Statistics (fissuresUtil 1.1.11-SNAPSHOT API)

java.lang.Object
- edu.sc.seis.fissuresUtil.bag.Statistics

```
public class Statistics
extends java.lang.Object
```
Statistics.java Created: Wed Apr 4 22:27:52 2001

Field Summary

Fields
Modifier and Type	Field and Description
`protected double[]`	`autocorrelation`
`protected int`	`beginIndex`
`protected double[]`	`dSeries`
`protected int`	`endIndex`
`protected float[]`	`fSeries`
`protected int[]`	`iSeries`
`protected double[]`	`minMaxMean` index 0 has min, index 1 has max, index 2 has mean.
`protected boolean`	`minMaxMeanCalculated` Has min/max/mean already been calculated and cached.
`protected double[]`	`partialautocorr`
`protected short[]`	`sSeries`
`protected double`	`variance`
`protected boolean`	`varianceCalculated` Has the variable been calculated and cached.

Constructor Summary

Constructors
Constructor and Description
`Statistics(double[] dSeries)` Creates a new `Statistics` instance.
`Statistics(float[] fSeries)` Creates a new `Statistics` instance.
`Statistics(int[] iSeries)` Creates a new `Statistics` instance.
`Statistics(java.util.List<edu.iris.Fissures.model.QuantityImpl> vals)`
`Statistics(java.util.List<edu.iris.Fissures.model.QuantityImpl> vals, edu.iris.Fissures.model.UnitImpl unit)`
`Statistics(edu.iris.Fissures.seismogramDC.LocalSeismogramImpl seismo)` Creates a new `Statistics` instance.
`Statistics(short[] sSeries)` Creates a new `Statistics` instance.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`double[]`	`acf(int maxlag)` Calculates the autocovariance function out to the given lag.
`double[]`	`acf95conf(int maxlag)` Describe `acf95conf` method here.
`double[]`	`acfTRatio(int maxlag)` Calculates the TRatio for the acf out to the given max lag.
`double`	`binaryIndexSum(int start, int finish)` Calulates the sum(i*yi) from beginIndex to endIndex-1.
`double`	`binarySum(int start, int finish)` Calulates the sum of the series from beginIndex to endIndex-1.
`double`	`binarySumDevLag(int start, int finish, double mean, int lag)` Describe `binarySumDevLag` method here.
`double`	`binarySumDevSqr(int start, int finish, double mean)` Calulates the sum of the square of the difference from the mean.
`double`	`correlation(double[] other)`
`double`	`covariance(double[] other)`
`double`	`covariance(Statistics otherStat)`
`int`	`getLength()` The length of the series.
`int[]`	`histogram(double start, double width, int number)` Creates a histogram of the values.
`double[]`	`linearLeastSquares()` Calculates the linear Least Squares slope and intercept for this series.
`double`	`max()` Finds the `max` value.
`double`	`max(int beginIndex, int endIndex)` Finds the `max` value between index beginIndex and endIndex-1.
`double`	`maxDeviation()` Finds the `max` deviation from the mean.
`double`	`maxDeviation(int beginIndex, int endIndex)` Finds the `max` deviation from the mean between index beginIndex and endIndex-1.
`double`	`mean()` Calulates `mean` of the data.
`double`	`mean(int beginIndex, int endIndex)` Finds the `mean` value between index beginIndex and endIndex-1.
`double`	`min()` Finds the `min` value.
`double`	`min(int beginIndex, int endIndex)` Finds the `min` value between index beginIndex and endIndex-1.
`double[]`	`minMaxMean()` Calculates the min, max and mean.
`double[]`	`minMaxMean(int beginIndex, int endIndex)` Calculates the min, max and mean from beginIndex to endIndex-1.
`double[]`	`pacf(int maxlag)` Computes the partial autocorrelation function, after Wei, William S.
`double`	`pacf95conf(int maxlag)` Calculates the 95 percent confidence for the pacf.
`double[]`	`pacfTRatio(int maxlag)` Calculates the TRatio for the pacf.
`double`	`rms()`
`double`	`stddev()` Calulates the standard deviation.
`java.lang.String`	`toString()`
`double`	`var()` Calualates the unbiased variance, ie 1/(n-1) SUM{(x-mean)^2}.
`double`	`var(double mean)` calculates the unbiased variance w.r.t the given mean.

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

- Field Detail
  - iSeries
```
protected int[] iSeries
```
  - sSeries
```
protected short[] sSeries
```
  - fSeries
```
protected float[] fSeries
```
  - dSeries
```
protected double[] dSeries
```
  - minMaxMeanCalculated
```
protected boolean minMaxMeanCalculated
```
    Has min/max/mean already been calculated and cached.
  - minMaxMean
```
protected double[] minMaxMean
```
    index 0 has min, index 1 has max, index 2 has mean.
  - variance
```
protected double variance
```
  - varianceCalculated
```
protected boolean varianceCalculated
```
    Has the variable been calculated and cached.
  - autocorrelation
```
protected double[] autocorrelation
```
  - partialautocorr
```
protected double[] partialautocorr
```
  - beginIndex
```
protected int beginIndex
```
  - endIndex
```
protected int endIndex
```
- Constructor Detail
  - Statistics
```
public Statistics(int[] iSeries)
```
    Creates a new Statistics instance.
    
    Parameters:
    
    iSeries - an int[] value
  - Statistics
```
public Statistics(short[] sSeries)
```
    Creates a new Statistics instance.
    
    Parameters:
    
    sSeries - a short[] value
  - Statistics
```
public Statistics(float[] fSeries)
```
    Creates a new Statistics instance.
    
    Parameters:
    
    fSeries - a float[] value
  - Statistics
```
public Statistics(double[] dSeries)
```
    Creates a new Statistics instance.
    
    Parameters:
    
    dSeries - a double[] value
  - Statistics
```
public Statistics(edu.iris.Fissures.seismogramDC.LocalSeismogramImpl seismo)
           throws edu.iris.Fissures.FissuresException
```
    Creates a new Statistics instance.
    
    Parameters:
    
    seismo - a LocalSeismogramImpl value
    
    Throws:
    
    edu.iris.Fissures.FissuresException
  - Statistics
```
public Statistics(java.util.List<edu.iris.Fissures.model.QuantityImpl> vals)
```
  - Statistics
```
public Statistics(java.util.List<edu.iris.Fissures.model.QuantityImpl> vals,
                  edu.iris.Fissures.model.UnitImpl unit)
```
- Method Detail
  - min
```
public double min()
```
    Finds the min value.
    
    Returns:
    
    the minimum
  - min
```
public double min(int beginIndex,
                  int endIndex)
```
    Finds the min value between index beginIndex and endIndex-1.
    
    Parameters:
    
    beginIndex - first index to search
    
    endIndex - end index, last + 1
    
    Returns:
    
    a double value
  - max
```
public double max()
```
    Finds the max value.
    
    Returns:
    
    a double value
  - max
```
public double max(int beginIndex,
                  int endIndex)
```
    Finds the max value between index beginIndex and endIndex-1.
    
    Parameters:
    
    beginIndex - an int value
    
    endIndex - an int value
    
    Returns:
    
    a double value
  - maxDeviation
```
public double maxDeviation()
```
    Finds the max deviation from the mean.
    
    Returns:
    
    a double value
  - maxDeviation
```
public double maxDeviation(int beginIndex,
                           int endIndex)
```
    Finds the max deviation from the mean between index beginIndex and endIndex-1.
    
    Parameters:
    
    beginIndex - an int value
    
    endIndex - an int value
    
    Returns:
    
    a double value
  - mean
```
public double mean()
```
    Calulates mean of the data.
    
    Returns:
    
    a double value
  - mean
```
public double mean(int beginIndex,
                   int endIndex)
```
    Finds the mean value between index beginIndex and endIndex-1.
    
    Parameters:
    
    beginIndex - an int value
    
    endIndex - an int value
    
    Returns:
    
    a double value
  - minMaxMean
```
public double[] minMaxMean()
```
    Calculates the min, max and mean. The value at index 0 is the min, at index 1 is the max and at index 2 is the mean
    
    Returns:
    
    min, max, mean in a double[3]
  - minMaxMean
```
public double[] minMaxMean(int beginIndex,
                           int endIndex)
```
    Calculates the min, max and mean from beginIndex to endIndex-1. The value at index 0 is the min, at index 1 is the max and at index 2 is the mean
    
    Parameters:
    
    beginIndex - first index
    
    endIndex - last index +1
    
    Returns:
    
    min, max, mean in a double[3]
  - var
```
public double var()
```
    Calualates the unbiased variance, ie 1/(n-1) SUM{(x-mean)^2}.
    
    Returns:
    
    variance
  - var
```
public double var(double mean)
```
    calculates the unbiased variance w.r.t the given mean. This allows a psuedo-variance to be calculated based on a longer term mean.
  - rms
```
public double rms()
```
  - stddev
```
public double stddev()
```
    Calulates the standard deviation. This is a shortcut for Math.sqrt(var()); Note this uses the unbiased variance.
    
    Returns:
    
    the Standard deviation
  - covariance
```
public double covariance(double[] other)
```
  - covariance
```
public double covariance(Statistics otherStat)
```
  - correlation
```
public double correlation(double[] other)
```
  - linearLeastSquares
```
public double[] linearLeastSquares()
```
    Calculates the linear Least Squares slope and intercept for this series. Note that this is calculated relative to the index of the array, with zero index as the first data point.
    
    Returns:
    
    the intercept in index 0 and the slope in index 1
  - acf
```
public double[] acf(int maxlag)
```
    Calculates the autocovariance function out to the given lag.
    
    Parameters:
    
    maxlag - the maximum lag to calculate the acf
    
    Returns:
    
    the acf for the series out to maxlag
  - acf95conf
```
public double[] acf95conf(int maxlag)
```
    Describe acf95conf method here.
    
    Parameters:
    
    maxlag - an int value
    
    Returns:
    
    a double[] value
  - acfTRatio
```
public double[] acfTRatio(int maxlag)
```
    Calculates the TRatio for the acf out to the given max lag.
    
    Parameters:
    
    maxlag - the maximu lag
    
    Returns:
    
    the T ratio
  - pacf
```
public double[] pacf(int maxlag)
```
    Computes the partial autocorrelation function, after Wei, William S. Time Series Analysis, pp 22-23.
    
    Parameters:
    
    maxlag - the maximum lag
    
    Returns:
    
    the pacf
  - pacf95conf
```
public double pacf95conf(int maxlag)
```
    Calculates the 95 percent confidence for the pacf. This is just 1.96*sqrt(length)
    
    Parameters:
    
    maxlag - the maximum lag
    
    Returns:
    
    the 95 percent conf for the pacf
  - pacfTRatio
```
public double[] pacfTRatio(int maxlag)
```
    Calculates the TRatio for the pacf.
    
    Parameters:
    
    maxlag - the maximum lag
    
    Returns:
    
    T ratio for the pacf
  - getLength
```
public int getLength()
```
    The length of the series.
  - histogram
```
public int[] histogram(double start,
                       double width,
                       int number)
```
    Creates a histogram of the values. Each value is added to the bin Math.floor((value-start)/width) and the returned int array has length number
    
    Parameters:
    
    start - binning start value
    
    width - bin width
    
    number - number of bins
    
    Returns:
    
    an histogram
  - toString
```
public java.lang.String toString()
```
    Overrides:
    
    toString in class java.lang.Object
  - binarySum
```
public double binarySum(int start,
                        int finish)
```
    Calulates the sum of the series from beginIndex to endIndex-1. This is done recursively in halves to avoid rounding errors.
    
    Parameters:
    
    start - starting index
    
    finish - last index +1
    
    Returns:
    
    the sum
  - binarySumDevSqr
```
public double binarySumDevSqr(int start,
                              int finish,
                              double mean)
```
    Calulates the sum of the square of the difference from the mean. Sum ((xi-mean)^2)
    
    Parameters:
    
    start - first index
    
    finish - last index+1
    
    mean - the mean
    
    Returns:
    
    the result
  - binarySumDevLag
```
public double binarySumDevLag(int start,
                              int finish,
                              double mean,
                              int lag)
```
    Describe binarySumDevLag method here.
    
    Parameters:
    
    start - an int value
    
    finish - an int value
    
    mean - a double value
    
    lag - an int value
    
    Returns:
    
    a double value
  - binaryIndexSum
```
public double binaryIndexSum(int start,
                             int finish)
```
    Calulates the sum(i*yi) from beginIndex to endIndex-1. This is done recursively in halves to avoid rounding errors. This is mainly used as one of the terms in a linear least squares.
    
    Parameters:
    
    start - starting index
    
    finish - last index +1
    
    Returns:
    
    the sum

Class Statistics

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

iSeries

sSeries

fSeries

dSeries

minMaxMeanCalculated

minMaxMean

variance

varianceCalculated

autocorrelation

partialautocorr

beginIndex

endIndex

Constructor Detail

Statistics

Statistics

Statistics

Statistics

Statistics

Statistics

Statistics

Method Detail

min

min

max

max

maxDeviation

maxDeviation

mean

mean

minMaxMean

minMaxMean

var

var

rms

stddev

covariance

covariance

correlation

linearLeastSquares

acf

acf95conf

acfTRatio

pacf

pacf95conf

pacfTRatio

getLength

histogram

toString

binarySum

binarySumDevSqr

binarySumDevLag

binaryIndexSum