Missing intercept in fixed effects model output I am running a fixed effects model using plm in R and I'm struggling to understand the output.

*

*Why is the estimate for the intercept term missing?

*Why the R-Squared value is so low compared to the Adj. R-Squared?

*Why the R-Squared value is so low compared to a random effects model I ran with R-Squared = 0.88?

> summary(fixed)
Oneway (individual) effect Within Model

Call:
plm(formula = Y ~ X, data = pdata, model = "within")

Unbalanced Panel: n = 271, T = 1-16, N = 1077

Residuals:
    Min.  1st Qu.   Median  3rd Qu.     Max. 
-10.4083  -1.5881   0.0000   1.6870   9.9549 

Coefficients:
  Estimate Std. Error t-value  Pr(>|t|)    
X 0.339422   0.023758  14.287 < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Total Sum of Squares:    10156
Residual Sum of Squares: 8101.5
R-Squared:      0.20226
Adj. R-Squared: -0.066294
F-statistic: 204.103 on 1 and 805 DF, p-value: < 2.22e-16
```


I am unfamiliar with plm() but I can attempt an answer to questions 1 & 2. For 2), the adjusted R2 is the amount of variance explained relative to the simple mean of the data. Because your adjusted R2 is essentially zero, it suggests that the result of your formula has been to take the mean of the response variable Y. So I would expect that your effect estimate X=0.339422, is essentially the mean of Y. This answers your first question -- actually the intercept is not missing. The X=0.339422 is an intercept. What is missing is a slope.
