summary.polymars {polymars} | R Documentation |
Summarize a polymars model.
summary(mars.model)
mars.model |
a model returned from a call to polymars |
.
is.polymars
and summary.polymars
are available.
print.polymars
defaults to summary.polymars
.
The returned object contains information about the fitting steps and
the model selected. The first data frame contains a row for each step
of the fitting procedure. In the columns are: a 1 for an addition step
or a 0 for a deletion step, the size of the model at each step,
residual sums of squares (RSS) and the generalized cross validation
value (GCV), testset residual sums of squares or testset
misclassification, whatever was used for the model selection.
The second data frame, model, contains a row for each basis function
of the model. Each row corresponds to one basis function (with two
possible components). The pred1 column contains the indices of the
first predictor of the basis function. Column knot1 is a possible knot
in this predictor. If this column is NA, the first component is
linear. If any of the basis functions of the model is categorical then
there will be a level1 column. Column pred2 is the possible second
predictor involved (if it is NA the basis function only depends on one
predictor). Column knot2 contains the possible knot for the predictor
pred2, and it is NA when this component is linear. This is a similar
format to the startmodel argument together with an additional first
row corresponding to the intercept but the startmodel doesn't use a
separate column to specify levels of a categorical variable. If any
predictor in pred2 is categorical then there will be a level2
column. The column "coefs" (more than one column in the case of
multiple response regression) contains the coefficients.
polymars
,
plot.polymars
,
summary.polymars
,
print.polymars
fit <- polymars(mydata[,1], mydata[,c(2,3,4,5,6)]) summary(fit)