pfa.lda {aurelius}R Documentation

PFA Formatting of Fitted Linear Discriminant Models

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)

[Package aurelius version 0.8.4 Index]