public class Statistics
extends java.lang.Object
Modifier and Type | Field and Description |
---|---|
static int |
ABSMAXIMIZATION |
static int |
ABSMINIMIZATION |
static int |
MAXIMIZATION |
static int |
MINIMIZATION |
Modifier and Type | Method and Description |
---|---|
static double |
dsGrad(double[] obs,
double[] sim) |
static double |
err_sum(double[] obs,
double[] sim) |
static double |
gradient(double[] xData,
double[] yData) |
static double |
intercept(double[] xData,
double[] yData) |
static double |
lag1(double[] vals)
Returns the lag-1 autocorrelation of a dataset;
|
static int |
length(double[] vals) |
static double[] |
linearReg(double[] xData,
double[] yData)
Calcs coefficients of linear regression between x, y data
|
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 |
norm_rmse(double[] obs,
double[] sim,
double missing) |
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 |
runoffCoefficientError(double[] obs,
double[] sim,
double[] precip)
Runoff coefficient error ROCE
|
static double |
stddev(double[] vals) |
static double |
stderr(double[] vals) |
static double |
stderrReg(double[] regcoeff,
double[] obs,
double[] sim) |
static double |
sum(double[] vals) |
static double |
variance(double[] vals) |
public static final int MAXIMIZATION
public static final int MINIMIZATION
public static final int ABSMAXIMIZATION
public static final int ABSMINIMIZATION
public static double norm_vec(double x, double y, double z)
public static double max(double[] vals)
public static double min(double[] vals)
public static double range(double[] vals)
public static int length(double[] vals)
public static double median(double[] vals)
public static double mean(double[] vals)
public static double stddev(double[] vals)
public static double stderr(double[] vals)
public static double variance(double[] vals)
public static double meandev(double[] vals)
public static double sum(double[] vals)
public static double product(double[] vals)
public static double quantile(double[] vals, double phi)
public static double lag1(double[] vals)
public static double norm_rmse(double[] obs, double[] sim, double missing)
public static double err_sum(double[] obs, double[] sim)
public static double[] linearReg(double[] xData, double[] yData)
xData
- the independent data array (x)yData
- the dependent data array (y)public static double intercept(double[] xData, double[] yData)
public static double gradient(double[] xData, double[] yData)
public static double r2(double[] xData, double[] yData)
public static double random(double min, double max)
min
- max
- public static double runoffCoefficientError(double[] obs, double[] sim, double[] precip)
obs
- sim
- precip
- public static double stderrReg(double[] regcoeff, double[] obs, double[] sim)
public static double dsGrad(double[] obs, double[] sim)
obs
- observed datasim
- simulated data(c) 2012-2022, OMSLab, Colorado State University.