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<?php
namespace PhpOffice\PhpSpreadsheet\Calculation\Statistical;
use PhpOffice\PhpSpreadsheet\Calculation\ArrayEnabled;
use PhpOffice\PhpSpreadsheet\Calculation\Exception;
use PhpOffice\PhpSpreadsheet\Calculation\Functions;
use PhpOffice\PhpSpreadsheet\Calculation\Information\ExcelError;
use PhpOffice\PhpSpreadsheet\Shared\Trend\Trend;
class Trends
{
use ArrayEnabled;
private static function filterTrendValues(array &$array1, array &$array2): void
{
foreach ($array1 as $key => $value) {
if ((is_bool($value)) || (is_string($value)) || ($value === null)) {
unset($array1[$key], $array2[$key]);
}
}
}
private static function checkTrendArrays(&$array1, &$array2): void
{
if (!is_array($array1)) {
$array1 = [$array1];
}
if (!is_array($array2)) {
$array2 = [$array2];
}
$array1 = Functions::flattenArray($array1);
$array2 = Functions::flattenArray($array2);
self::filterTrendValues($array1, $array2);
self::filterTrendValues($array2, $array1);
// Reset the array indexes
$array1 = array_merge($array1);
$array2 = array_merge($array2);
}
protected static function validateTrendArrays(array $yValues, array $xValues): void
{
$yValueCount = count($yValues);
$xValueCount = count($xValues);
if (($yValueCount === 0) || ($yValueCount !== $xValueCount)) {
throw new Exception(ExcelError::NA());
} elseif ($yValueCount === 1) {
throw new Exception(ExcelError::DIV0());
}
}
/**
* CORREL.
*
* Returns covariance, the average of the products of deviations for each data point pair.
*
* @param mixed $yValues array of mixed Data Series Y
* @param null|mixed $xValues array of mixed Data Series X
*
* @return float|string
*/
public static function CORREL($yValues, $xValues = null)
{
if (($xValues === null) || (!is_array($yValues)) || (!is_array($xValues))) {
return ExcelError::VALUE();
}
try {
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear->getCorrelation();
}
/**
* COVAR.
*
* Returns covariance, the average of the products of deviations for each data point pair.
*
* @param mixed $yValues array of mixed Data Series Y
* @param mixed $xValues array of mixed Data Series X
*
* @return float|string
*/
public static function COVAR($yValues, $xValues)
{
try {
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear->getCovariance();
}
/**
* FORECAST.
*
* Calculates, or predicts, a future value by using existing values.
* The predicted value is a y-value for a given x-value.
*
* @param mixed $xValue Float value of X for which we want to find Y
* Or can be an array of values
* @param mixed $yValues array of mixed Data Series Y
* @param mixed $xValues of mixed Data Series X
*
* @return array|bool|float|string
* If an array of numbers is passed as an argument, then the returned result will also be an array
* with the same dimensions
*/
public static function FORECAST($xValue, $yValues, $xValues)
{
if (is_array($xValue)) {
return self::evaluateArrayArgumentsSubset([self::class, __FUNCTION__], 1, $xValue, $yValues, $xValues);
}
try {
$xValue = StatisticalValidations::validateFloat($xValue);
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear->getValueOfYForX($xValue);
}
/**
* GROWTH.
*
* Returns values along a predicted exponential Trend
*
* @param mixed[] $yValues Data Series Y
* @param mixed[] $xValues Data Series X
* @param mixed[] $newValues Values of X for which we want to find Y
* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
*
* @return float[]
*/
public static function GROWTH($yValues, $xValues = [], $newValues = [], $const = true)
{
$yValues = Functions::flattenArray($yValues);
$xValues = Functions::flattenArray($xValues);
$newValues = Functions::flattenArray($newValues);
$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
$bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);
if (empty($newValues)) {
$newValues = $bestFitExponential->getXValues();
}
$returnArray = [];
foreach ($newValues as $xValue) {
$returnArray[0][] = [$bestFitExponential->getValueOfYForX($xValue)];
}
return $returnArray;
}
/**
* INTERCEPT.
*
* Calculates the point at which a line will intersect the y-axis by using existing x-values and y-values.
*
* @param mixed[] $yValues Data Series Y
* @param mixed[] $xValues Data Series X
*
* @return float|string
*/
public static function INTERCEPT($yValues, $xValues)
{
try {
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear->getIntersect();
}
/**
* LINEST.
*
* Calculates the statistics for a line by using the "least squares" method to calculate a straight line
* that best fits your data, and then returns an array that describes the line.
*
* @param mixed[] $yValues Data Series Y
* @param null|mixed[] $xValues Data Series X
* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
* @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
*
* @return array|int|string The result, or a string containing an error
*/
public static function LINEST($yValues, $xValues = null, $const = true, $stats = false)
{
$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
$stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
if ($xValues === null) {
$xValues = $yValues;
}
try {
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);
if ($stats === true) {
return [
[
$bestFitLinear->getSlope(),
$bestFitLinear->getIntersect(),
],
[
$bestFitLinear->getSlopeSE(),
($const === false) ? ExcelError::NA() : $bestFitLinear->getIntersectSE(),
],
[
$bestFitLinear->getGoodnessOfFit(),
$bestFitLinear->getStdevOfResiduals(),
],
[
$bestFitLinear->getF(),
$bestFitLinear->getDFResiduals(),
],
[
$bestFitLinear->getSSRegression(),
$bestFitLinear->getSSResiduals(),
],
];
}
return [
$bestFitLinear->getSlope(),
$bestFitLinear->getIntersect(),
];
}
/**
* LOGEST.
*
* Calculates an exponential curve that best fits the X and Y data series,
* and then returns an array that describes the line.
*
* @param mixed[] $yValues Data Series Y
* @param null|mixed[] $xValues Data Series X
* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
* @param mixed $stats A logical (boolean) value specifying whether to return additional regression statistics
*
* @return array|int|string The result, or a string containing an error
*/
public static function LOGEST($yValues, $xValues = null, $const = true, $stats = false)
{
$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
$stats = ($stats === null) ? false : (bool) Functions::flattenSingleValue($stats);
if ($xValues === null) {
$xValues = $yValues;
}
try {
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
foreach ($yValues as $value) {
if ($value < 0.0) {
return ExcelError::NAN();
}
}
$bestFitExponential = Trend::calculate(Trend::TREND_EXPONENTIAL, $yValues, $xValues, $const);
if ($stats === true) {
return [
[
$bestFitExponential->getSlope(),
$bestFitExponential->getIntersect(),
],
[
$bestFitExponential->getSlopeSE(),
($const === false) ? ExcelError::NA() : $bestFitExponential->getIntersectSE(),
],
[
$bestFitExponential->getGoodnessOfFit(),
$bestFitExponential->getStdevOfResiduals(),
],
[
$bestFitExponential->getF(),
$bestFitExponential->getDFResiduals(),
],
[
$bestFitExponential->getSSRegression(),
$bestFitExponential->getSSResiduals(),
],
];
}
return [
$bestFitExponential->getSlope(),
$bestFitExponential->getIntersect(),
];
}
/**
* RSQ.
*
* Returns the square of the Pearson product moment correlation coefficient through data points
* in known_y's and known_x's.
*
* @param mixed[] $yValues Data Series Y
* @param mixed[] $xValues Data Series X
*
* @return float|string The result, or a string containing an error
*/
public static function RSQ($yValues, $xValues)
{
try {
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear->getGoodnessOfFit();
}
/**
* SLOPE.
*
* Returns the slope of the linear regression line through data points in known_y's and known_x's.
*
* @param mixed[] $yValues Data Series Y
* @param mixed[] $xValues Data Series X
*
* @return float|string The result, or a string containing an error
*/
public static function SLOPE($yValues, $xValues)
{
try {
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear->getSlope();
}
/**
* STEYX.
*
* Returns the standard error of the predicted y-value for each x in the regression.
*
* @param mixed[] $yValues Data Series Y
* @param mixed[] $xValues Data Series X
*
* @return float|string
*/
public static function STEYX($yValues, $xValues)
{
try {
self::checkTrendArrays($yValues, $xValues);
self::validateTrendArrays($yValues, $xValues);
} catch (Exception $e) {
return $e->getMessage();
}
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues);
return $bestFitLinear->getStdevOfResiduals();
}
/**
* TREND.
*
* Returns values along a linear Trend
*
* @param mixed[] $yValues Data Series Y
* @param mixed[] $xValues Data Series X
* @param mixed[] $newValues Values of X for which we want to find Y
* @param mixed $const A logical (boolean) value specifying whether to force the intersect to equal 0 or not
*
* @return float[]
*/
public static function TREND($yValues, $xValues = [], $newValues = [], $const = true)
{
$yValues = Functions::flattenArray($yValues);
$xValues = Functions::flattenArray($xValues);
$newValues = Functions::flattenArray($newValues);
$const = ($const === null) ? true : (bool) Functions::flattenSingleValue($const);
$bestFitLinear = Trend::calculate(Trend::TREND_LINEAR, $yValues, $xValues, $const);
if (empty($newValues)) {
$newValues = $bestFitLinear->getXValues();
}
$returnArray = [];
foreach ($newValues as $xValue) {
$returnArray[0][] = [$bestFitLinear->getValueOfYForX($xValue)];
}
return $returnArray;
}
}
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