[R] question about glm behavior

[Yip]

Hello,

I was trying a glm fitting (as shown below) and I got a warning and a fitted
residual deviance larger than the null deviance. Is this the expected
behavor of glm? I would expect that even though the warning might be
warranted I should not get worse fitting with an additional covariate in the
model. Could anyone tell me what I'm missing?

I get the same results in both R2.5.1 on windows and 2.5.0 on
amd64-pc-linux.

Thank you!

--Yiping

> x1
 [1] -4.320069e-02 -4.574697e-02 -4.091768e-02 -4.405891e-02  1.723491e-02
 [6] -4.534468e-02 -3.514106e-02 -4.115642e-02 -4.384857e-02 -4.098890e-02
[11] -4.423853e-02 -4.320503e-02 -3.949471e-02 -4.438473e-02 -4.328487e-02
[16] -4.100160e-02 -4.238224e-02 -3.961957e-02 -4.343796e-02 -4.375786e-02
[21] -4.291601e-02 -4.363903e-02 -3.944323e-02 -4.492861e-02 -4.314135e-02
[26] -4.407560e-02 -4.262769e-02 -4.446548e-02 -4.332781e-02 -4.330175e-02
[31] -4.533415e-02  3.365331e-01 -4.394758e-02 -4.291074e-02 -4.400895e-02
[36] -4.230687e-02 -4.389724e-02  3.303411e-01 -3.907463e-02  3.147079e-01
[41] -4.174071e-02  2.152082e-01 -4.189925e-02 -4.189424e-02 -3.934256e-02
[46]  3.075004e-01 -4.372070e-02 -4.253140e-02 -3.940970e-02 -4.363639e-02
[51] -4.294994e-02 -2.925661e-02 -2.721621e-02 -4.225099e-02  2.083608e-01
[56] -4.356620e-02 -4.142498e-02 -4.303415e-02 -4.297786e-02  5.919961e-05
[61] -4.367790e-02 -4.370738e-02 -4.025630e-02 -3.830009e-02  3.117086e-01
[66]  3.290152e-01 -2.538811e-02 -4.468090e-02  2.784418e-01  3.511454e-02
[71]  2.913742e-01 -4.387962e-02 -4.268680e-02 -4.171391e-02 -4.202010e-02
[76] -4.322166e-02 -3.706578e-02 -4.390152e-02 -3.587444e-02 -4.384691e-02
[81] -1.480347e-03 -4.389834e-02 -4.185581e-02 -6.265638e-03 -4.202021e-02
[86] -1.808579e-03 -4.264013e-02
> x2
 [1] -0.030178811 -0.030383785 -0.024195005 -0.015303415  0.505774406
 [6] -0.023545322 -0.026341561 -0.031700423 -0.033170361 -0.016807687
[11] -0.030765392 -0.032847977 -0.022241150 -0.030362745 -0.034153581
[16] -0.034486433 -0.026723467 -0.024891104 -0.029309960 -0.028852347
[21] -0.031251711 -0.025874702 -0.025032587 -0.035013137 -0.019088676
[26] -0.021198249 -0.029941158 -0.026721874 -0.023171718 -0.032476985
[31] -0.028476595 -0.048960041 -0.029837101 -0.017454529 -0.026821415
[36] -0.027268238 -0.023836780 -0.065271644 -0.034818157 -0.032746126
[41] -0.025742516 -0.057888806 -0.029212633 -0.022687920 -0.027572481
[46]  0.027638085 -0.030762472 -0.024144720 -0.027526008 -0.025081009
[51] -0.026338116 -0.017626651  0.001067303 -0.028009695 -0.039969065
[56] -0.025602297 -0.027279180 -0.015731185 -0.023141642  0.333521248
[61] -0.025868744 -0.021978049 -0.022180398 -0.010942728  0.021911932
[66] -0.103832477 -0.017300324 -0.024064572 -0.036209915  0.602510894
[71] -0.023094473 -0.024840084 -0.035736611 -0.027606170 -0.019964185
[76] -0.029183955 -0.023701532 -0.027828418 -0.014666741 -0.024613236
[81]  0.284105237 -0.026196040 -0.029447691  0.280181037 -0.027221380
[86]  0.184637507 -0.039031585
> g
 [1] 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 1 0 1 2
1 2
[39] 1 1 2 1 2 1 1 1 0 1 2 1 1 2 2 2 2 1 1 1 1 2 1 2 1 2 1 2 2 1 1 2 2 2 2 2
2 1
[77] 1 2 2 2 2 1 1 2 2 1 1
> f
 [1] 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 1 1 1 1 1 1 0 0 0 0 0 1 1 1 1 1 1 0
1 0
[39] 1 1 0 1 0 1 1 1 1 1 0 1 1 0 0 0 0 1 1 1 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 0
0 1
[77] 1 0 0 0 0 1 1 0 0 1 1
> table(f,g)
   g
f    0  1  2
  0  0  0 44
  1  2 38  3
> glm(f~x1+x2+g, family=binomial(link="logit"), na.action=na.omit)

Call:  glm(formula = f ~ x1 + x2 + g, family = binomial(link = "logit"),     
na.action = na.omit) 

Coefficients:
(Intercept)           x1           x2            g  
  9.184e+15   -4.359e+15    7.889e+14   -6.190e+15  

Degrees of Freedom: 86 Total (i.e. Null);  83 Residual
Null Deviance:      120.6 
Residual Deviance: 216.3        AIC: 224.3 
Warning message:
fitted probabilities numerically 0 or 1 occurred in: glm.fit(x = X, y = Y,
weights = weights, start = start, etastart = etastart,  
> 

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