# calculating regression sum of square in R

Here is sample data:

        brainIQ <-
onlinecourses.science.psu.edu.stat501/files/data/iqsize.txt",


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 ?

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$df.residual dfReg <- nrow(brainIQ) - 1 - dfE MSreg <- SSreg / dfReg MSE <- SSE / dfE Fstat <- MSreg / MSE pval <- pf( Fstat , dfReg, dfE , lower.tail=FALSE )  • Note also that deviance(mylm) will directly report the SSE. – Tyler R. Apr 27 at 16:42 I found matrix approach to solve this problem: Here we can calculate SSR and Total sum of square.  # Y matrix Y <- as.matrix(brainIQ$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])