Capital N gives you the total number of rows in your data which corresponds to the number of observations in the pooled model (option model="pooling" in the plm function).
Lowercase n gives you the unique number of observations (let's say groups or individuals). This corresponds to the number of dummies you add if you use the least square dummy variable estimator rather than the within estimator which is equivalent.
Capital $T$ shows how often an observation or an individual is observed (referring to the time dimension). $T=1-2$ means that there are both individuals which you observe only ones but also others are who are observed twice.
Since you have at most two periods, you can easily calculate that there are 35 individuals (211-176) who appear twice while information for 141 individuals is only available for one period. In general, you cannot cannot make this calculation if T>2 and you need further information.
Here is a dataset of soccer matches for three seasons in the German Bundesliga. In Germany, 18 teams play in a season 34 matches each. Each game appears here twice from the view of each team, "Home" refers to the home team. We use the fixed-effects within model to estimate the home advantage over time (relative to the first season in our sample) controlling for possible time/season effects.
rm(list=ls(all=TRUE))
library(plm)
bundesliga<-read.csv("https://drive.google.com/uc?export=download&id=0B70aDwYo0zuGRGxVV1p2MTlqaUk")
head(bundesliga)
> head(bundesliga)
Season Round Team Opponent Home Goals_Diff
1 2013/14 1 Bayern München Bor. Mönchengladbach H 2
2 2013/14 1 1899 Hoffenheim 1. FC Nürnberg H 0
3 2013/14 1 Bayer 04 Leverkusen SC Freiburg H 2
4 2013/14 1 Hannover 96 VfL Wolfsburg H 2
5 2013/14 1 FC Augsburg Borussia Dortmund H -4
6 2013/14 1 Hertha BSC Eintracht Frankfurt H 5
# Create time index
bundesliga$Index<-as.numeric(as.factor(bundesliga$Season))*100+bundesliga$Round
# Declare panel data
bl_panel<-pdata.frame(bundesliga,c("Team","Index"))
# Run regression
summary(plm(Goals_Diff~Home*Season,data=bl_panel,model = "within"))
Here are the results. Home advantage is strong and corresponds to a goal difference of 0.67 goals in the first season, no statistical differences in home advantage in the subsequent two seasons relative to the first season (and no discernible season effects):
> summary(plm(Goals_Diff~Home*Season,data=bl_panel,model = "within"))
Oneway (individual) effect Within Model
Call:
plm(formula = Goals_Diff ~ Home * Season, data = bl_panel, model = "within")
Unbalanced Panel: n=22, T=34-102, N=1836
Residuals :
Min. 1st Qu. Median 3rd Qu. Max.
-7.0400 -1.1700 -0.0185 1.1300 5.8300
Coefficients :
Estimate Std. Error t-value Pr(>|t|)
HomeH 0.673203 0.143947 4.6768 3.131e-06 ***
Season2014/15 -0.132377 0.147800 -0.8957 0.3706
Season2015/16 -0.057980 0.149638 -0.3875 0.6985
HomeH:Season2014/15 0.169935 0.203571 0.8348 0.4040
HomeH:Season2015/16 -0.071895 0.203571 -0.3532 0.7240
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Total Sum of Squares: 5970.7
Residual Sum of Squares: 5735
R-Squared : 0.039484
Adj. R-Squared : 0.038904
F-statistic: 14.8726 on 5 and 1809 DF, p-value: 2.5075e-14
There are in total n=1,836 observations, 612 observations for each of the three seasons. We observe n=22 unique teams.
T=34-102 means that there are teams which we observe only for one seasons or 34 matches while others appear in all three seasons (or 102 games). To assess how balanced your sample is, you can look at the number of seasons per team which shows you that most teams are observed for all three seasons. You can calculate $N=1836=(15*3+2*2+5*1)*34$ and $n=15+2+5$.
> table(table(bl_panel$Team)/34)
1 2 3
5 2 15
