
PairID4 -13.42706 6.47253 -2.074 0.038036 *
PairID5 -0.16041 1.50785 -0.106 0.915276
PairID6 -13.20065 1268.45572 -0.010 0.991697
PairID7 -19.62178 1833.21665 -0.011 0.991460
PairID8 -7.08125 3.55520 -1.992 0.046393 *
PairID9 -0.05779 1.84367 -0.031 0.974994
PairID10 -6.59761 3.44665 -1.914 0.055593 .
PairID11 3.13177 1.55304 2.017 0.043744 *
PairID12 9.04193 4.16930 2.169 0.030106 *
PairID13 15.00443 7.03528 2.133 0.032946 *
PairID14 1.00987 1.58307 0.638 0.523527
PairID15 5.45367 2.91475 1.871 0.061336 .
PairID16 -3.13399 2.18068 -1.437 0.150672
PairID17 6.85244 3.63025 1.888 0.059081 .
PairID18 5.60300 3.02755 1.851 0.064217 .
PairID19 NA NA NA NA
PairID20 -11.53556 5.23541 -2.203 0.027569 *
PairID21 -11.09677 5.23554 -2.120 0.034048 *
PairID22 6.15099 3.16279 1.945 0.051799 .
PairID23 NA NA NA NA
PairID24 -18.62866 1268.45749 -0.015 0.988283
PairID25 4.83989 1.81209 2.671 0.007565 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 226.13 on 540 degrees of freedom
Residual deviance: 167.62 on 511 degrees of freedom
AIC: 227.62
Number of Fisher Scoring iterations: 17
From this output, seems that the covariate menopausal has an significant effect. However, If I apply analysis of deviance to test the significance of each covariate, covariate menopausal shows df=0. Does anyone know why?
drop1(mod2, test = "Chisq")
Single term deletions
Model:
collapsed ~ MaxDose + Age + PS + menopausal + PairID
Df Deviance AIC LRT Pr(>Chi)
<none> 167.62 227.62
MaxDose 1 213.08 271.08 45.462 1.557e-11 ***
Age 1 175.22 233.22 7.602 0.005829 **
PS 3 173.49 227.49 5.868 0.118227
menopausal 0 167.62 227.62 0.000
PairID 22 186.31 202.31 18.684 0.664773
