title: plm vs lm - different results?
body: <p>I tried several times to use lm and plm to do a regression. And I get different results. </p>
  
  <p>First, I used lm as follows: </p>
  
  <pre><code>fixed.Region1 &lt;- lm(CapNormChange ~ Policychanges + factor(Region), 
      data=Panel)
  </code></pre>
  
  <p>Further I used plm in the following way: </p>
  
  <pre><code>fixed.Region2 &lt;- plm(CapNormChange ~ Policychanges+ factor(Region), 
      data=Panel, index=c("Region", "Year"), model="within", effect="individual")
  </code></pre>
  
  <p>I think there is something wrong with plm because I don't see an intercept in the results (see below). 
  Furthermore, I am not entirely sure if <code>+ factor (Region)</code> is necessary, however, if it is not there, I don't see the coefficients (and significance) for the dummy. </p>
  
  <p>So, my question is: </p>
  
  <ol>
  <li>I am using the plm function wrong? (or what is wrong about it)</li>
  <li>If not, how can it be that the results are different? </li>
  </ol>
  
  <p>If somebody could give me a hint, I would really appreciate. </p>
  
  <p>Results from LM: </p>
  
  <pre><code>Call:
  lm(formula = CapNormChange ~ Policychanges + factor(Region), 
      data = Panel)
  
  Residuals:
      Min      1Q  Median      3Q     Max 
  -31.141  -4.856  -0.642   1.262 192.803 
  
  Coefficients:
                                  Estimate Std. Error t value Pr(&gt;|t|)    
  (Intercept)                      17.3488     4.9134   3.531 0.000558 ***
  Policychanges                     0.6412     0.1215   5.277 4.77e-07 ***
  factor(Region)Asia              -19.3377     6.7804  -2.852 0.004989 ** 
  factor(Region)C America + Carib   0.1147     6.8049   0.017 0.986578    
  factor(Region)Eurasia           -17.6476     6.8294  -2.584 0.010767 *  
  factor(Region)Europe            -20.7759     8.8993  -2.335 0.020959 *  
  factor(Region)Middle East       -17.3348     6.8285  -2.539 0.012200 *  
  factor(Region)N America         -17.5932     6.8064  -2.585 0.010745 *  
  factor(Region)Oceania           -14.0440     6.8417  -2.053 0.041925 *  
  factor(Region)S America         -14.3580     6.7781  -2.118 0.035878 *  
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  Residual standard error: 19.72 on 143 degrees of freedom
  Multiple R-squared:  0.3455,    Adjusted R-squared:  0.3043 
  F-statistic: 8.386 on 9 and 143 DF,  p-value: 5.444e-10`
  </code></pre>
  
  <p>Results from PLM:</p>
  
  <pre><code> Call:
  plm(formula = CapNormChange ~ Policychanges, data = Panel, effect = "individual", 
      model = "within", index = c("Region", "Year"))
  
  Balanced Panel: n = 9, T = 17, N = 153
  
  Residuals:
       Min.   1st Qu.    Median   3rd Qu.      Max. 
  -31.14147  -4.85551  -0.64177   1.26236 192.80277 
  
  Coefficients:
                Estimate Std. Error t-value  Pr(&gt;|t|)    
  Policychanges  0.64118    0.12150   5.277 4.769e-07 ***
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  Total Sum of Squares:    66459
  Residual Sum of Squares: 55627
  R-Squared:      0.16299
  Adj. R-Squared: 0.11031
  F-statistic: 27.8465 on 1 and 143 DF, p-value: 4.7687e-07`
  </code></pre>