```
public class Stats
extends Object
```
Author:

Olaf David

Method Summary

All Methods Static Methods Concrete Methods
Modifier and Type	Method	Description
`static double`	`bias(double[] pred, double[] valid)`
`static double`	`err_sum(double[] validation, double[] prediction)`
`static double`	`gradient(double[] xData, double[] yData)`
`static double`	`intercept(double[] xData, double[] yData)`
`static double`	`ioa(double[] prediction, double[] validation, double pow)`	Calculates the index of agreement (ioa) between a test data set and a verification data set after Willmot & Wicks (1980).
`static double`	`lag1(double[] vals)`	Returns the lag-1 autocorrelation of a dataset;
`static int`	`length(double[] vals)`
`static double`	`max(double[] vals)`
`static double`	`mean(double[] vals)`
`static double`	`meandev(double[] vals)`
`static double`	`median(double[] vals)`
`static double`	`min(double[] vals)`
`static double`	`nashsut(double[] prediction, double[] validation, double pow)`	Calculates the efficiency between a test data set and a verification data set after Nash & Sutcliffe (1970).
`static double`	`nashsut_log(double[] prediction, double[] validation, double pow)`	Calculates the efficiency between the log values of a test data set and a verification data set after Nash & Sutcliffe (1970).
`static double`	`norm_vec(double x, double y, double z)`	Normalized Vector.
`static double`	`product(double[] vals)`
`static double`	`quantile(double[] vals, double phi)`
`static double`	`r2(double[] xData, double[] yData)`
`static double`	`random(double min, double max)`	Generate a random number in a range.
`static double`	`range(double[] vals)`
`static double`	`rmse(double[] pred, double[] valid)`
`static double`	`round(double val, int places)`	Round a double value to a specified number of decimal places.
`static float`	`round(float val, int places)`	Round a float value to a specified number of decimal places.
`static double`	`stddev(double[] vals)`
`static double`	`stderr(double[] vals)`
`static double`	`sum(double[] vals)`
`static double`	`variance(double[] vals)`

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

- Method Detail
  - norm_vec
```
public static double norm_vec(double x,
                              double y,
                              double z)
```
    Normalized Vector.
  - max
```
public static double max(double[] vals)
```
  - min
```
public static double min(double[] vals)
```
  - range
```
public static double range(double[] vals)
```
  - length
```
public static int length(double[] vals)
```
  - median
```
public static double median(double[] vals)
```
  - mean
```
public static double mean(double[] vals)
```
  - stddev
```
public static double stddev(double[] vals)
```
  - stderr
```
public static double stderr(double[] vals)
```
  - variance
```
public static double variance(double[] vals)
```
  - meandev
```
public static double meandev(double[] vals)
```
  - sum
```
public static double sum(double[] vals)
```
  - product
```
public static double product(double[] vals)
```
  - quantile
```
public static double quantile(double[] vals,
                              double phi)
```
  - lag1
```
public static double lag1(double[] vals)
```
    Returns the lag-1 autocorrelation of a dataset;
  - rmse
```
public static double rmse(double[] pred,
                          double[] valid)
```
  - bias
```
public static double bias(double[] pred,
                          double[] valid)
```
  - nashsut
```
public static double nashsut(double[] prediction,
                             double[] validation,
                             double pow)
```
    Calculates the efficiency between a test data set and a verification data set after Nash & Sutcliffe (1970). The efficiency is described as the proportion of the cumulated cubic deviation between both data sets and the cumulated cubic deviation between the verification data set and its mean value.
    
    Parameters:
    
    prediction - the simulation data set
    
    validation - the validation (observed) data set
    
    pow - the power for the deviation terms
    
    Returns:
    
    the calculated efficiency or -9999 if an error occurs
  - nashsut_log
```
public static double nashsut_log(double[] prediction,
                                 double[] validation,
                                 double pow)
```
    Calculates the efficiency between the log values of a test data set and a verification data set after Nash & Sutcliffe (1970). The efficiency is described as the proportion of the cumulated cubic deviation between both data sets and the cumulated cubic deviation between the verification data set and its mean value.
    
    Parameters:
    
    prediction - the simulation data set
    
    validation - the validation (observed) data set
    
    pow - the power for the deviation terms
    
    Returns:
    
    the calculated log_efficiency or -9999 if an error occurs
  - err_sum
```
public static double err_sum(double[] validation,
                             double[] prediction)
```
  - ioa
```
public static double ioa(double[] prediction,
                         double[] validation,
                         double pow)
```
    Calculates the index of agreement (ioa) between a test data set and a verification data set after Willmot & Wicks (1980). The ioa is described as the proportion of the cumulated cubic deviation between both data sets and the squared sum of the absolute deviations between the verification data set and the test mean value and the test data set and its mean value.
    
    Parameters:
    
    prediction - the test Data set
    
    validation - the verification data set
    
    pow - the power
    
    Returns:
    
    the calculated ioa or -9999 if an error occurs
  - intercept
```
public static double intercept(double[] xData,
                               double[] yData)
```
  - gradient
```
public static double gradient(double[] xData,
                              double[] yData)
```
  - r2
```
public static double r2(double[] xData,
                        double[] yData)
```
  - round
```
public static double round(double val,
                           int places)
```
    Round a double value to a specified number of decimal places.
    
    Parameters:
    
    val - the value to be rounded.
    
    places - the number of decimal places to round to.
    
    Returns:
    
    val rounded to places decimal places.
  - round
```
public static float round(float val,
                          int places)
```
    Round a float value to a specified number of decimal places.
    
    Parameters:
    
    val - the value to be rounded.
    
    places - the number of decimal places to round to.
    
    Returns:
    
    val rounded to places decimal places.
  - random
```
public static double random(double min,
                            double max)
```
    Generate a random number in a range.
    
    Parameters:
    
    min -
    
    max -
    
    Returns:
    
    the random value in the min/max range

Class Stats

Method Summary

Methods inherited from class java.lang.Object

Method Detail

norm_vec

max

min

range

length

median

mean

stddev

stderr

variance

meandev

sum

product

quantile

lag1

rmse

bias

nashsut

nashsut_log

err_sum

ioa

intercept

gradient

r2

round

round

random