calculating regression sum of square in R Here is sample data:
        brainIQ <- 
      read.table (file= "https://onlinecourses.science.psu.edu/stat501/sites/
  onlinecourses.science.psu.edu.stat501/files/data/iqsize.txt",
     head = TRUE)

I am trying to fit multiple linear regression.
mylm <- lm(PIQ ~  Brain + Height + Weight, data = brainIQ)
anova(mylm)

Default function anova in R provides sequential sum of squares (type I) sum of square.
Analysis of Variance Table

Response: PIQ
          Df  Sum Sq Mean Sq F value  Pr(>F)  
Brain      1  2697.1 2697.09  6.8835 0.01293 *
Height     1  2875.6 2875.65  7.3392 0.01049 *
Weight     1     0.0    0.00  0.0000 0.99775  
Residuals 34 13321.8  391.82                  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

I belief, thus the SS are Brain, Height | Brain, Weight | (Brain, Weight) and residuals respectively.
Using package car we can also get type II sum of square.
library(car)
Anova(mylm, type="II")
Anova Table (Type II tests)

Response: PIQ
           Sum Sq Df F value    Pr(>F)    
Brain      5239.2  1 13.3716 0.0008556 ***
Height     1934.7  1  4.9378 0.0330338 *  
Weight        0.0  1  0.0000 0.9977495    
Residuals 13321.8 34                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Here sum of squares are like: Brian | (Height, Weight), Height | (Brain, Weight), Weight | (Brain, Height).
Which look pretty like Mintab output:

My question is how can I calculate the regression row in the above table in R ?
 A: SS(Regression) = SS(Total) - S(Residual)

You can get SS(Total) by:
SSTotal <- var( brainIQ$PIQ ) * (nrow(brainIQ)-1)
    SSE     <- sum( mylm$resid^2 )
SSreg   <- SSTotal - SSE

The degrees of freedom for the "Regression" row are the sum of the degrees of freedom for the corresponding components of the Regression (in this case: Brain, Height, and Weight).
Then to get the rest:
dfE   <- mylm$df.residual
dfReg <- nrow(brainIQ) - 1 - dfE
MSreg <- SSreg / dfReg
MSE   <- SSE / dfE
Fstat <- MSreg / MSE
pval  <- pf( Fstat , dfReg, dfE , lower.tail=FALSE )

A: If you have built a linear model already, you can compute the regression sum of squares with one line. Using your model:
sum((mylm$fitted.values - mean(mylm$fitted.values))^2)

This takes advantage of the fact that the mean of the response is equal to the mean of the fitted values.
UPDATE: As @whuber pointed out, the line above assumes that the model includes a constant ("intercept") in the span of its design matrix. The line below does not make this assumption and will work more generally:
sum((mylm$fitted.values - mean(mylm$model$y))^2)

A: I found matrix approach to solve this problem:
Here we can calculate SSR and Total sum of square. 
    # Y matrix 
    Y <- as.matrix(brainIQ$PIQ, ncol=1)
    n= nrow(Y)
    J = matrix(1, nrow=n, ncol=n)
    # Total sum of Square 
    SSTO = t(Y) %*% Y - (1/n)*t(Y)%*%J%*%Y 
    X <- as.matrix(cbind(1, brainIQ[,-1]))
    b = solve(t(X)%*%X)%*%t(X)%*%Y
# regression sum of square 
    SSR = t(b)%*%t(X)%*%Y - (1/n)%*%t(Y)%*%J%*%Y
> SSR
         [,1]
[1,] 5572.744

> SSTO
         [,1]
[1,] 18894.55

Here - SSR(Brain, Height, Weight) = SSR(Brain) + SSR(Height|Brain) + SSR (Weight|Height, Brain) - which is actually sequential sum of square output from anova.
mylm <- lm(PIQ ~  Brain + Height + Weight, data = brainIQ)

# SSR(Brain, Height, Weight)
sum(anova(mylm)[-4,2])

# Total sum of square 
sum(anova(mylm)[,2])

