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Stevens","url":"http://harryjstevens.com/"},"license":"BSD-3-Clause","homepage":"https://github.com/HarryStevens/d3-regression#readme","keywords":["d3","d3-module","statistics","regressions"],"repository":{"type":"git","url":"git+https://github.com/HarryStevens/d3-regression.git"},"description":"Calculate statistical regressions for two-dimensional data","maintainers":[{"name":"harrystevens","email":"harryjosephstevens@gmail.com"}],"readme":"# d3-regression\nCalculate statistical regressions from two-dimensional data.\n\n[<img alt=\"Statistical Regressions\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/cover.svg\" width=\"882\">](https://observablehq.com/@harrystevens/introducing-d3-regression)\n\n## Installing\nIf you use NPM, `npm install d3-regression`.\n\n```js\nimport { regressionLinear } from \"d3-regression\";\n\nconst regression = regressionLinear()\n  .x(d => d.x)\n  .y(d => d.y)\n  .domain([0, 100]);\n```\n\nYou can also load the UMD bundle from a CDN; in vanilla, a d3 global is exported:\n\n```html\n<script src=\"https://unpkg.com/d3-regression@2/dist/d3-regression.min.js\"></script>\n<script>\n\nconst regression = d3.regressionLinear()\n  .x(d => d.x)\n  .y(d => d.y)\n  .domain([0, 100]);\n\n</script>\n```\n\n## API Reference\nAll regression generators accept any iterable of data, including arrays, Maps, and Sets. Maps work with the default accessors when their entries are two-element [<em>x</em>, <em>y</em>] pairs; Sets work with the default accessors when their values are two-element [<em>x</em>, <em>y</em>] arrays.\n\n- [Linear](#regressionLinear)\n- [Exponential](#regressionExp)\n- [Logarithmic](#regressionLog)\n- [Quadratic](#regressionQuad)\n- [Polynomial](#regressionPoly)\n- [Power law](#regressionPow)\n- [LOESS](#regressionLoess)\n- [Logistic](#regressionLogistic)\n- [Sigmoidal](#regressionSigmoidal)\n\n<a name=\"regressionLinear\" href=\"#regressionLinear\">#</a> d3.<b>regressionLinear</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/linear.js \"Source\"), [Example](https://observablehq.com/@harrystevens/linear-regression \"Example\")\n\nCreates a new linear regression generator with default [<em>x</em>-](#linear_x) and [<em>y</em>-](#linear_y) accessors and a null [domain](#linear_domain).\n\n[<img alt=\"Linear regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/linear.svg\" width=\"250\">](https://observablehq.com/@harrystevens/linear-regression)\n\n<a name=\"_linear\" href=\"#_linear\">#</a> <i>linear</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/linear.js#L9 \"Source\")\n\nComputes the linear regression, which takes the form <em>y</em> = <em>ax</em> + <em>b</em>, for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity).\n\nReturns a line represented as an array of two points, where each point is an array of two numbers representing the point's coordinates.\n\nAlso returns properties <em>a</em> and <em>b</em>, representing the equation's coefficients, and <em>rSquared</em>, representing the coefficient of determination. Lastly, returns a <em>predict</em> property, which is a function that outputs a <em>y</em>-coordinate given an input <em>x</em>-coordinate.\n\n<a name=\"linear_x\" href=\"#linear_x\">#</a> <i>linear</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/linear.js#L47 \"Source\")\n\nIf <em>x</em> is specified, sets the <em>x</em>-coordinate accessor, which is passed the current datum (<em>d</em>), the current index (<em>i</em>), and the entire data array (<em>data</em>). If <em>x</em> is not specified, returns the current <em>x</em>-coordinate accessor, which defaults to:\n\n```js\nfunction x(d, i, data) {\n  return d[0];\n}\n```\n\n<a name=\"linear_y\" href=\"#linear_y\">#</a> <i>linear</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/linear.js#L51 \"Source\")\n\nIf <em>y</em> is specified, sets the <em>y</em>-coordinate accessor, which is passed the current datum (<em>d</em>), the current index (<em>i</em>), and the entire data array (<em>data</em>). If <em>y</em> is not specified, returns the current <em>y</em>-coordinate accessor, which defaults to:\n\n```js\nfunction y(d, i, data) {\n  return d[1];\n}\n```\n\n<a name=\"linear_domain\" href=\"#linear_domain\">#</a> <i>linear</i>.<b>domain</b>([<i>domain</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/linear.js#L43 \"Source\")\n\nIf <em>domain</em> is specified, sets the minimum and maximum <em>x</em>-coordinates of the returned line to the specified array of numbers. The array must contain two elements. If the elements in the given array are not numbers, they will be coerced to numbers. If <em>domain</em> is not specified, returns a copy of the regression generator’s current domain.\n\nIf data is passed to the regression generator before a <em>domain</em> has been specified, the domain will be set to the minimum and maximum <em>x</em>-coordinate values of the data.\n\n<a name=\"regressionExp\" href=\"#regressionExp\">#</a> d3.<b>regressionExp</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/exponential.js \"Source\"), [Example](https://observablehq.com/@harrystevens/exponential-regression \"Example\")\n\nCreates a new exponential regression generator with default [<em>x</em>-](#exp_x) and [<em>y</em>-](#exp_y) accessors and a null [domain](#exp_domain).\n\n[<img alt=\"Exponential regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/exponential.svg\" width=\"250\">](https://observablehq.com/@harrystevens/exponential-regression)\n\n<a name=\"_exponential\" href=\"#_exponential\">#</a> <i>exp</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/exponential.js#L10 \"Source\")\n\nComputes the exponential regression, which takes the form <em>y</em> = <em>ae</em><sup><em>bx</em></sup>, for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity) and nonpositive <em>y</em>-values.\n\nReturns a smooth line represented as an array of points, where each point is an array of two numbers representing the point's coordinates.\n\nAlso returns properties <em>a</em> and <em>b</em>, representing the equation's coefficients, and <em>rSquared</em>, representing the coefficient of determination. Lastly, returns a <em>predict</em> property, which is a function that outputs a <em>y</em>-coordinate given an input <em>x</em>-coordinate.\n\n<a name=\"exp_x\" href=\"#exp_x\">#</a> <i>exp</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/exponential.js#L53 \"Source\")\n\nSee [<em>linear</em>.x()](#linear_x).\n\n<a name=\"exp_y\" href=\"#exp_y\">#</a> <i>exp</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/exponential.js#L57 \"Source\")\n\nSee [<em>linear</em>.y()](#linear_y).\n\n<a name=\"exp_domain\" href=\"#exp_domain\">#</a> <i>exp</i>.<b>domain</b>([<i>domain</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/exponential.js#L49 \"Source\")\n\nSee [<em>linear</em>.domain()](#linear_domain).\n\n<a name=\"regressionLog\" href=\"#regressionLog\">#</a> d3.<b>regressionLog</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logarithmic.js \"Source\"), [Example](https://observablehq.com/@harrystevens/logarithmic-regression \"Example\")\n\nCreates a new logarithmic regression generator with default [<em>x</em>-](#log_x) and [<em>y</em>-](#log_y) accessors and a null [domain](#log_domain).\n\n[<img alt=\"Logarithmic regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/logarithmic.svg\" width=\"250\">](https://observablehq.com/@harrystevens/logarithmic-regression)\n\n<a name=\"_log\" href=\"#_log\">#</a> <i>log</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logarithmic.js#L12 \"Source\")\n\nComputes the logarithmic regression, which takes the form <em>y</em> = <em>a</em> · ln(<em>x</em>) + <em>b</em>, for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity) and nonpositive <em>x</em>-values.\n\nReturns a smooth line represented as an array of points, where each point is an array of two numbers representing the point's coordinates.\n\nAlso returns properties <em>a</em> and <em>b</em>, representing the equation's coefficients, and <em>rSquared</em>, representing the coefficient of determination. Lastly, returns a <em>predict</em> property, which is a function that outputs a <em>y</em>-coordinate given an input <em>x</em>-coordinate.\n\n<a name=\"log_x\" href=\"#log_x\">#</a> <i>log</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logarithmic.js#L52 \"Source\")\n\nSee [<em>linear</em>.x()](#linear_x).\n\n<a name=\"log_y\" href=\"#log_y\">#</a> <i>log</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logarithmic.js#L56 \"Source\")\n\nSee [<em>linear</em>.y()](#linear_y).\n\n<a name=\"log_domain\" href=\"#log_domain\">#</a> <i>log</i>.<b>domain</b>([<i>domain</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logarithmic.js#L48 \"Source\")\n\nSee [<em>linear</em>.domain()](#linear_domain).\n\n<a name=\"log_base\" href=\"#log_base\">#</a> <i>log</i>.<b>base</b>([<i>base</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logarithmic.js#L60 \"Source\")\n\nIf <i>base</i> is specified, sets the base of the logarithmic regression. If <i>base</i> is not specified, returns the current base, which defaults to [Euler's number](https://en.wikipedia.org/wiki/E_(mathematical_constant)).\n\n<a name=\"regressionQuad\" href=\"#regressionQuad\">#</a> d3.<b>regressionQuad</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/quadratic.js \"Source\"), [Example](https://observablehq.com/@harrystevens/quadratic-regression \"Example\")\n\nCreates a new quadratic regression generator with default [<em>x</em>-](#quad_x) and [<em>y</em>-](#quad_y) accessors and a null [domain](#quad_domain).\n\n[<img alt=\"Quadratic regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/quadratic.svg\" width=\"250\">](https://observablehq.com/@harrystevens/quadratic-regression)\n\n<a name=\"_quad\" href=\"#_quad\">#</a> <i>quad</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/quadratic.js#L9 \"Source\")\n\nComputes the quadratic regression, which takes the form <em>y</em> = <em>ax</em><sup>2</sup> + <em>bx</em> + <em>c</em>, for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity).\n\nReturns a smooth line represented as an array of points, where each point is an array of two numbers representing the point's coordinates.\n\nAlso returns properties <em>a</em>, <em>b</em>, and <em>c</em>, representing the equation's coefficients, and <em>rSquared</em>, representing the coefficient of determination. Lastly, returns a <em>predict</em> property, which is a function that outputs a <em>y</em>-coordinate given an input <em>x</em>-coordinate.\n\n<a name=\"quad_x\" href=\"#quad_x\">#</a> <i>quad</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/quadratic.js#L71 \"Source\")\n\nSee [<em>linear</em>.x()](#linear_x).\n\n<a name=\"quad_y\" href=\"#quad_y\">#</a> <i>quad</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/quadratic.js#L75 \"Source\")\n\nSee [<em>linear</em>.y()](#linear_y).\n\n<a name=\"quad_domain\" href=\"#quad_domain\">#</a> <i>quad</i>.<b>domain</b>([<i>domain</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/quadratic.js#L67 \"Source\")\n\nSee [<em>linear</em>.domain()](#linear_domain).\n\n<a name=\"regressionPoly\" href=\"#regressionPoly\">#</a> d3.<b>regressionPoly</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/polynomial.js \"Source\"), [Example](https://observablehq.com/@harrystevens/polynomial-regression \"Example\")\n\nCreates a new polynomial regression generator with default [<em>x</em>-](#poly_x) and [<em>y</em>-](#poly_y) accessors, a null [domain](#poly_domain), and an [order](#poly_order) of 3. This implementation was adapted from [regression-js](https://github.com/Tom-Alexander/regression-js/).\n\n[<img alt=\"Polynomial regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/polynomial.svg\" width=\"250\">](https://observablehq.com/@harrystevens/polynomial-regression)\n\n<a name=\"_poly\" href=\"#_poly\">#</a> <i>poly</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/polynomial.js#L13 \"Source\")\n\nComputes the polynomial regression, which takes the form <em>y</em> = <em>a<sub>n</sub>x<sup>n</sup></em> + ... + <em>a</em><sub>1</sub><em>x</em> + <em>a</em><sub>0</sub>, for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity).\n\nReturns a smooth line represented as an array of points, where each point is an array of two numbers representing the point's coordinates.\n\nAlso returns three properties: <em>coefficients</em>, an array representing the equation's coefficients with the intercept as the first item and nth degree coefficient as the last item; <em>rSquared</em>, representing the coefficient of determination; and <em>predict</em>, a function that outputs a <em>y</em>-coordinate given an input <em>x</em>-coordinate.\n\n<a name=\"poly_x\" href=\"#poly_x\">#</a> <i>poly</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/polynomial.js#L92 \"Source\")\n\nSee [<em>linear</em>.x()](#linear_x).\n\n<a name=\"poly_y\" href=\"#poly_y\">#</a> <i>poly</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/polynomial.js#L96 \"Source\")\n\nSee [<em>linear</em>.y()](#linear_y).\n\n<a name=\"poly_domain\" href=\"#poly_domain\">#</a> <i>poly</i>.<b>domain</b>([<i>domain</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/polynomial.js#L88 \"Source\")\n\nSee [<em>linear</em>.domain()](#linear_domain).\n\n<a name=\"poly_order\" href=\"#poly_order\">#</a> <i>poly</i>.<b>order</b>([<i>order</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/polynomial.js#L100 \"Source\")\n\nIf <em>order</em> is specified, sets the regression's order to the specified number. For example, if <em>order</em> is set to 4, the regression generator will perform a fourth-degree polynomial regression. Likewise, if <em>order</em> is set to 2, the regression generator will perform a quadratic regression. Be careful about attempting to fit your data with higher order polynomials; though the regression line will fit your data with a high determination coefficient, it may have little predictive power for data outside of your domain.\n\nIf <em>order</em> is not specified, returns the regression generator's current <em>order</em>, which defaults to 3.\n\n<a name=\"regressionPow\" href=\"#regressionPow\">#</a> d3.<b>regressionPow</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/power.js \"Source\"), [Example](https://observablehq.com/@harrystevens/power-law-regression \"Example\")\n\nCreates a new power law regression generator with default [<em>x</em>-](#pow_x) and [<em>y</em>-](#pow_y) accessors and a null [domain](#pow_domain).\n\n[<img alt=\"Power law regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/power.svg\" width=\"250\">](https://observablehq.com/@harrystevens/power-law-regression)\n\n<a name=\"_pow\" href=\"#_pow\">#</a> <i>pow</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/power.js#L10 \"Source\")\n\nComputes the power law regression, which takes the form <em>y</em> = <em>a</em><em>x</em><sup><em>b</em></sup>, for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity) and nonpositive <em>x</em>- or <em>y</em>-values.\n\nReturns a smooth line represented as an array of points, where each point is an array of two numbers representing the point's coordinates.\n\nAlso returns properties <em>a</em> and <em>b</em>, representing the equation's coefficients, and <em>rSquared</em>, representing the coefficient of determination. Lastly, returns a <em>predict</em> property, which is a function that outputs a <em>y</em>-coordinate given an input <em>x</em>-coordinate.\n\n<a name=\"pow_x\" href=\"#pow_x\">#</a> <i>pow</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/power.js#L54 \"Source\")\n\nSee [<em>linear</em>.x()](#linear_x).\n\n<a name=\"pow_y\" href=\"#pow_y\">#</a> <i>pow</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/power.js#L58 \"Source\")\n\nSee [<em>linear</em>.y()](#linear_y).\n\n<a name=\"pow_domain\" href=\"#pow_domain\">#</a> <i>pow</i>.<b>domain</b>([<i>domain</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/power.js#L50 \"Source\")\n\nSee [<em>linear</em>.domain()](#linear_domain).\n\n<a name=\"regressionLoess\" href=\"#regressionLoess\">#</a> d3.<b>regressionLoess</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/loess.js \"Source\"), [Example](https://observablehq.com/@harrystevens/loess-regression \"Example\")\n\nCreates a new [LOESS regression](https://en.wikipedia.org/wiki/Local_regression) generator with default [<em>x</em>-](#loess_x) and [<em>y</em>-](#loess_y) accessors and a [bandwidth](#loess_bandwidth) of .3. This implementation was adapted from [science.js](https://github.com/jasondavies/science.js).\n\n[<img alt=\"LOESS regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/loess.svg\" width=\"250\">](https://observablehq.com/@harrystevens/loess-regression)\n\n<a name=\"_loess\" href=\"#_loess\">#</a> <i>loess</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/loess.js#L14 \"Source\")\n\nComputes the LOESS regression for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity).\n\nReturns a line represented as an array of <em>n</em> points, where each point is an array of two numbers representing the point's coordinates. Also returns <em>rSquared</em>, representing the coefficient of determination for the smoothed fitted values, and a <em>predict</em> property, which is a function that outputs a <em>y</em>-coordinate for a given <em>x</em>-coordinate by interpolating along the smoothed regression line.\n\n<a name=\"loess_x\" href=\"#loess_x\">#</a> <i>loess</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/loess.js#L81 \"Source\")\n\nSee [<em>linear</em>.x()](#linear_x).\n\n<a name=\"loess_y\" href=\"#loess_y\">#</a> <i>loess</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/loess.js#L85 \"Source\")\n\nSee [<em>linear</em>.y()](#linear_y).\n\n<a name=\"loess_bandwidth\" href=\"#loess_bandwidth\">#</a> <i>loess</i>.<b>bandwidth</b>([<i>bandwidth</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/loess.js#L77 \"Source\")\n\nIf <em>bandwidth</em> is specified, sets the LOESS regression's bandwidth, or smoothing parameter, to the specific number between 0 and 1. The bandwidth represents the share of the total data points that are used to calculate each local fit. Higher bandwidths produce smoother lines, and vice versa. If <em>bandwidth</em> is not specified, returns a copy of the regression generator’s current bandwidth, which defaults to .3.\n\n<a name=\"regressionLogistic\" href=\"#regressionLogistic\">#</a> d3.<b>regressionLogistic</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logistic.js \"Source\"), [Example](https://observablehq.com/@harrystevens/logistic-regression \"Example\")\n\nCreates a new logistic regression generator with default [<em>x</em>-](#logistic_x) and [<em>y</em>-](#logistic_y) accessors and a null [domain](#logistic_domain). Use [sigmoidal regression](#regressionSigmoidal) instead when the curve has a nonzero lower asymptote.\n\n[<img alt=\"Logistic regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/logistic.svg\" width=\"250\">](https://observablehq.com/@harrystevens/logistic-regression)\n\n<a name=\"_logistic\" href=\"#_logistic\">#</a> <i>logistic</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logistic.js#L20 \"Source\")\n\nComputes the logistic regression, which takes the form <em>y</em> = <em>a</em> / (1 + <em>e</em><sup>-<em>b</em>(<em>x</em> - <em>c</em>)</sup>), for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity) and nonpositive <em>y</em>-values. This is the right choice for logistic growth or decay whose lower asymptote is zero; for shifted sigmoid curves of the form <em>a</em> + <em>b</em> / (1 + <em>e</em><sup>-<em>c</em>(<em>x</em> - <em>d</em>)</sup>), use [regressionSigmoidal](#regressionSigmoidal).\n\nReturns a smooth line represented as an array of points, where each point is an array of two numbers representing the point's coordinates.\n\nAlso returns properties <em>a</em>, <em>b</em>, and <em>c</em>, representing the equation's coefficients, and <em>rSquared</em>, representing the coefficient of determination. Lastly, returns a <em>predict</em> property, which is a function that outputs a <em>y</em>-coordinate given an input <em>x</em>-coordinate.\n\n<a name=\"logistic_x\" href=\"#logistic_x\">#</a> <i>logistic</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logistic.js#L138 \"Source\")\n\nSee [<em>linear</em>.x()](#linear_x).\n\n<a name=\"logistic_y\" href=\"#logistic_y\">#</a> <i>logistic</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logistic.js#L142 \"Source\")\n\nSee [<em>linear</em>.y()](#linear_y).\n\n<a name=\"logistic_domain\" href=\"#logistic_domain\">#</a> <i>logistic</i>.<b>domain</b>([<i>domain</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/logistic.js#L134 \"Source\")\n\nSee [<em>linear</em>.domain()](#linear_domain).\n\n<a name=\"regressionSigmoidal\" href=\"#regressionSigmoidal\">#</a> d3.<b>regressionSigmoidal</b>() · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/sigmoidal.js \"Source\"), [Example](https://observablehq.com/@harrystevens/sigmoidal-regression \"Example\")\n\nCreates a new sigmoidal regression generator with default [<em>x</em>-](#sigmoidal_x) and [<em>y</em>-](#sigmoidal_y) accessors and a null [domain](#sigmoidal_domain). Use [logistic regression](#regressionLogistic) instead when the lower asymptote is known to be zero.\n\n[<img alt=\"Sigmoidal regression\" src=\"https://raw.githubusercontent.com/harrystevens/d3-regression/main/img/sigmoidal.svg\" width=\"250\">](https://observablehq.com/@harrystevens/sigmoidal-regression)\n\n<a name=\"_sigmoidal\" href=\"#_sigmoidal\">#</a> <i>sigmoidal</i>(<i>data</i>) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/sigmoidal.js#L20 \"Source\")\n\nComputes the sigmoidal regression, which takes the form <em>y</em> = <em>a</em> + <em>b</em> / (1 + <em>e</em><sup>-<em>c</em>(<em>x</em> - <em>d</em>)</sup>), for the specified *data* points, ignoring points with invalid values (null, undefined, NaN, Infinity). This four-parameter model fits shifted sigmoid curves with a baseline offset; for ordinary logistic growth or decay from zero to <em>a</em>, use [regressionLogistic](#regressionLogistic).\n\nReturns a smooth line represented as an array of points, where each point is an array of two numbers representing the point's coordinates.\n\nAlso returns properties <em>a</em>, <em>b</em>, <em>c</em>, and <em>d</em>, representing the equation's coefficients, and <em>rSquared</em>, representing the coefficient of determination. Lastly, returns a <em>predict</em> property, which is a function that outputs a <em>y</em>-coordinate given an input <em>x</em>-coordinate.\n\n<a name=\"sigmoidal_x\" href=\"#sigmoidal_x\">#</a> <i>sigmoidal</i>.<b>x</b>([<i>x</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/sigmoidal.js#L72 \"Source\")\n\nSee [<em>linear</em>.x()](#linear_x).\n\n<a name=\"sigmoidal_y\" href=\"#sigmoidal_y\">#</a> <i>sigmoidal</i>.<b>y</b>([<i>y</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/sigmoidal.js#L76 \"Source\")\n\nSee [<em>linear</em>.y()](#linear_y).\n\n<a name=\"sigmoidal_domain\" href=\"#sigmoidal_domain\">#</a> <i>sigmoidal</i>.<b>domain</b>([<i>domain</i>]) · [Source](https://github.com/harrystevens/d3-regression/blob/main/src/sigmoidal.js#L68 \"Source\")\n\nSee [<em>linear</em>.domain()](#linear_domain).\n","readmeFilename":"README.md"}