Cook's D

Fit Analyses

Cook's D measures the change in the parameter estimates caused by deleting each observation. For linear models,

D_i = [1/(p s²)] (b- b_(i))' (X'X) (b- b_(i))

where b_(i) is the vector of parameter estimates obtained after deleting the ith observation.

Cook (1977) suggests comparing D_i to the F distribution with p and n-p degrees of freedom.

For generalized linear models,

$D_{i} = \frac{1}{p \hat{ \phi}} {(b- b_{(i)})'} ({X'}W{X}) (b- b_{(i)})$

where W = W_o when the full Hessian is used and W = W_e when Fisher's scoring method is used.

Cook's D statistics are stored in variables named D_yname for each response variable, where yname is the response variable name.