pfa.lda: PFA Formatting of Fitted Linear Discriminant Models

In aurelius: Generates PFA Documents from R Code and Optionally Runs Them

Description

This function takes a linear discriminant model fit using lda and returns a list-of-lists representing in valid PFA document that could be used for scoring

Usage

## S3 method for class 'lda'
pfa(object, name = NULL, version = NULL, doc = NULL,
  metadata = NULL, randseed = NULL, options = NULL,
  prior = object$prior, dimen = length(object$svd), method = c("plug-in"),
  pred_type = c("response", "prob"), cutoffs = NULL, ...)

Arguments

object	an object of class "lda"
name	a character which is an optional name for the scoring engine
version	an integer which is sequential version number for the model
doc	a character which is documentation string for archival purposes
metadata	a list of strings that is computer-readable documentation for archival purposes
randseed	a integer which is a global seed used to generate all random numbers. Multiple scoring engines derived from the same PFA file have different seeds generated from the global one
options	a list with value types depending on option name Initialization or runtime options to customize implementation (e.g. optimization switches). May be overridden or ignored by PFA consumer
prior	a named vector specifying the prior probabilities of class membership. If unspecified, the class proportions for the training set are used.
dimen	an integer specifying the dimension of the space to be used. If this is less than min(p input variables, number of classes - 1) then the first N number of dimensions will be used in the calculation
method	a character string indicating the prediction method. Currently, only the plug-in method is supported.
pred_type	a string with value "response" for returning a prediction on the same scale as what was provided during modeling, or value "prob", which for classification problems returns the probability of each class.
cutoffs	(Classification only) A named numeric vector of length equal to number of classes. The "winning" class for an observation is the one with the maximum ratio of predicted probability to its cutoff. The default cutoffs assume the same cutoff for each class that is 1/k where k is the number of classes
...	additional arguments affecting the PFA produced

Value

a list of lists that compose valid PFA document

Source

pfa_config.R avro_typemap.R avro.R pfa_cellpool.R pfa_expr.R pfa_utils.R

See Also

lda extract_params.lda

Examples

model <- MASS::lda(Species ~ ., data=iris)
model_as_pfa <- pfa(model)

Example output

aurelius documentation built on May 2, 2019, 3:43 a.m.