<?php
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namespace PhpOffice\PhpSpreadsheet\Shared\Trend;
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abstract class BestFit
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{
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/**
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* Indicator flag for a calculation error.
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*/
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protected bool $error = false;
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/**
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* Algorithm type to use for best-fit.
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*/
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protected string $bestFitType = 'undetermined';
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/**
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* Number of entries in the sets of x- and y-value arrays.
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*/
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protected int $valueCount;
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/**
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* X-value dataseries of values.
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*
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* @var float[]
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*/
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protected array $xValues = [];
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/**
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* Y-value dataseries of values.
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*
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* @var float[]
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*/
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protected array $yValues = [];
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/**
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* Flag indicating whether values should be adjusted to Y=0.
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*/
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protected bool $adjustToZero = false;
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/**
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* Y-value series of best-fit values.
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*
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* @var float[]
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*/
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protected array $yBestFitValues = [];
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protected float $goodnessOfFit = 1;
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protected float $stdevOfResiduals = 0;
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protected float $covariance = 0;
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protected float $correlation = 0;
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protected float $SSRegression = 0;
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protected float $SSResiduals = 0;
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protected float $DFResiduals = 0;
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protected float $f = 0;
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protected float $slope = 0;
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protected float $slopeSE = 0;
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protected float $intersect = 0;
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protected float $intersectSE = 0;
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protected float $xOffset = 0;
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protected float $yOffset = 0;
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public function getError(): bool
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{
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return $this->error;
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}
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public function getBestFitType(): string
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{
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return $this->bestFitType;
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}
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/**
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* Return the Y-Value for a specified value of X.
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*
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* @param float $xValue X-Value
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*
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* @return float Y-Value
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*/
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abstract public function getValueOfYForX(float $xValue): float;
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/**
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* Return the X-Value for a specified value of Y.
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*
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* @param float $yValue Y-Value
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*
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* @return float X-Value
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*/
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abstract public function getValueOfXForY(float $yValue): float;
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/**
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* Return the original set of X-Values.
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*
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* @return float[] X-Values
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*/
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public function getXValues(): array
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{
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return $this->xValues;
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}
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/**
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* Return the Equation of the best-fit line.
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*
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* @param int $dp Number of places of decimal precision to display
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*/
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abstract public function getEquation(int $dp = 0): string;
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/**
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* Return the Slope of the line.
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*
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* @param int $dp Number of places of decimal precision to display
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*/
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public function getSlope(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->slope, $dp);
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}
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return $this->slope;
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}
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/**
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* Return the standard error of the Slope.
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*
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* @param int $dp Number of places of decimal precision to display
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*/
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public function getSlopeSE(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->slopeSE, $dp);
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}
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return $this->slopeSE;
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}
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/**
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* Return the Value of X where it intersects Y = 0.
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*
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* @param int $dp Number of places of decimal precision to display
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*/
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public function getIntersect(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->intersect, $dp);
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}
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return $this->intersect;
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}
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/**
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* Return the standard error of the Intersect.
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*
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* @param int $dp Number of places of decimal precision to display
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*/
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public function getIntersectSE(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->intersectSE, $dp);
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}
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return $this->intersectSE;
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}
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/**
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* Return the goodness of fit for this regression.
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*
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getGoodnessOfFit(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->goodnessOfFit, $dp);
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}
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return $this->goodnessOfFit;
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}
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/**
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* Return the goodness of fit for this regression.
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*
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getGoodnessOfFitPercent(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->goodnessOfFit * 100, $dp);
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}
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return $this->goodnessOfFit * 100;
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}
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/**
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* Return the standard deviation of the residuals for this regression.
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*
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getStdevOfResiduals(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->stdevOfResiduals, $dp);
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}
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return $this->stdevOfResiduals;
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}
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/**
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getSSRegression(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->SSRegression, $dp);
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}
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return $this->SSRegression;
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}
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/**
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getSSResiduals(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->SSResiduals, $dp);
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}
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return $this->SSResiduals;
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}
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/**
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getDFResiduals(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->DFResiduals, $dp);
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}
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return $this->DFResiduals;
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}
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/**
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getF(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->f, $dp);
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}
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return $this->f;
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}
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/**
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getCovariance(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->covariance, $dp);
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}
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return $this->covariance;
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}
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/**
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* @param int $dp Number of places of decimal precision to return
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*/
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public function getCorrelation(int $dp = 0): float
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{
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if ($dp != 0) {
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return round($this->correlation, $dp);
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}
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return $this->correlation;
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}
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/**
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* @return float[]
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*/
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public function getYBestFitValues(): array
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{
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return $this->yBestFitValues;
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}
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protected function calculateGoodnessOfFit(float $sumX, float $sumY, float $sumX2, float $sumY2, float $sumXY, float $meanX, float $meanY, bool|int $const): void
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{
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$SSres = $SScov = $SStot = $SSsex = 0.0;
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foreach ($this->xValues as $xKey => $xValue) {
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$bestFitY = $this->yBestFitValues[$xKey] = $this->getValueOfYForX($xValue);
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$SSres += ($this->yValues[$xKey] - $bestFitY) * ($this->yValues[$xKey] - $bestFitY);
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if ($const === true) {
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$SStot += ($this->yValues[$xKey] - $meanY) * ($this->yValues[$xKey] - $meanY);
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} else {
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$SStot += $this->yValues[$xKey] * $this->yValues[$xKey];
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}
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$SScov += ($this->xValues[$xKey] - $meanX) * ($this->yValues[$xKey] - $meanY);
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if ($const === true) {
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$SSsex += ($this->xValues[$xKey] - $meanX) * ($this->xValues[$xKey] - $meanX);
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} else {
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$SSsex += $this->xValues[$xKey] * $this->xValues[$xKey];
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}
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}
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$this->SSResiduals = $SSres;
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$this->DFResiduals = $this->valueCount - 1 - ($const === true ? 1 : 0);
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if ($this->DFResiduals == 0.0) {
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$this->stdevOfResiduals = 0.0;
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} else {
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$this->stdevOfResiduals = sqrt($SSres / $this->DFResiduals);
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}
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if ($SStot == 0.0 || $SSres == $SStot) {
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$this->goodnessOfFit = 1;
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} else {
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$this->goodnessOfFit = 1 - ($SSres / $SStot);
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}
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$this->SSRegression = $this->goodnessOfFit * $SStot;
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$this->covariance = $SScov / $this->valueCount;
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$this->correlation = ($this->valueCount * $sumXY - $sumX * $sumY) / sqrt(($this->valueCount * $sumX2 - $sumX ** 2) * ($this->valueCount * $sumY2 - $sumY ** 2));
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$this->slopeSE = $this->stdevOfResiduals / sqrt($SSsex);
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$this->intersectSE = $this->stdevOfResiduals * sqrt(1 / ($this->valueCount - ($sumX * $sumX) / $sumX2));
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if ($this->SSResiduals != 0.0) {
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if ($this->DFResiduals == 0.0) {
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$this->f = 0.0;
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} else {
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$this->f = $this->SSRegression / ($this->SSResiduals / $this->DFResiduals);
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}
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} else {
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if ($this->DFResiduals == 0.0) {
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$this->f = 0.0;
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} else {
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$this->f = $this->SSRegression / $this->DFResiduals;
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}
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}
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}
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/** @return float|int */
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private function sumSquares(array $values)
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{
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return array_sum(
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array_map(
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fn ($value): float|int => $value ** 2,
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$values
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)
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);
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}
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/**
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* @param float[] $yValues
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* @param float[] $xValues
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*/
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protected function leastSquareFit(array $yValues, array $xValues, bool $const): void
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{
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// calculate sums
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$sumValuesX = array_sum($xValues);
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$sumValuesY = array_sum($yValues);
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$meanValueX = $sumValuesX / $this->valueCount;
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$meanValueY = $sumValuesY / $this->valueCount;
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$sumSquaresX = $this->sumSquares($xValues);
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$sumSquaresY = $this->sumSquares($yValues);
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$mBase = $mDivisor = 0.0;
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$xy_sum = 0.0;
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for ($i = 0; $i < $this->valueCount; ++$i) {
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$xy_sum += $xValues[$i] * $yValues[$i];
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if ($const === true) {
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$mBase += ($xValues[$i] - $meanValueX) * ($yValues[$i] - $meanValueY);
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$mDivisor += ($xValues[$i] - $meanValueX) * ($xValues[$i] - $meanValueX);
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} else {
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$mBase += $xValues[$i] * $yValues[$i];
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$mDivisor += $xValues[$i] * $xValues[$i];
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}
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}
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// calculate slope
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$this->slope = $mBase / $mDivisor;
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// calculate intersect
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$this->intersect = ($const === true) ? $meanValueY - ($this->slope * $meanValueX) : 0.0;
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$this->calculateGoodnessOfFit($sumValuesX, $sumValuesY, $sumSquaresX, $sumSquaresY, $xy_sum, $meanValueX, $meanValueY, $const);
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}
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/**
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* Define the regression.
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*
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* @param float[] $yValues The set of Y-values for this regression
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* @param float[] $xValues The set of X-values for this regression
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*/
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public function __construct(array $yValues, array $xValues = [])
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{
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// Calculate number of points
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$yValueCount = count($yValues);
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$xValueCount = count($xValues);
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// Define X Values if necessary
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if ($xValueCount === 0) {
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$xValues = range(1, $yValueCount);
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} elseif ($yValueCount !== $xValueCount) {
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// Ensure both arrays of points are the same size
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$this->error = true;
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}
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$this->valueCount = $yValueCount;
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$this->xValues = $xValues;
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$this->yValues = $yValues;
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}
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}
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