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loess\n\nJavaScript implementation of the Locally-Weighted Regression package originally written in C by Cleveland, Grosse and Shyu (1992)\n\n## Getting started\n\nFirst install the package:\n```\nnpm install loess --save\n```\n\nLoad in your data:\n```javascript\nvar data = require('./myData.json')\n```\n\nInstantiate a LOESS model with the data:\n```javascript\nvar Loess = require('loess')\nvar options = {span: 0.5, band: 0.8, degree: 1}\nvar model = new Loess(data, option)\n```\n\nFit model by calling the **.predict( )** method on the model object:\n```javascript\nvar fit = model.predict()\nconsole.log(fit.fitted)\n// do something else with fit.fitted\n```\n\nTo fit model on a new set of points, pass a data object into **.predict( )**\n```javascript\nvar newData = {\n  x: [1, 2, 3, 4, 5],\n  x2: [6, 7, 8, 9, 10]\n}\n\nfit = model.predict(newData)\n\nvar upperLimit = fit.fitted.map((yhat, idx) => yhat + fit.halfwidth[idx])\nvar lowerLimit = fit.fitted.map((yhat, idx) => yhat - fit.halfwidth[idx])\n// plot upperLimit and lowerLimit\n```\n\nAlternatively, use **.grid( )** method to generate a grid of equally spaced points:\n```javascript\nnewData = model.grid([20, 20])\n\nfit = model.predict(newData)\n```\n\n***\n\n## Usage\n\n![screenshot1](./img/screenshot1.png)\n\n![screenshot2](./img/screenshot2.png)\n\n#### Find out more by visiting my demo app: <br>\n[https://loess.daburu.xyz/](https://loess.daburu.xyz/)\n\n***\n\n## Documentation\n\n```javascript\nclass Loess {\n  constructor (data: object, options: object) {\n    // arguments\n    data /*required*/ = {        \n      y: [number],\n      x: [number],\n      x2: [number], // optional\n      w: [number]  // optional\n    }\n\n    options /*optional*/ = {\n      span: number, // 0 to inf, default 0.75\n      band: number, // 0 to 1, default 0\n      degree: [0, 1, 2] || ['constant', 'linear', 'quadratic'] // default 2\n      normalize: boolean, // default true if degree > 1, false otherwise\n      robust: boolean, // default false\n      iterations: integer //default 4 if robust = true, 1 otherwise\n    }\n\n    // return a LOESS model object with the following properties\n    this.y = data.y\n    this.x = [data.x, data.x2] // predictor matrix\n    this.n = this.y.length // number of data points\n    this.d = this.x.length // dimension of predictors\n    this.bandwidth = options.span * this.n // number of data points used in local regression\n    this.options = options\n  }\n\n  predict (data: object) {\n    // arguments\n    data /*optional*/ = {        \n      x: [number],\n      x2: [number]\n    } // default this.x\n\n    return {\n      fitted: [number], // fitted values for the specified data points\n      halfwidth: [number] // fitted +- halfwidth is the uncertainty band\n    }\n  }\n\n  grid (cuts: [integer]) {\n    return {\n      x_cut: [number], // equally-spaced data points\n      x_cut2: [number],\n      x: [number], // all combination of x_cut and x_cut2, forming a grid\n      x2: [number]\n    }\n  }\n}\n```\n\n#### Note:\n\n- **data** should be passed into the constructor function as json with keys **y**, **x** and optionally **x2** and **w**. Values being the arrays of response, predictor variables, and observation weights.\n- If no data is supplied to **.predict( )** method, default is to perform fitting on the original dataset the model is constructed with.\n- **span** refers to the percentage number of neighboring points used in local regression.\n- **band** specifies how wide the uncertainty band should be. The higher the value, the greater number of points encompassed by the uncertainty band. Setting to 0 will return only **fitted** values.\n- By default LOESS model will perform local fitting using the quadratic function. Overwrite this by setting the **degree** option to \"linear\" or \"constant\". Lower degree fitting function computes faster.\n- For multivariate data, **normalize** option defaults to true. This means normalization is applied before performing proximity calculation. Data is transformed by dividing the factors by their 10% trimmed sample standard deviation. Turn off this option if dealing with geographical data.\n- Set **robust** option to true to turn on iterative robust fitting procedure. Applicable for estimates that have non-Gaussian errors. More **iterations** requires longer computation time.\n- When using **.grid( )**, cuts refers to the number of equally spaced points required along each axis.\n\n\n## Credits\n\nWilliam S. Cleveland, Susan J. Devlin <br>\n[Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting](http://www.stat.washington.edu/courses/stat527/s13/readings/Cleveland_Delvin_JASA_1988.pdf) <br>\nJournal of the American Statistical Association, Vol. 83, No. 403. (Sep., 1988), pp. 596-610.\n\nWilliam S. Cleveland, Eric Grosse, Ming-Jen Shyu <br>\n[A Package of C and Fortran Routines for Fitting Local Regression Models ](www.netlib.org/a/cloess.ps) (20 August 1992) <br>\nSource code available at [http://www.netlib.org/a/dloess](http://www.netlib.org/a/dloess)\n","maintainers":[{"name":"yongjun21","email":"yjthong.2009@smu.edu.sg"}],"time":{"modified":"2022-06-19T13:42:29.079Z","created":"2016-02-04T15:07:45.651Z","1.0.0":"2016-02-04T15:07:45.651Z","1.0.1":"2016-02-04T15:46:29.278Z","1.0.2":"2016-02-04T20:15:29.966Z","1.1.0":"2016-02-05T01:35:54.407Z","1.1.1":"2016-02-05T01:39:33.294Z","1.1.2":"2016-02-05T06:12:25.506Z","1.1.3":"2016-02-05T06:14:17.653Z","1.1.4":"2016-02-05T06:37:39.984Z","1.1.5":"2016-02-05T06:39:14.108Z","1.1.6":"2016-02-05T07:05:15.862Z","1.1.7":"2016-02-05T07:06:29.473Z","1.1.8":"2016-02-05T07:12:16.847Z","1.1.9":"2016-02-05T07:38:03.003Z","1.1.10":"2016-02-05T08:20:28.594Z","1.1.11":"2016-02-05T08:24:17.059Z","1.1.12":"2016-02-14T12:08:27.273Z","1.1.13":"2016-07-01T14:00:46.883Z","1.2.0":"2016-07-23T04:39:55.131Z","1.2.1":"2016-07-23T04:42:20.992Z","1.3.0":"2016-07-24T03:23:40.111Z","1.3.1":"2016-07-24T09:39:19.282Z","1.3.2":"2016-08-03T16:12:17.864Z","1.3.3":"2016-08-03T16:13:01.570Z","1.3.4":"2016-08-03T16:38:34.445Z","1.3.5":"2017-12-19T02:24:22.356Z"},"homepage":"https://github.com/yongjun21/loess#readme","keywords":["statistic","regression","smoothing","fitting","graph","plot","utility"],"repository":{"type":"git","url":"git+https://github.com/yongjun21/loess.git"},"author":{"name":"Yong Jun"},"bugs":{"url":"https://github.com/yongjun21/loess/issues"},"license":"ISC","readmeFilename":"README.md","users":{"jaggedsoft":true}}