Solved – Interaction suppresses the main effect? How to interpret it

I have a simple model without interaction and it stated significant effect for all the explanatory variables (continuous variable rok and categorical variables obdobi (levels hn and nehn) and kraj:

Call: glm(formula = cbind(ml, ad) ~ rok + obdobi + kraj, family = "quasibinomial")  Deviance Residuals:      Min       1Q   Median       3Q      Max   -3.8007  -1.1716  -0.5117   1.0864   4.2184    Coefficients:               Estimate Std. Error t value Pr(>|t|)    (Intercept) -107.60761   53.96993  -1.994  0.04674 *  rok            0.05381    0.02686   2.003  0.04576 *  obdobinehn    -0.26962    0.11646  -2.315  0.02104 *  krajJHC        0.68869    0.31009   2.221  0.02683 *  krajJHM       -0.26607    0.32166  -0.827  0.40855    krajLBK       -1.11305    0.61942  -1.797  0.07298 .  krajMSK       -0.61390    0.41828  -1.468  0.14285    krajOLK       -0.49704    0.36981  -1.344  0.17958    krajPAK       -1.18444    0.39401  -3.006  0.00279 ** krajPLK       -1.28668    0.49672  -2.590  0.00988 ** krajSTC        0.01872    0.31222   0.060  0.95220    krajULKV      -0.41950    0.69220  -0.606  0.54478    krajVYS       -1.17290    0.44614  -2.629  0.00884 ** krajZLK       -0.38170    0.40969  -0.932  0.35198    --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1   (Dispersion parameter for quasibinomial family taken to be 1.645035)      Null deviance: 1136.22  on 489  degrees of freedom Residual deviance:  938.02  on 476  degrees of freedom AIC: NA  Number of Fisher Scoring iterations: 4 

Then I added interaction obdobi:kraj:

Call: glm(formula = cbind(ml, ad) ~ rok + obdobi + kraj + obdobi:kraj,      family = "quasibinomial")  Deviance Residuals:      Min       1Q   Median       3Q      Max   -3.4635  -1.1706  -0.4597   1.0275   4.6829    Coefficients:                       Estimate Std. Error t value Pr(>|t|)    (Intercept)         -101.49501   54.53576  -1.861  0.06336 .  rok                    0.05102    0.02715   1.879  0.06086 .  obdobinehn            -1.11653    0.62058  -1.799  0.07264 .  krajJHC               -0.16805    0.51957  -0.323  0.74651    krajJHM               -0.77451    0.53738  -1.441  0.15018    krajLBK               -3.29567    1.42164  -2.318  0.02087 *  krajMSK               -0.73640    0.67267  -1.095  0.27420    krajOLK               -0.41582    0.68758  -0.605  0.54564    krajPAK               -1.50156    0.63871  -2.351  0.01914 *  krajPLK               -1.48611    0.75745  -1.962  0.05036 .  krajSTC               -0.34170    0.52059  -0.656  0.51191    krajULKV              -1.72550    1.02726  -1.680  0.09369 .  krajVYS               -1.93603    0.65862  -2.940  0.00345 ** krajZLK               -0.71065    0.65791  -1.080  0.28063    obdobinehn:krajJHC     1.44638    0.65507   2.208  0.02773 *  obdobinehn:krajJHM     0.82070    0.67910   1.209  0.22746    obdobinehn:krajLBK     3.31340    1.61026   2.058  0.04018 *  obdobinehn:krajMSK     0.12470    0.87281   0.143  0.88645    obdobinehn:krajOLK     0.04528    0.82529   0.055  0.95627    obdobinehn:krajPAK     0.48978    0.81921   0.598  0.55022    obdobinehn:krajPLK     0.23075    1.02316   0.226  0.82167    obdobinehn:krajSTC     0.50339    0.65976   0.763  0.44585    obdobinehn:krajULKV    2.49157    1.43679   1.734  0.08356 .  obdobinehn:krajVYS     1.48201    0.92082   1.609  0.10820    obdobinehn:krajZLK     0.49357    0.85087   0.580  0.56214    --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1   (Dispersion parameter for quasibinomial family taken to be 1.613648)      Null deviance: 1136.22  on 489  degrees of freedom Residual deviance:  899.28  on 465  degrees of freedom AIC: NA  Number of Fisher Scoring iterations: 4 

Strange thing happened – the main effects rok and obdobi are no longer significant! How can this happen? How to interpret this fact? If the interaction obdobi:kraj has significant effect, then the obdobi also has significant effect, right?

Note that the second model differs significantly (tested by anova(..., test = "Chi")).

Thanks in advance!

EDIT: added anova tables of the models (but since this is glm and not simple lm, mean sum of squares and p-values are missing and I don't know how to interpret it…)

> anova(model1) Analysis of Deviance Table  Model: quasibinomial, link: logit  Response: cbind(ml, ad)  Terms added sequentially (first to last)          Df Deviance Resid. Df Resid. Dev NULL                      489    1136.22 rok      1     3.06       488    1133.16 obdobi   1    11.20       487    1121.96 kraj    11   183.94       476     938.02  > anova(model2) Analysis of Deviance Table  Model: quasibinomial, link: logit  Response: cbind(ml, ad)  Terms added sequentially (first to last)               Df Deviance Resid. Df Resid. Dev NULL                           489    1136.22 rok           1     3.06       488    1133.16 obdobi        1    11.20       487    1121.96 kraj         11   183.94       476     938.02 obdobi:kraj  11    38.74       465     899.28 

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