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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
[... cut out ...]
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11
  • $\begingroup$ Well, have you looked at a correlation matrix of krjXXX variables to see if they are highly correlated? $\endgroup$
    – zbicyclist
    Commented Jan 29, 2013 at 1:10
  • $\begingroup$ @zbicyclist, thanks. kraj is just one categorical variable of 12 levels (HKK (hidden in intercept), JHC, JHM, LBK, MSK, ...), so I guess correlation matrix for krajXXX doesn't make sense, am I correct? What should I do then? $\endgroup$
    – Tomas
    Commented Jan 29, 2013 at 1:14
  • $\begingroup$ Quick request: your link to a discussion by Scortchi above has no actual link in it, could you please add that in? Thanks! $\endgroup$
    – James Stanley
    Commented Jan 29, 2013 at 1:26
  • 2
    $\begingroup$ Tomas, I presume the HKK level is a frequent level (i.e. you didn't drop a level with just 1 or 2 observations). A mistake that's sometimes made is to drop the least frequent level. I think @James Stanley has the best suggestion on what to do next. $\endgroup$
    – zbicyclist
    Commented Feb 2, 2013 at 17:18
  • 1
    $\begingroup$ No worries, good to know -- I think @zbicyclist's point is that if you choose a reference category which has really infrequent outcome, then all of the parameters for that factor might be affected by the quasi-complete separation (whereas choosing a level with more frequent outcomes will prevent this being a problem for all parameters). [FYI, which you might already know -- you can change the reference level if needed: in R, one would use e.g. kraj <- relevel(kraj, ref = "JHC") if you wanted to use JHC as the reference level instead.] $\endgroup$
    – James Stanley
    Commented Feb 3, 2013 at 22:40

1 Answer 1

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$\begingroup$

I would suggest that the massive coefficients, and the correspondingly massive standard errors, would almost definitely be caused by quasi-complete or complete separation. That is, for some combination of parameters, either everyone had the outcome or nobody had the outcome, and so the coefficient heads towards infinity (or negative infinity.)

This tends to happen especially when one specifies a lot of interaction terms, as the chances of having a combination of factors which results in some "empty" (no outcomes in cell, or everyone has outcomes) cells will increase.

See the following page for some further details and suggested strategies (link updated March 2021): https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/

More generally, it means that you're probably trying to do "too much" with your model for the size of your dataset (particularly the number of outcomes observed).

EDIT: A couple of pragmatic suggestions

You might try (1) quick and simple: drop the interaction terms from your model, to see if that helps (whether this makes sense from a research question perspective is an entirely different issue); or (2) get R to make you a bi-i-i-i-g contingency table for (e.g. rows) the combinations described in the interactions by (e.g. columns) the outcome variable. You might be able to see some evidence of separation here.

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10
  • $\begingroup$ thanks James. So does this actually mean overfitting? Does this mean that I should possibly not include the interactions into the model? $\endgroup$
    – Tomas
    Commented Jan 29, 2013 at 1:32
  • $\begingroup$ I don't think this is technically "overfitting", but a case of overstretching your model. See e.g. Wikipedia on what is generally meant by overfitting (and I won't pretend to be an expert on the definition): en.wikipedia.org/wiki/Overfitting -- that an overspecified model is one where the parameters estimated would likely not perform well in cross-validation, or in other words, the model you've specified will describe this sample, but would not work well on another sample from the same population. $\endgroup$
    – James Stanley
    Commented Jan 29, 2013 at 1:39
  • $\begingroup$ thanks James - but this is exactly what I imagine under the term Overfitting.. BTW, I used VIF and got enormous values too, please see my edited question. Does this tell you something new about multicollinearity/overfitting issues? $\endgroup$
    – Tomas
    Commented Jan 29, 2013 at 1:46
  • 2
    $\begingroup$ I think this is just a question of terminology/jargon -- what you are describing is still a problem, and is due to overspecification, but I don't think we would refer to this as "overfitting" in a formal sense. I'll have to go away and read some bits on the distinctions to be clearer! $\endgroup$
    – James Stanley
    Commented Jan 29, 2013 at 1:48
  • 2
    $\begingroup$ I'm not sure whether there is a technical term beyond quasi-complete separation. I would say "to avoid quasi-complete separation (due to sparse data in combinations of the two factors) we did not test for interactions". Obviously this is pretty much all jargon, but I think this might be the best description? $\endgroup$
    – James Stanley
    Commented Feb 2, 2013 at 19:20

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