---
title: "Factor Analysis in JavaScript and TypeScript"
description: "Fit a latent Gaussian factor model with per-feature noise in browser or Node.js using @kanaries/ml FactorAnalysis."
canonical_url: "https://ml.kanaries.net/docs/apis/decomposition/factorAnalysis"
markdown_url: "https://ml.kanaries.net/docs/apis/decomposition/factorAnalysis.md"
---
# Factor analysis in JavaScript

## Algorithm overview

Factor analysis explains correlated observations with a smaller set of latent variables while estimating separate noise variance for every feature. It is useful when measurement noise matters more than PCA's total-variance objective.

## JavaScript implementation

`FactorAnalysis` runs the sklearn-style EM/SVD update in JavaScript and supports optional varimax or quartimax rotation.

## Quick start

```ts
import { Decomposition } from '@kanaries/ml';

const X = [[1, 2, 1], [2, 4, 1], [3, 6, 2], [4, 8, 2]];
const model = new Decomposition.FactorAnalysis({ nComponents: 2 });
const scores = model.fitTransform(X);
console.log(scores);
```

## Detailed API reference

Constructor options are `nComponents`, `tol`, `maxIter`, `noiseVarianceInit`, and `rotation`. Fitted state includes `components`, `mean`, `noiseVariance`, `loglike`, and `nIter`, plus `getCovariance()` and `getPrecision()`.