Univariate (CML Euclid 2.9 API)

java.lang.Object
- org.xmlcml.euclid.Univariate

```
public class Univariate
extends Object
```
Univariate stats.

Author:

(C) P. Murray-Rust, 2001, 2004

Constructor Summary

Constructors
Constructor and Description

Univariate()
default constructor.

Univariate(RealArray realArray)
creates from data array.

Constructors
Constructor and Description
`Univariate()` default constructor.
`Univariate(RealArray realArray)` creates from data array.

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`static double`	`betainv(double x, double p, double q)` betainv ALGORITHM AS 63 APPL.
`static double`	`fact(int n)`
`static double`	`fbeta(double a, double b)`
`static double`	`fgamma(double c)`
`double[]`	`getArray()` get data points.
`int`	`getBinCount()` get bin count.
`List<Real2>`	`getBinsSortedByFrequency()` gets bin values and frequencies sorted by frequency each Real2 is x-value, frequency zero counts are not reported
`double`	`getBinWidth()`
`int`	`getCount()` get number of data points.
`RealArray`	`getDeviateValues()` get deviate values.
`int[]`	`getHistogramCounts()` return bins for Histogram.
`IntArray`	`getIndexOfBinsSortedByDescendingFrequency()` gets pointers to bins sorted by descending frequency.
`double`	`getMax()` get maximum value.
`double`	`getMean()` get mean value.
`double`	`getMedian()` get median.
`RealArray`	`getMedianBinValuesSortedByFrequency()` this sorts the bins in order of frequency.
`RealArray`	`getMedianXValues()` gets the median xvalues for the bins.
`double`	`getMin()` get minimum value.
`RealArray`	`getNormalizedValues()` get normalized values.
`static Univariate`	`getNormalParams(int count)` get normal parameters I think...
`double`	`getQuantile(double q)` get quantile.
`RealRange`	`getRange()`
`double[]`	`getSortedArray()` get sorted data points.
`double`	`getStandardDeviation()` get standard deviation.
`double`	`getStandardError()` get standard error.
`List<Univariate>`	`getUnivariatesForBins()`
`double`	`getVariance()` get variance.
`RealArray`	`getXValues()` gets the xvalues for the bins.
`static double`	`lnfact(int n)`
`static double`	`lnfbeta(double a, double b)`
`static double`	`lnfgamma(double c)`
`static void`	`main(String[] args)`
`static double`	`nCr(int n, int r)`
`static double`	`nPr(int n, int r)`
`double`	`pchisq(double q, double df)` chiSquared.
`static double`	`pf(double x, double df1, double df2)` betainv ALGORITHM AS 63 APPL.
`static double`	`pnorm(double z, boolean upper)` normal integral.
`static double`	`pnorm(double x, boolean upper, double mu, double sigma2)` normal integral.
`static double`	`pt(double t, double df)` T-test.
`static double`	`qnorm(double p)` percentage points of normal distribution.
`static double`	`qnorm(double p, boolean upper)` percentage points of normal distribution.
`static double`	`qnorm(double p, boolean upper, double mu, double sigma2)` percentage points of normal distribution.
`static double`	`qt(double p, double ndf, boolean lower_tail)` Student's t-quantiles.
`void`	`setArray(RealArray realArray)` sets data copies realArray
`void`	`setBinCount(int binCount)` set bin count.
`static void`	`test()`

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - Univariate
```
public Univariate()
```
    default constructor.
  - Univariate
```
public Univariate(RealArray realArray)
```
    creates from data array. copies realArray
    
    Parameters:
    
    realArray - the data
- Method Detail
  - setArray
```
public void setArray(RealArray realArray)
```
    sets data copies realArray
    
    Parameters:
    
    realArray - the data
  - setBinCount
```
public void setBinCount(int binCount)
```
    set bin count.
    
    Parameters:
    
    binCount - the number of bins
  - getBinCount
```
public int getBinCount()
```
    get bin count.
    
    Returns:
    
    binCount (default 10)
  - getCount
```
public int getCount()
```
    get number of data points.
    
    Returns:
    
    count
  - getMin
```
public double getMin()
```
    get minimum value.
    
    Returns:
    
    minimum value
  - getMax
```
public double getMax()
```
    get maximum value.
    
    Returns:
    
    maximum value
  - getMean
```
public double getMean()
```
    get mean value. @ not enough points
    
    Returns:
    
    mean value; Double NaN if no points
  - getVariance
```
public double getVariance()
```
    get variance. @ not enough points
    
    Returns:
    
    variance
  - getStandardDeviation
```
public double getStandardDeviation()
```
    get standard deviation. @ not enough points
    
    Returns:
    
    standard deviation
  - getStandardError
```
public double getStandardError()
```
    get standard error. @ not enough points
    
    Returns:
    
    standard error
  - getArray
```
public double[] getArray()
```
    get data points.
    
    Returns:
    
    the points
  - getSortedArray
```
public double[] getSortedArray()
```
    get sorted data points. modifies this
    
    Returns:
    
    the points
  - getNormalizedValues
```
public RealArray getNormalizedValues()
```
    get normalized values. array transformed by subtracting mean and dividing by standard deviation. result (the "z"-values") therefore have mean of zero and stdev of 1.0. does not modify this.
    
    Returns:
    
    the normalized values
  - getDeviateValues
```
public RealArray getDeviateValues()
```
    get deviate values. array transformed by subtrating mean. result therefore has mean of zero. does not modify this.
    
    Returns:
    
    the deviate values
  - getQuantile
```
public double getQuantile(double q)
```
    get quantile.
    
    Parameters:
    
    q - the value of the quantile (0 =< q =< 1.0) @ not enough points
    
    Returns:
    
    quantile
  - getMedian
```
public double getMedian()
```
    get median. @ not enough points
    
    Returns:
    
    median
  - getXValues
```
public RealArray getXValues()
```
    gets the xvalues for the bins. These are the low value for each bin. bin(i) runs from xvalue(i)-xvalue(i+1) the median value for the bin is xvalue(i)+deltax the last bin is from xvalue(binCount-1) ... xvalue(binCount-1)+deltaX
    
    Returns:
    
    xvalues
  - getMedianXValues
```
public RealArray getMedianXValues()
```
    gets the median xvalues for the bins. These are the median for each bin. bin(i) runs from xvalue(i)-xvalue(i+1) the median value for the bin is xvalue(i)+deltax
    
    Returns:
    
    xvalues
  - getBinWidth
```
public double getBinWidth()
```
  - getMedianBinValuesSortedByFrequency
```
public RealArray getMedianBinValuesSortedByFrequency()
```
    this sorts the bins in order of frequency. A simple way of finding the local maxima
    
    Returns:
  - getIndexOfBinsSortedByDescendingFrequency
```
public IntArray getIndexOfBinsSortedByDescendingFrequency()
```
    gets pointers to bins sorted by descending frequency. array.elementAt(0) points to largest bin
    
    Returns:
    
    pointers
  - getBinsSortedByFrequency
```
public List<Real2> getBinsSortedByFrequency()
```
    gets bin values and frequencies sorted by frequency each Real2 is x-value, frequency zero counts are not reported
    
    Returns:
  - getRange
```
public RealRange getRange()
```
  - getUnivariatesForBins
```
public List<Univariate> getUnivariatesForBins()
```
  - getHistogramCounts
```
public int[] getHistogramCounts()
```
    return bins for Histogram. @ not enough points
    
    Returns:
    
    the counts in each bin
  - getNormalParams
```
public static Univariate getNormalParams(int count)
```
    get normal parameters I think... variate should be normally distributed about 1.0 with sd = 1.0
    
    Parameters:
    
    count - number of points
    
    Returns:
    
    the normal distribution
  - qnorm
```
public static double qnorm(double p)
```
    percentage points of normal distribution. upper is true.
    
    Parameters:
    
    p - (0 <= p <= 1)
    
    Returns:
    
    the percentage point (?)
  - qnorm
```
public static double qnorm(double p,
                           boolean upper)
```
    percentage points of normal distribution. J. D. Beasley and S. G. Springer Algorithm AS 111: "The Percentage Points of the Normal Distribution" Applied Statistics
    
    Parameters:
    
    p - (0 <= p <= 1)
    
    upper - if true use upper half (??)
    
    Returns:
    
    the percentage point (?)
  - qnorm
```
public static double qnorm(double p,
                           boolean upper,
                           double mu,
                           double sigma2)
```
    percentage points of normal distribution. J. D. Beasley and S. G. Springer Algorithm AS 111: "The Percentage Points of the Normal Distribution" Applied Statistics
    
    Parameters:
    
    p - (0 <= p <= 1)
    
    upper - if true use upper half (??)
    
    mu - mean
    
    sigma2 - the variance(?)
    
    Returns:
    
    the percentage point (?)
  - pnorm
```
public static double pnorm(double z,
                           boolean upper)
```
    normal integral. I. D. Hill Algorithm AS 66: "The Normal Integral" Applied Statistics
    
    Parameters:
    
    z -
    
    upper - if true use upper half (??)
    
    Returns:
    
    the integral (?)
  - pnorm
```
public static double pnorm(double x,
                           boolean upper,
                           double mu,
                           double sigma2)
```
    normal integral. I. D. Hill Algorithm AS 66: "The Normal Integral" Applied Statistics
    
    Parameters:
    
    x -
    
    upper - if true use upper half (??)
    
    mu - mean
    
    sigma2 - the variance(?)
    
    Returns:
    
    the integral (?)
  - qt
```
public static double qt(double p,
                        double ndf,
                        boolean lower_tail)
```
    Student's t-quantiles. Algorithm 396: Student's t-quantiles by G.W. Hill CACM 13(10), 619-620, October 1970
    
    Parameters:
    
    p - (0 <= p <= 1)
    
    ndf - degrees of freedom >= 1
    
    lower_tail -
    
    Returns:
    
    the integral (?)
  - pt
```
public static double pt(double t,
                        double df)
```
    T-test. ALGORITHM AS 3 APPL. STATIST. (1968) VOL.17, P.189 Computes P(T < t)
    
    Parameters:
    
    t -
    
    df - degrees of freedom
    
    Returns:
    
    probability (?)
  - pchisq
```
public double pchisq(double q,
                     double df)
```
    chiSquared. Posten, H. (1989) American Statistician 43 p. 261-265
    
    Parameters:
    
    q -
    
    df - degrees of freedom
    
    Returns:
    
    chisq (?)
  - betainv
```
public static double betainv(double x,
                             double p,
                             double q)
```
    betainv ALGORITHM AS 63 APPL. STATIST. VOL.32, NO.1 Computes P(Beta>x)
    
    Parameters:
    
    x -
    
    p -
    
    q -
    
    Returns:
    
    betainv (?)
  - pf
```
public static double pf(double x,
                        double df1,
                        double df2)
```
    betainv ALGORITHM AS 63 APPL. STATIST. VOL.32, NO.1 Computes P(F>x)
    
    Parameters:
    
    x -
    
    df1 -
    
    df2 -
    
    Returns:
    
    (?)
  - test
```
public static void test()
```
  - lnfgamma
```
public static double lnfgamma(double c)
```
  - lnfbeta
```
public static double lnfbeta(double a,
                             double b)
```
  - fbeta
```
public static double fbeta(double a,
                           double b)
```
  - fgamma
```
public static double fgamma(double c)
```
  - fact
```
public static double fact(int n)
```
  - lnfact
```
public static double lnfact(int n)
```
  - nCr
```
public static double nCr(int n,
                         int r)
```
  - nPr
```
public static double nPr(int n,
                         int r)
```
  - main
```
public static void main(String[] args)
```

Class Univariate

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

Univariate

Univariate

Method Detail

setArray

setBinCount

getBinCount

getCount

getMin

getMax

getMean

getVariance

getStandardDeviation

getStandardError

getArray

getSortedArray

getNormalizedValues

getDeviateValues

getQuantile

getMedian

getXValues

getMedianXValues

getBinWidth

getMedianBinValuesSortedByFrequency

getIndexOfBinsSortedByDescendingFrequency

getBinsSortedByFrequency

getRange

getUnivariatesForBins

getHistogramCounts

getNormalParams

qnorm

qnorm

qnorm

pnorm

pnorm

qt

pt

pchisq

betainv

pf

test

lnfgamma

lnfbeta

fbeta

fgamma

fact

lnfact

nCr

nPr

main