A method for dealing with missing values, friendly to GAM models.
a model or data frame
a model or data frame is returned, with the missing observations
(NAs) replaced. The following rules are used. A factor with missing data is
replaced by a new factor with one more level, labelled
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
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.
"gam" in the name is relevant, because
special use of this filter. All columns of a model frame that were created
by a call to
s() have an attribute names
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 Hastie
(1992) for more details.
Hastie, T. J. (1992) Generalized additive models. Chapter 7 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.
data(airquality) gam(Ozone^(1/3) ~ lo(Solar.R) + lo(Wind, Temp), data=airquality, na=na.gam.replace)
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