PCA

Defined in: ds/src/mva/estimators/PCA.js:30

Extends

Transformer

Constructors

Constructor

new PCA(params?): PCA

Defined in: ds/src/mva/estimators/PCA.js:31

Parameters

params?

Returns

PCA

Overrides

Transformer.constructor

Properties

_state

_state: object

Defined in: ds/src/core/estimators/estimator.js:27

Inherited from

Transformer._state

_warnings

_warnings: any[]

Defined in: ds/src/core/estimators/estimator.js:29

Inherited from

Transformer._warnings

fitted

fitted: boolean

Defined in: ds/src/core/estimators/estimator.js:25

Inherited from

Transformer.fitted

model

model: any

Defined in: ds/src/mva/estimators/PCA.js:35

params

params: object

Defined in: ds/src/mva/estimators/PCA.js:34

center

center: boolean = true

columns

columns: any = null

omit_missing

omit_missing: boolean = true

scale

scale: boolean = false

scaling

scaling: number = 2

Inherited from

Transformer.params

Methods

_prepareArgsForFit()

_prepareArgsForFit(args?): { columns?: undefined; columnsX: any[]; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y: any[]; } | { columns: any[]; columnsX?: undefined; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y?: undefined; } | { columns?: undefined; columnsX?: undefined; prepared?: undefined; raw: any[]; rows?: undefined; X?: undefined; y?: undefined; }

Defined in: ds/src/core/estimators/estimator.js:367

Convenience helper: parse arguments passed to fit/predict/transform.

Supports declarative table-style inputs:

fit({ X, y, data, omit_missing })
fit({ data, columns, … })

Returns an object { X, y, prepared, rows } where X/y are numeric arrays if preparation was required, otherwise returns the original values.

Note: this helper only prepares numeric matrices/vectors using core table utilities; it does not perform encoding of categorical predictors.

Parameters

args?

any[] = []

Returns

{ columns?: undefined; columnsX: any[]; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y: any[]; } | { columns: any[]; columnsX?: undefined; prepared: boolean; raw?: undefined; rows: any[]; X: any[][]; y?: undefined; } | { columns?: undefined; columnsX?: undefined; prepared?: undefined; raw: any[]; rows?: undefined; X?: undefined; y?: undefined; }

Inherited from

Transformer._prepareArgsForFit

_repr_html_()

_repr_html_(): string

Defined in: ds/src/core/estimators/estimator.js:201

Observable/Jupyter HTML representation

Returns

string

HTML representation

Inherited from

Transformer._repr_html_

clearWarnings()

clearWarnings(): void

Defined in: ds/src/core/estimators/estimator.js:139

Clear all warnings

Returns

void

Inherited from

Transformer.clearWarnings

cumulativeVariance()

cumulativeVariance(): number[]

Defined in: ds/src/mva/estimators/PCA.js:125

Helper to expose functional cumulative variance.

Returns

number[]

fit()

fit(X, opts?): PCA

Defined in: ds/src/mva/estimators/PCA.js:52

Fit PCA on the provided data.

Accepts either a numeric matrix (fit(X[, opts])) or a declarative { data, columns } object (fit({ data, columns, center, scale, omit_missing })).

Parameters

X

any

Data matrix (n samples × p features), or a declarative { data, columns } object

opts?

Fitting options (used for the numeric-matrix call form)

center?

boolean

Whether to mean-center the columns

columns?

string[]

Column names to use for declarative inputs

omit_missing?

boolean

Whether to drop rows with missing values

scale?

boolean

Whether to scale columns to unit variance

scaling?

number

Ordination scaling convention

Returns

PCA

The fitted estimator (for chaining)

Overrides

Transformer.fit

fitTransform()

fitTransform(…args): void

Defined in: ds/src/core/estimators/estimator.js:683

Convenience: fit then transform Returns transformed data.

Parameters

args

…any[]

Returns

void

Inherited from

Transformer.fitTransform

getMemoryUsage()

getMemoryUsage(): string

Defined in: ds/src/core/estimators/estimator.js:97

Get memory usage in human-readable format

Returns

string

Memory usage string (e.g., “2.3 MB” or “145 KB”)

Inherited from

Transformer.getMemoryUsage

getParams()

getParams(): any

Defined in: ds/src/core/estimators/estimator.js:294

Get a shallow copy of parameters.

Returns

any

Inherited from

Transformer.getParams

getScores()

getScores(type?, scaled?): any

Defined in: ds/src/mva/estimators/PCA.js:156

Retrieve site or variable scores with optional scaling.

Parameters

type?

"sites" | "samples" | "variables" | "loadings"

scaled?

boolean = true

return scaled or raw coordinates

Returns

any

getState()

getState(): any

Defined in: ds/src/core/estimators/estimator.js:65

Get comprehensive model state

Returns

any

State information including fitted status, memory estimate, warnings

Inherited from

Transformer.getState

getWarnings()

getWarnings(): any[]

Defined in: ds/src/core/estimators/estimator.js:124

Get all warnings

Returns

any[]

Array of warning objects

Inherited from

Transformer.getWarnings

getWarningsByType()

getWarningsByType(type): any[]

Defined in: ds/src/core/estimators/estimator.js:148

Get warnings of a specific type

Parameters

type

string

Warning type

Returns

any[]

Filtered warnings

Inherited from

Transformer.getWarningsByType

hasWarnings()

hasWarnings(): boolean

Defined in: ds/src/core/estimators/estimator.js:132

Check if model has warnings

Returns

boolean

Inherited from

Transformer.hasWarnings

isFitted()

isFitted(): boolean

Defined in: ds/src/core/estimators/estimator.js:36

Check if model is fitted

Returns

boolean

Inherited from

Transformer.isFitted

predict()

predict(): void

Defined in: ds/src/core/estimators/estimator.js:424

Predict should be implemented by supervised estimators.

Returns

void

Inherited from

Transformer.predict

save()

save(): string

Defined in: ds/src/core/estimators/estimator.js:329

Save model to JSON string

Returns

string

JSON representation of the model

Inherited from

Transformer.save

setParams()

setParams(params?): PCA

Defined in: ds/src/core/estimators/estimator.js:285

Set parameters (mutates instance).

Parameters

params?

any = {}

Returns

PCA

Inherited from

Transformer.setParams

summary()

summary(): object

Defined in: ds/src/mva/estimators/PCA.js:133

Provide lightweight summary of the fitted model.

Returns

object

centered

centered: boolean

cumulativeVariance

cumulativeVariance: number[]

eigenvalues

eigenvalues: any

means

means: any

nComponents

nComponents: any

scaled

scaled: boolean

scaling

scaling: number

sds

sds: any

varianceExplained

varianceExplained: any

toJSON()

toJSON(): object

Defined in: ds/src/mva/estimators/PCA.js:176

Serialization helper for saving estimator state.

Returns

object

class

__class__: string = 'PCA'

fitted

fitted: boolean

model

model: any

params

params: any

Overrides

Transformer.toJSON

transform()

transform(X): any[]

Defined in: ds/src/mva/estimators/PCA.js:99

Transform new data into principal-component scores using the fitted model.

Accepts numeric arrays or declarative table objects { data, columns }.

Parameters

X

any

Data matrix (n samples × p features), or a declarative { data, columns } object

Returns

any[]

Score objects, one per row, keyed by component (e.g. pc1, pc2, …)

Overrides

Transformer.transform

fromJSON()

static fromJSON(obj?): PCA

Defined in: ds/src/mva/estimators/PCA.js:188

Restore PCA instance from JSON produced by toJSON().

Parameters

obj?

Returns

PCA

Overrides

Transformer.fromJSON

load()

static load(jsonString): Estimator

Defined in: ds/src/core/estimators/estimator.js:346

Load model from JSON string

Parameters

jsonString

string

JSON representation

Returns

Estimator

Reconstructed estimator instance

Inherited from

Transformer.load