I am running a linear model on the impact of a particular team composition on the performance of teams. I have included the region as a separate variable since I plan to compare the coeeficients afterwards. I run the model with interactions between the strategy and every single region. That is what the code for the model looks like. lm(Kills~Solocarry + Region:Solocarry, unidt) This is the result I get
Estimate Std. Error t value Pr(>|t|)
(Intercept) 26.8390 0.6221 43.139 < 2e-16 ***
SolocarryTRUE -3.0468 1.4180 -2.149 0.03178 *
SolocarryFALSE:RegionEurope 0.9498 0.8943 1.062 0.28833
SolocarryTRUE:RegionEurope -3.3712 1.7146 -1.966 0.04942 *
SolocarryFALSE:RegionKorea -3.2339 0.8861 -3.649 0.00027 ***
SolocarryTRUE:RegionKorea -7.6628 1.7591 -4.356 1.39e-05 ***
SolocarryFALSE:RegionNAmerica -1.0089 0.8876 -1.137 0.25581
SolocarryTRUE:RegionNAmerica -7.1216 1.7591 -4.048 5.36e-05 ***
SolocarryFALSE:RegionOceania 0.6659 0.8927 0.746 0.45581
SolocarryTRUE:RegionOceania -1.5712 1.7146 -0.916 0.35959
And here you can see that the last one region (that is Brazil) is missing. In the beginning, I thought that Brazil is included as an intercept, but all the other regions have both estimates for True and False for the solocarry coefficient, so shouldn't I have at least something like SolocarryTRUE:RegionBrazil or SolocarryFALSE:RegionBrazil? When I check it with the unique function, I receive this.
> unique(unidt$Region)
[1] "Europe" "NAmerica" "Korea" "Brazil" "Oceania"
There are 400 observations for each of the regions (except North America but there are 399). When I open the data frame, I can see that it is still there. I have already tried turning it off and on again. Has anybody seen something like this? If yes or you have any idea how to solve it, I would appreciate your help.
