ExprsPipeline ObjectpipeFilter subsets an ExprsPipeline object.
pipeFilter(object, colBy, how = 0, gate = 0, top = 0)# S4 method for ExprsPipeline
pipeFilter(object, colBy, how = 0, gate = 0,
top = 0)
An ExprsPipeline-class object.
A character vector or string. Specifies column(s) to use when filtering by classifier performance. Listing multiple columns will result in a filter based on a performance metric equal to the product of those listed columns.
A numeric scalar. Arguments between 0 and 1 will impose
a threshold or ceiling filter, respectively, based on the raw value of
colBy. Arguments between 1 and 100 will impose a filter based on
the percentile of colBy. The user may also provide "midrange",
"median", or "mean" as an argument for these filters. Set how = 0
or gate = 0, to skip the threshold or ceiling filter,
respectively.
A numeric scalar. Determines the top N models based on
colBy to include after the threshold and ceiling filters.
In the case that the @summary slot contains the column
"boot", this determines the top N models for each unique bootstrap.
Set top = 0 to skip this subset.
An ExprsPipeline-class object.
ExprsPipeline: Method to filter ExprsPipeline objects.
The filter process occurs in three steps. However, the user may skip
any one of these steps by setting the respective argument to 0.
First, a threshold filter gets imposed. Any model with a performance
less than the threshold filter, how, gets excluded. Second,
a ceiling filter gets imposed. Any model with a performance less
than the ceiling filter, gate, gets excluded. Third, an
arbitrary subset occurs. The top N models in the ExprsPipeline
object get selected based on the argument top. However,
in the case that the @summary slot contains the column
"boot", pipeFilter selects the top N models for each unique
bootstrap.
pipeFilter will apply this filter for one or more performance
metrics listed in the colBy argument. Listing multiple columns
will result in a filter based on a performance metric equal to the
product of all listed performance metrics. To more heavily weigh
one performance metric over another, consider listing that column
more than once.
pipeFilter
pipeUnboot
plCV
plGrid
plGridMulti
plMonteCarlo
plNested