# How to manually calculate dfbetas

I am trying to replicate what the function dfbetas() does in R.

dfbeta() is not an issue... Here is a set of vectors:

x <- c(0.512, 0.166, -0.142, -0.614, 12.72)
y <- c(0.545, -0.02, -0.137, -0.751, 1.344)


If I fit two regression models as follows:

fit1 <- lm(y ~ x)
fit2 <- lm(y[-5] ~ x[-5])


I see that eliminating the last point results in a very different slope (blue line - steeper): This is reflected in the change in slopes:

fit1$coeff - fit2$coeff
-0.9754245


which coincides with the dfbeta(fit1) for the fifth value:

   (Intercept)            x
1  0.182291949 -0.011780253
2  0.020129324 -0.001482465
3 -0.006317008  0.000513419
4 -0.207849024  0.019182219
5 -0.032139356 -0.975424544


Now if I want to standardize this change in slope (obtain dfbetas) and I resort to:

Williams, D. A. (1987) Generalized linear model diagnostics using the deviance and single case deletions. Applied Statistics 36, 181–191

which I think may be one of the references in the R documentation under the package {stats}. There the formula for dfbetas is:

$\large \mathrm{dfbetas} (i, \mathrm{fit}) = \Large {(\hat{b} - \hat{b}_{-i})\over \mathrm{SE}\, \hat{b}_{-i}}$

This could be easily calculated in R:

(fit1$coef - fit2$coef)/summary(fit2)$coef  yielding: -6.79799 The question is why I am not getting the fifth value for the slope in: dfbetas(fit1) (Intercept) x 1 1.06199661 -0.39123009 2 0.06925319 -0.02907481 3 -0.02165967 0.01003539 4 -1.24491242 0.65495527 5 -0.54223793 -93.81415653!  What is the right equation to go from dfbeta to dfbetas? ## 1 Answer$DFBETAS_{k(i)}$is calculated by:$b_k-b_{k(i)}\over{\sqrt{MSE_{(i)}c_{kk}}}$, for$k$= 1, 2, . . . ,$p$. where$b_k$is the$k$th regression coefficient that uses all the data and$b_{k(i)}$is the same coefficient with the$i$th case deleted.$MSE_{(i)}$here is the mean-square error from the regression where the$i$case is deleted and$c_{kk}$is the$k$th diagonal element of the unscaled covariance matrix$(X^{\prime}X)^{-1}$. So you can calculate$DFBETAS_{k(i)}$manually with the following R code: numerator<-(fit1$coef - fit2$coef) denominator<-sqrt((summary(fit2)$sigma^2)*diag(summary(fit1)\$cov.unscaled))
DFBETAS<-numerator/denominator
DFBETAS
x
-93.81416

• if it's not obvious, p is the number of regression parameters or coefficients. Sorry for the sloppy notation. ;-) Mar 11 '15 at 2:35