org.hipparchus.stat.regression.RegressionResults Maven / Gradle / Ivy
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.hipparchus.stat.regression;
import java.io.Serializable;
import java.util.Arrays;
import org.hipparchus.exception.MathIllegalArgumentException;
import org.hipparchus.util.FastMath;
import org.hipparchus.util.MathUtils;
/**
* Results of a Multiple Linear Regression model fit.
*
*/
public class RegressionResults implements Serializable {
/** INDEX of Sum of Squared Errors */
private static final int SSE_IDX = 0;
/** INDEX of Sum of Squares of Model */
private static final int SST_IDX = 1;
/** INDEX of R-Squared of regression */
private static final int RSQ_IDX = 2;
/** INDEX of Mean Squared Error */
private static final int MSE_IDX = 3;
/** INDEX of Adjusted R Squared */
private static final int ADJRSQ_IDX = 4;
/** UID */
private static final long serialVersionUID = 1l;
/** regression slope parameters */
private final double[] parameters;
/** variance covariance matrix of parameters */
private final double[][] varCovData;
/** boolean flag for variance covariance matrix in symm compressed storage */
private final boolean isSymmetricVCD;
/** rank of the solution */
@SuppressWarnings("unused")
private final int rank;
/** number of observations on which results are based */
private final long nobs;
/** boolean flag indicator of whether a constant was included*/
private final boolean containsConstant;
/** array storing global results, SSE, MSE, RSQ, adjRSQ */
private final double[] globalFitInfo;
/**
* Set the default constructor to private access
* to prevent inadvertent instantiation
*/
@SuppressWarnings("unused")
private RegressionResults() {
this.parameters = null;
this.varCovData = null;
this.rank = -1;
this.nobs = -1;
this.containsConstant = false;
this.isSymmetricVCD = false;
this.globalFitInfo = null;
}
/**
* Constructor for Regression Results.
*
* @param parameters a double array with the regression slope estimates
* @param varcov the variance covariance matrix, stored either in a square matrix
* or as a compressed
* @param isSymmetricCompressed a flag which denotes that the variance covariance
* matrix is in symmetric compressed format
* @param nobs the number of observations of the regression estimation
* @param rank the number of independent variables in the regression
* @param sumy the sum of the independent variable
* @param sumysq the sum of the squared independent variable
* @param sse sum of squared errors
* @param containsConstant true model has constant, false model does not have constant
* @param copyData if true a deep copy of all input data is made, if false only references
* are copied and the RegressionResults become mutable
*/
public RegressionResults(
final double[] parameters, final double[][] varcov,
final boolean isSymmetricCompressed,
final long nobs, final int rank,
final double sumy, final double sumysq, final double sse,
final boolean containsConstant,
final boolean copyData) {
if (copyData) {
this.parameters = parameters.clone();
this.varCovData = new double[varcov.length][];
for (int i = 0; i < varcov.length; i++) {
this.varCovData[i] = varcov[i].clone();
}
} else {
this.parameters = parameters;
this.varCovData = varcov;
}
this.isSymmetricVCD = isSymmetricCompressed;
this.nobs = nobs;
this.rank = rank;
this.containsConstant = containsConstant;
this.globalFitInfo = new double[5];
Arrays.fill(this.globalFitInfo, Double.NaN);
if (rank > 0) {
this.globalFitInfo[SST_IDX] = containsConstant ?
(sumysq - sumy * sumy / nobs) : sumysq;
}
this.globalFitInfo[SSE_IDX] = sse;
this.globalFitInfo[MSE_IDX] = this.globalFitInfo[SSE_IDX] /
(nobs - rank);
this.globalFitInfo[RSQ_IDX] = 1.0 -
this.globalFitInfo[SSE_IDX] /
this.globalFitInfo[SST_IDX];
if (!containsConstant) {
this.globalFitInfo[ADJRSQ_IDX] = 1.0-
(1.0 - this.globalFitInfo[RSQ_IDX]) *
( (double) nobs / ( (double) (nobs - rank)));
} else {
this.globalFitInfo[ADJRSQ_IDX] = 1.0 - (sse * (nobs - 1.0)) /
(globalFitInfo[SST_IDX] * (nobs - rank));
}
}
/**
* Returns the parameter estimate for the regressor at the given index.
*
* A redundant regressor will have its redundancy flag set, as well as
* a parameters estimated equal to {@code Double.NaN}
*
* @param index Index.
* @return the parameters estimated for regressor at index.
* @throws MathIllegalArgumentException if {@code index} is not in the interval
* {@code [0, number of parameters)}.
*/
public double getParameterEstimate(int index) throws MathIllegalArgumentException {
if (parameters == null) {
return Double.NaN;
}
MathUtils.checkRangeInclusive(index, 0, this.parameters.length - 1);
return this.parameters[index];
}
/**
* Returns a copy of the regression parameters estimates.
*
* The parameter estimates are returned in the natural order of the data.
*
* A redundant regressor will have its redundancy flag set, as will
* a parameter estimate equal to {@code Double.NaN}.
*
* @return array of parameter estimates, null if no estimation occurred
*/
public double[] getParameterEstimates() {
if (this.parameters == null) {
return null;
}
return parameters.clone();
}
/**
* Returns the standard
* error of the parameter estimate at index,
* usually denoted s(bindex).
*
* @param index Index.
* @return the standard errors associated with parameters estimated at index.
* @throws MathIllegalArgumentException if {@code index} is not in the interval
* {@code [0, number of parameters)}.
*/
public double getStdErrorOfEstimate(int index) throws MathIllegalArgumentException {
if (parameters == null) {
return Double.NaN;
}
MathUtils.checkRangeInclusive(index, 0, this.parameters.length - 1);
double var = this.getVcvElement(index, index);
if (!Double.isNaN(var) && var > Double.MIN_VALUE) {
return FastMath.sqrt(var);
}
return Double.NaN;
}
/**
* Returns the standard
* error of the parameter estimates,
* usually denoted s(bi).
*
* If there are problems with an ill conditioned design matrix then the regressor
* which is redundant will be assigned Double.NaN.
*
* @return an array standard errors associated with parameters estimates,
* null if no estimation occurred
*/
public double[] getStdErrorOfEstimates() {
if (parameters == null) {
return null;
}
double[] se = new double[this.parameters.length];
for (int i = 0; i < this.parameters.length; i++) {
double var = this.getVcvElement(i, i);
if (!Double.isNaN(var) && var > Double.MIN_VALUE) {
se[i] = FastMath.sqrt(var);
continue;
}
se[i] = Double.NaN;
}
return se;
}
/**
* Returns the covariance between regression parameters i and j.
*
* If there are problems with an ill conditioned design matrix then the covariance
* which involves redundant columns will be assigned {@code Double.NaN}.
*
* @param i {@code i}th regression parameter.
* @param j {@code j}th regression parameter.
* @return the covariance of the parameter estimates.
* @throws MathIllegalArgumentException if {@code i} or {@code j} is not in the
* interval {@code [0, number of parameters)}.
*/
public double getCovarianceOfParameters(int i, int j) throws MathIllegalArgumentException {
if (parameters == null) {
return Double.NaN;
}
MathUtils.checkRangeInclusive(i, 0, this.parameters.length - 1);
MathUtils.checkRangeInclusive(j, 0, this.parameters.length - 1);
return this.getVcvElement(i, j);
}
/**
* Returns the number of parameters estimated in the model.
*
* This is the maximum number of regressors, some techniques may drop
* redundant parameters
*
* @return number of regressors, -1 if not estimated
*/
public int getNumberOfParameters() {
if (this.parameters == null) {
return -1;
}
return this.parameters.length;
}
/**
* Returns the number of observations added to the regression model.
*
* @return Number of observations, -1 if an error condition prevents estimation
*/
public long getN() {
return this.nobs;
}
/**
* Returns the sum of squared deviations of the y values about their mean.
*
* This is defined as SSTO
* here.
*
* If {@code n < 2}, this returns {@code Double.NaN}.
*
* @return sum of squared deviations of y values
*/
public double getTotalSumSquares() {
return this.globalFitInfo[SST_IDX];
}
/**
* Returns the sum of squared deviations of the predicted y values about
* their mean (which equals the mean of y).
*
* This is usually abbreviated SSR or SSM. It is defined as SSM
* here
*
* Preconditions:
* - At least two observations (with at least two different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated,
Double.NaN is
* returned.
*
*
* @return sum of squared deviations of predicted y values
*/
public double getRegressionSumSquares() {
return this.globalFitInfo[SST_IDX] - this.globalFitInfo[SSE_IDX];
}
/**
* Returns the
* sum of squared errors (SSE) associated with the regression
* model.
*
* The return value is constrained to be non-negative - i.e., if due to
* rounding errors the computational formula returns a negative result,
* 0 is returned.
*
* Preconditions:
* - numberOfParameters data pairs
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated,
Double,NaN is
* returned.
*
*
* @return sum of squared errors associated with the regression model
*/
public double getErrorSumSquares() {
return this.globalFitInfo[ SSE_IDX];
}
/**
* Returns the sum of squared errors divided by the degrees of freedom,
* usually abbreviated MSE.
*
* If there are fewer than numberOfParameters + 1 data pairs in the model,
* or if there is no variation in x, this returns
* Double.NaN.
*
* @return sum of squared deviations of y values
*/
public double getMeanSquareError() {
return this.globalFitInfo[ MSE_IDX];
}
/**
* Returns the
* coefficient of multiple determination,
* usually denoted r-square.
*
* Preconditions:
* - At least numberOfParameters observations (with at least numberOfParameters different x values)
* must have been added before invoking this method. If this method is
* invoked before a model can be estimated, {@code Double,NaN} is
* returned.
*
*
* @return r-square, a double in the interval [0, 1]
*/
public double getRSquared() {
return this.globalFitInfo[ RSQ_IDX];
}
/**
* Returns the adjusted R-squared statistic, defined by the formula
* R2adj = 1 - [SSR (n - 1)] / [SSTO (n - p)]
*
* where SSR is the sum of squared residuals},
* SSTO is the total sum of squares}, n is the number
* of observations and p is the number of parameters estimated (including the intercept).
*
* If the regression is estimated without an intercept term, what is returned is
* 1 - (1 - {@link #getRSquared()} ) * (n / (n - p))
*
*
* @return adjusted R-Squared statistic
*/
public double getAdjustedRSquared() {
return this.globalFitInfo[ ADJRSQ_IDX];
}
/**
* Returns true if the regression model has been computed including an intercept.
* In this case, the coefficient of the intercept is the first element of the
* {@link #getParameterEstimates() parameter estimates}.
* @return true if the model has an intercept term
*/
public boolean hasIntercept() {
return this.containsConstant;
}
/**
* Gets the i-jth element of the variance-covariance matrix.
*
* @param i first variable index
* @param j second variable index
* @return the requested variance-covariance matrix entry
*/
private double getVcvElement(int i, int j) {
if (this.isSymmetricVCD) {
if (this.varCovData.length > 1) {
//could be stored in upper or lower triangular
if (i == j) {
return varCovData[i][i];
} else if (i >= varCovData[j].length) {
return varCovData[i][j];
} else {
return varCovData[j][i];
}
} else {//could be in single array
if (i > j) {
return varCovData[0][(i + 1) * i / 2 + j];
} else {
return varCovData[0][(j + 1) * j / 2 + i];
}
}
} else {
return this.varCovData[i][j];
}
}
}