public static class MultiRegression.MultivariateRegression
extends java.lang.Object
Modifier and Type | Field and Description |
---|---|
double |
AdjustedrSquared
adjusted rSquared of the entire regression
|
double |
bestMedian
best median, as established by the robust regression
|
MultiRegression.MultivariateRegression |
bestMultivariateRegression
best multivariate regression
|
double[] |
beta
vector of beta parameters
|
MultiRegression.MultivariateRegression |
cleanedMultivariateRegression
best multivariate regression without outliers
|
org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression |
OLSMultiReg
multivariate regression
|
java.util.ArrayList<double[]> |
outliersX
x's outliers
|
java.util.ArrayList<java.lang.Double> |
outliersY
y's outliers
|
double[] |
parametersStdErrors
standard errors
|
double[][] |
parametersVariance
variance matrix
|
double |
regressandVariance
variance matrix
|
double[] |
residuals
residuals
|
double |
rSquared
r squared of the entire regression
|
double |
sigma
sigma
|
double[][] |
x2regress
x to regress
|
double[][] |
x2regressClean
x without outliers
|
double[] |
y2regress
y to regress
|
double[] |
y2regressClean
y without outliers
|
Constructor and Description |
---|
MultivariateRegression(double[] localFile2regress,
double[][] remoteFile2regress)
instanciate a multivariate regression using the samples provided in the routine
|
Modifier and Type | Method and Description |
---|---|
void |
buildRLSRegression()
Builds a new LinearRegression without outliers found by buildWeight
|
static int |
combinations(int n,
int r)
number of combination of r elements among n elements
|
void |
findBestMultipleRegression()
created to run sub-samples of the data and find the regression with least median residuals squares
|
double |
getMyMedian(java.util.List<java.lang.Double> list)
return the median
|
long |
getRandomSeed()
get the seed for the random number generator
|
double[][] |
getSampleFeatures(java.util.List<java.lang.Integer> sampleIndex,
int sampleSize)
return the features
|
java.util.List<java.lang.Integer> |
getSampleIndex(int sampleSize)
return samples
|
double[] |
getSamplePredictedVariables(java.util.List<java.lang.Integer> sampleIndex,
int sampleSize)
predicted variables
|
int |
makeCumul(double[] in)
sum the provided samples
|
void |
setRandomSeed(long randomseed)
Set the seed for the random number generator
|
public org.apache.commons.math3.stat.regression.OLSMultipleLinearRegression OLSMultiReg
public double[] beta
public double[] residuals
public double[][] parametersVariance
public double[] parametersStdErrors
public double regressandVariance
public double rSquared
public double AdjustedrSquared
public double sigma
public double[][] x2regress
public double[] y2regress
public double[][] x2regressClean
public double[] y2regressClean
public java.util.ArrayList<double[]> outliersX
public java.util.ArrayList<java.lang.Double> outliersY
public double bestMedian
public MultiRegression.MultivariateRegression bestMultivariateRegression
public MultiRegression.MultivariateRegression cleanedMultivariateRegression
public MultivariateRegression(double[] localFile2regress, double[][] remoteFile2regress)
public void findBestMultipleRegression()
public void buildRLSRegression()
public void setRandomSeed(long randomseed)
randomseed
- the seedpublic long getRandomSeed()
public java.util.List<java.lang.Integer> getSampleIndex(int sampleSize)
sampleSize
- amount of samplepublic double[][] getSampleFeatures(java.util.List<java.lang.Integer> sampleIndex, int sampleSize)
sampleIndex
- samplessampleSize
- sample sizepublic double[] getSamplePredictedVariables(java.util.List<java.lang.Integer> sampleIndex, int sampleSize)
sampleIndex
- samplessampleSize
- sample sizepublic int makeCumul(double[] in)
in
- samplespublic double getMyMedian(java.util.List<java.lang.Double> list)
list
- samplespublic static int combinations(int n, int r)
n
- total amount of elementsr
- amount of subsamples