\PhpOffice\PhpSpreadsheet\Shared\TrendExponentialBestFit

Summary

Methods

Properties

Constants

getError()
getBestFitType()
getValueOfYForX()
getValueOfXForY()
getXValues()
getEquation()
getSlope()
getSlopeSE()
getIntersect()
getIntersectSE()
getGoodnessOfFit()
getGoodnessOfFitPercent()
getStdevOfResiduals()
getSSRegression()
getSSResiduals()
getDFResiduals()
getF()
getCovariance()
getCorrelation()
getYBestFitValues()
__construct()

No public properties found

No constants found

calculateGoodnessOfFit()
leastSquareFit()

$error
$bestFitType
$valueCount
$xValues
$yValues
$adjustToZero
$yBestFitValues
$goodnessOfFit
$stdevOfResiduals
$covariance
$correlation
$SSRegression
$SSResiduals
$DFResiduals
$f
$slope
$slopeSE
$intersect
$intersectSE
$xOffset
$yOffset

N/A

exponentialRegression()

No private properties found

N/A

Properties

$error

$error : boolean

Indicator flag for a calculation error.

Type

boolean

$bestFitType

$bestFitType : string

Algorithm type to use for best-fit (Name of this Trend class).

Type

string

$valueCount

$valueCount : integer

Number of entries in the sets of x- and y-value arrays.

Type

integer

$xValues

$xValues : array<mixed,float>

X-value dataseries of values.

Type

array<mixed,float>

$yValues

$yValues : array<mixed,float>

Y-value dataseries of values.

Type

array<mixed,float>

$adjustToZero

$adjustToZero : boolean

Flag indicating whether values should be adjusted to Y=0.

Type

boolean

$yBestFitValues

$yBestFitValues : array<mixed,float>

Y-value series of best-fit values.

Type

array<mixed,float>

$goodnessOfFit

$goodnessOfFit :

Type

$stdevOfResiduals

$stdevOfResiduals :

Type

$covariance

$covariance :

Type

$correlation

$correlation :

Type

$SSRegression

$SSRegression :

Type

$SSResiduals

$SSResiduals :

Type

$DFResiduals

$DFResiduals :

Type

$f

$f :

Type

$slope

$slope :

Type

$slopeSE

$slopeSE :

Type

$intersect

$intersect :

Type

$intersectSE

$intersectSE :

Type

$xOffset

$xOffset :

Type

$yOffset

$yOffset :

Type

Methods

getError()

getError()

getBestFitType()

getBestFitType()

getValueOfYForX()

getValueOfYForX(float  $xValue) : float

Return the Y-Value for a specified value of X.

Parameters

float

$xValue

X-Value

Returns

float —

Y-Value

getValueOfXForY()

getValueOfXForY(float  $yValue) : float

Return the X-Value for a specified value of Y.

Parameters

float

$yValue

Y-Value

Returns

float —

X-Value

getXValues()

getXValues() : array<mixed,float>

Return the original set of X-Values.

Returns

array<mixed,float> —

X-Values

getEquation()

getEquation(integer  $dp) : string

Return the Equation of the best-fit line.

Parameters

integer

$dp

Number of places of decimal precision to display

Returns

string

getSlope()

getSlope(integer  $dp) : float

Return the Slope of the line.

Parameters

integer

$dp

Number of places of decimal precision to display

Returns

float

getSlopeSE()

getSlopeSE(integer  $dp) : float

Return the standard error of the Slope.

Parameters

integer

$dp

Number of places of decimal precision to display

Returns

float

getIntersect()

getIntersect(integer  $dp) : float

Return the Value of X where it intersects Y = 0.

Parameters

integer

$dp

Number of places of decimal precision to display

Returns

float

getIntersectSE()

getIntersectSE(integer  $dp) : float

Return the standard error of the Intersect.

Parameters

integer

$dp

Number of places of decimal precision to display

Returns

float

getGoodnessOfFit()

getGoodnessOfFit(integer  $dp) : float

Return the goodness of fit for this regression.

Parameters

integer

$dp

Number of places of decimal precision to return

Returns

float

getGoodnessOfFitPercent()

getGoodnessOfFitPercent(integer  $dp) : float

Return the goodness of fit for this regression.

Parameters

integer

$dp

Number of places of decimal precision to return

Returns

float

getStdevOfResiduals()

getStdevOfResiduals(integer  $dp) : float

getYBestFitValues() : array<mixed,float>

Returns

array<mixed,float>

__construct()

__construct(array<mixed,float>  $yValues, array<mixed,float>  $xValues = array(), boolean  $const = true)

Define the regression and calculate the goodness of fit for a set of X and Y data values.

Parameters

array<mixed,float>	$yValues	The set of Y-values for this regression
array<mixed,float>	$xValues	The set of X-values for this regression
boolean	$const

calculateGoodnessOfFit()

calculateGoodnessOfFit(  $sumX,   $sumY,   $sumX2,   $sumY2,   $sumXY,   $meanX,   $meanY,   $const)

Parameters

	$sumX
	$sumY
	$sumX2
	$sumY2
	$sumXY
	$meanX
	$meanY
	$const

leastSquareFit()

leastSquareFit(array<mixed,float>  $yValues, array<mixed,float>  $xValues, boolean  $const)

Parameters

array<mixed,float>	$yValues
array<mixed,float>	$xValues
boolean	$const

exponentialRegression()

exponentialRegression(array<mixed,float>  $yValues, array<mixed,float>  $xValues, boolean  $const)

Execute the regression and calculate the goodness of fit for a set of X and Y data values.

Parameters

array<mixed,float>	$yValues	The set of Y-values for this regression
array<mixed,float>	$xValues	The set of X-values for this regression
boolean	$const