I get enormous coefficients during logistic regression, see coefficients with krajULKV:
> summary(m5)
Call:
glm(formula = cbind(ml, ad) ~ rok + obdobi + kraj + resid_usili2 +
rok:obdobi + rok:kraj + obdobi:kraj + kraj:resid_usili2 +
rok:obdobi:kraj, family = "quasibinomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.7796 -1.0958 -0.3101 1.0034 2.8370
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -486.72087 664.71911 -0.732 0.46424
rok 0.24232 0.33114 0.732 0.46452
obdobinehn 3400.43703 1354.14874 2.511 0.01223 *
krajJHC 786.22409 708.50291 1.110 0.26746
krajJHM 511.85538 823.03038 0.622 0.53417
krajLBK -23.94180 2388.86316 -0.010 0.99201
krajMSK 1281.88767 955.09736 1.342 0.17992
krajOLK -175.19425 1255.82946 -0.140 0.88909
krajPAK 349.76438 1071.03364 0.327 0.74408
krajPLK -1335.73206 1534.09899 -0.871 0.38418
krajSTC 868.99157 692.30426 1.255 0.20976
krajULKV 245661.86828 17496742.31677 0.014 0.98880
krajVYS 3341.76686 1314.77140 2.542 0.01121 *
krajZLK 3950.75617 2922.25220 1.352 0.17676
resid_usili2 -1.44719 0.89315 -1.620 0.10555
rok:obdobinehn -1.69479 0.67462 -2.512 0.01219 *
rok:krajJHC -0.39108 0.35295 -1.108 0.26817
rok:krajJHM -0.25481 0.40997 -0.622 0.53443
rok:krajLBK 0.01621 1.19155 0.014 0.98915
rok:krajMSK -0.63985 0.47592 -1.344 0.17917
rok:krajOLK 0.08714 0.62545 0.139 0.88923
rok:krajPAK -0.17419 0.53344 -0.327 0.74410
rok:krajPLK 0.66539 0.76383 0.871 0.38394
rok:krajSTC -0.43292 0.34490 -1.255 0.20976
rok:krajULKV -122.01076 8704.03367 -0.014 0.98882
rok:krajVYS -1.66391 0.65468 -2.542 0.01122 *
rok:krajZLK -1.96718 1.45474 -1.352 0.17667
obdobinehn:krajJHC -3623.86807 1385.86009 -2.615 0.00909 **
obdobinehn:krajJHM -3220.08906 1458.83842 -2.207 0.02757 *
obdobinehn:krajLBK -1051.07131 3434.11845 -0.306 0.75963
obdobinehn:krajMSK -6415.65781 1978.30260 -3.243 0.00123 **
obdobinehn:krajOLK -2427.66591 1777.51914 -1.366 0.17239
obdobinehn:krajPAK -3111.45312 1623.59145 -1.916 0.05566 .
obdobinehn:krajPLK -1800.26258 2065.74461 -0.871 0.38375
obdobinehn:krajSTC -4409.45624 1379.64196 -3.196 0.00145 **
obdobinehn:krajULKV -187832.68360 16454272.74951 -0.011 0.99089
obdobinehn:krajVYS -5445.51446 1791.38012 -3.040 0.00244 **
obdobinehn:krajZLK -6216.43343 3167.49836 -1.963 0.05003 .
krajJHC:resid_usili2 1.60474 0.98554 1.628 0.10385
krajJHM:resid_usili2 1.57822 1.04518 1.510 0.13143
krajLBK:resid_usili2 11.53462 13.40012 0.861 0.38961
krajMSK:resid_usili2 -1.33600 1.55241 -0.861 0.38971
krajOLK:resid_usili2 0.07296 1.27034 0.057 0.95421
krajPAK:resid_usili2 1.35880 1.23033 1.104 0.26974
krajPLK:resid_usili2 1.90189 1.41163 1.347 0.17826
krajSTC:resid_usili2 2.05237 0.95972 2.139 0.03277 *
krajULKV:resid_usili2 599.79215 20568.86123 0.029 0.97674
krajVYS:resid_usili2 3.03834 1.16464 2.609 0.00925 **
krajZLK:resid_usili2 1.18574 1.11024 1.068 0.28583
rok:obdobinehn:krajJHC 1.80611 0.69042 2.616 0.00906 **
rok:obdobinehn:krajJHM 1.60475 0.72676 2.208 0.02751 *
rok:obdobinehn:krajLBK 0.52268 1.71244 0.305 0.76027
rok:obdobinehn:krajMSK 3.19712 0.98564 3.244 0.00123 **
rok:obdobinehn:krajOLK 1.21012 0.88541 1.367 0.17208
rok:obdobinehn:krajPAK 1.55034 0.80886 1.917 0.05563 .
rok:obdobinehn:krajPLK 0.89718 1.02893 0.872 0.38349
rok:obdobinehn:krajSTC 2.19742 0.68732 3.197 0.00144 **
rok:obdobinehn:krajULKV 93.43130 8189.24994 0.011 0.99090
rok:obdobinehn:krajVYS 2.71357 0.89236 3.041 0.00243 **
rok:obdobinehn:krajZLK 3.09624 1.57711 1.963 0.04996 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for quasibinomial family taken to be 1.258421)
Null deviance: 1518.0 on 878 degrees of freedom
Residual deviance: 1228.6 on 819 degrees of freedom
(465 observations deleted due to missingness)
AIC: NA
Number of Fisher Scoring iterations: 18
What does this mean?? Does it mean some multicollinearity, like @Scortchi mentioned in this discussion? Or does this mean overfitting? How to detect the problem? What shall I do now?
I tried to remove some variables. This helps a bit but not so much:
> m6 <- update(m5, ~.- kraj:resid_usili2)
> m7 <- update(m6, ~.- resid_usili2)
> summary(m7)
Call:
glm(formula = cbind(ml, ad) ~ rok + obdobi + kraj + rok:obdobi +
rok:kraj + obdobi:kraj + rok:obdobi:kraj, family = "quasibinomial")
Deviance Residuals:
Min 1Q Median 3Q Max
-2.9098 -1.1931 -0.2274 1.0529 3.1283
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -118.95199 476.34698 -0.250 0.803
rok 0.05971 0.23718 0.252 0.801
obdobinehn 412.69412 646.95083 0.638 0.524
krajJHC 447.69791 498.45358 0.898 0.369
krajJHM -62.92516 525.85737 -0.120 0.905
krajLBK 677.73239 1595.20024 0.425 0.671
krajMSK 278.24639 621.32312 0.448 0.654
krajOLK -705.97832 782.53474 -0.902 0.367
krajPAK 387.96543 608.98961 0.637 0.524
krajPLK -653.68419 782.20737 -0.836 0.403
krajSTC -114.34822 489.06318 -0.234 0.815
krajULKV -2117.64674 1797.75836 -1.178 0.239
krajVYS 884.74411 681.05324 1.299 0.194
krajZLK -997.77613 925.93280 -1.078 0.281
rok:obdobinehn -0.20602 0.32211 -0.640 0.523
rok:krajJHC -0.22303 0.24819 -0.899 0.369
rok:krajJHM 0.03092 0.26180 0.118 0.906
rok:krajLBK -0.33909 0.79438 -0.427 0.670
rok:krajMSK -0.13889 0.30935 -0.449 0.654
rok:krajOLK 0.35102 0.38943 0.901 0.368
rok:krajPAK -0.19392 0.30323 -0.640 0.523
rok:krajPLK 0.32463 0.38937 0.834 0.405
rok:krajSTC 0.05677 0.24351 0.233 0.816
rok:krajULKV 1.05287 0.89453 1.177 0.239
rok:krajVYS -0.44149 0.33911 -1.302 0.193
rok:krajZLK 0.49612 0.46081 1.077 0.282
obdobinehn:krajJHC -776.31258 672.68911 -1.154 0.249
obdobinehn:krajJHM -267.78650 700.38741 -0.382 0.702
obdobinehn:krajLBK -1246.67321 1760.37329 -0.708 0.479
obdobinehn:krajMSK -383.77613 858.81391 -0.447 0.655
obdobinehn:krajOLK -96.72334 947.75189 -0.102 0.919
obdobinehn:krajPAK -540.25140 827.13134 -0.653 0.514
obdobinehn:krajPLK -517.49161 1124.63474 -0.460 0.645
obdobinehn:krajSTC -683.81160 672.66674 -1.017 0.310
obdobinehn:krajULKV 2344.32314 2073.98366 1.130 0.259
obdobinehn:krajVYS -795.62043 917.80551 -0.867 0.386
obdobinehn:krajZLK 618.33075 1093.37768 0.566 0.572
rok:obdobinehn:krajJHC 0.38725 0.33493 1.156 0.248
rok:obdobinehn:krajJHM 0.13374 0.34870 0.384 0.701
rok:obdobinehn:krajLBK 0.62237 0.87662 0.710 0.478
rok:obdobinehn:krajMSK 0.19114 0.42758 0.447 0.655
rok:obdobinehn:krajOLK 0.04842 0.47171 0.103 0.918
rok:obdobinehn:krajPAK 0.26922 0.41184 0.654 0.513
rok:obdobinehn:krajPLK 0.25790 0.55986 0.461 0.645
rok:obdobinehn:krajSTC 0.34078 0.33492 1.017 0.309
rok:obdobinehn:krajULKV -1.16571 1.03236 -1.129 0.259
rok:obdobinehn:krajVYS 0.39675 0.45704 0.868 0.386
rok:obdobinehn:krajZLK -0.30732 0.54422 -0.565 0.572
(Dispersion parameter for quasibinomial family taken to be 1.313286)
Null deviance: 2396.8 on 1343 degrees of freedom
Residual deviance: 2110.3 on 1296 degrees of freedom
AIC: NA
Number of Fisher Scoring iterations: 5
EDIT: As proposed by Scortchi, I tried to use VIF and I also get enormous values. What does this mean? See:
> require(HH)
> vif(cbind(ml, ad) ~ rok + obdobi + kraj + resid_usili2 +
+ rok:obdobi + rok:kraj + obdobi:kraj + kraj:resid_usili2 +
+ rok:obdobi:kraj)
rok obdobinehn krajJHC krajJHM
50.281603 45075363.969712 15194580.406796 11362184.620230
krajLBK krajMSK krajOLK krajPAK
7567915.376763 5228018.864051 17105623.986998 10944471.683601
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