The influence.measures()
and other functions listed in
See Also provide a more user oriented way of computing a
variety of regression diagnostics. These all build on
lm.influence
. Note that for GLMs (other than the Gaussian
family with identity link) these are based on one-step approximations
which may be inadequate if a case has high influence.
An attempt is made to ensure that computed hat values that are
probably one are treated as one, and the corresponding rows in
sigma
and coefficients
are NaN
. (Dropping such a
case would normally result in a variable being dropped, so it is not
possible to give simple drop-one diagnostics.)
naresid
is applied to the results and so will fill in
with NA
s it the fit had na.action = na.exclude
.