na.gam.replace(frame)
The following rules are used. A factor with missing data is
replaced by a new factor with one more level, labeled
"NA"
,
which records the missing data.
Ordered factors are treated similarly, except the result is an unordered factor.
A missing numeric vector has its missing entires replaced
by the mean of the non-missing entries.
Similarly, a matrix with missing entries has each missing
entry replace by the mean of its column.
If
frame
is a model frame, the response variable can be identified,
as can the weights (if present).
Any rows for which the response or weight is missing are removed entirely
from the model frame.
The word
"gam"
in the name is relevant, because
gam
makes
special use of this filter.
All columns of a model frame that were created by a call
to
lo
or
s
have an attribute names
"NAs"
if NAs
are present in their columns.
Despite the replacement by means, these attributes remain on the object,
and
gam
takes appropriate action when smoothing against these columns.
See section 7.3.2 in
Statistical Models in Sfor more details.
gam(pick ~ s(income) + size, family = binomial, data = market.frame, na.action = na.gam.replace) # fit an additive model in the presence of missing data replaced.data <- na.gam.replace(market.frame) attach(replaced.data)