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Predict method for a discriminant_projector, supporting LDA or Euclid

predict.discriminant_projector.Rd

This produces class predictions or posterior-like scores for new data. We first project the data into the subspace defined by x$v, then either:

  1. LDA approach (method="lda"), which uses a (simplified) linear discriminant formula or distance to class means in the subspace combined with prior probabilities.

  2. Euclid approach (method="euclid"), which uses plain Euclidean distance to each class mean in the subspace.

We return either a type="class" label or type="prob" posterior-like matrix.

Usage

# S3 method for class 'discriminant_projector'
predict(
  object,
  new_data,
  method = c("lda", "euclid"),
  type = c("class", "prob"),
  colind = NULL,
  ...
)

Arguments

object

A discriminant_projector object.

new_data

A numeric matrix (or vector) with the same # of columns as the original data (unless partial usage). Rows=observations, columns=features.

method

Either "lda" (the default) or "euclid" (nearest-mean).

type

"class" (default) for predicted class labels, or "prob" for posterior-like probabilities.

colind

(optional) if partial columns are used, specify which columns map to the subspace. If NULL, assume full columns.

...

further arguments (not used or for future expansions).

Value

If type="class", a factor vector of length n (predicted classes). If type="prob", an (n x #classes) numeric matrix of posterior-like values, with row names matching new_data if available.

Predict method for a discriminant_projector

This produces class predictions or posterior-like scores for new data, based on:

  • LDA approach (method="lda"), which uses a linear discriminant formula with a pooled covariance matrix if x\$Sigma is given, or the identity matrix if Sigma=NULL. If that covariance matrix is not invertible, a pseudo-inverse is used and a warning is emitted.

  • Euclid approach (method="euclid"), which uses plain Euclidean distance to each class mean in the subspace.

We return either a type="class" label or type="prob" posterior-like matrix.

If type="class", a factor vector of length n (predicted classes). If type="prob", an (n x #classes) numeric matrix of posterior-like values.