| /* |
| * 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.apache.commons.math.stat.correlation; |
| |
| import org.apache.commons.math.MathRuntimeException; |
| import org.apache.commons.math.exception.util.LocalizedFormats; |
| import org.apache.commons.math.linear.BlockRealMatrix; |
| import org.apache.commons.math.linear.RealMatrix; |
| import org.apache.commons.math.stat.ranking.NaturalRanking; |
| import org.apache.commons.math.stat.ranking.RankingAlgorithm; |
| |
| /** |
| * <p>Spearman's rank correlation. This implementation performs a rank |
| * transformation on the input data and then computes {@link PearsonsCorrelation} |
| * on the ranked data.</p> |
| * |
| * <p>By default, ranks are computed using {@link NaturalRanking} with default |
| * strategies for handling NaNs and ties in the data (NaNs maximal, ties averaged). |
| * The ranking algorithm can be set using a constructor argument.</p> |
| * |
| * @since 2.0 |
| * @version $Revision: 983921 $ $Date: 2010-08-10 12:46:06 +0200 (mar. 10 août 2010) $ |
| */ |
| |
| public class SpearmansCorrelation { |
| |
| /** Input data */ |
| private final RealMatrix data; |
| |
| /** Ranking algorithm */ |
| private final RankingAlgorithm rankingAlgorithm; |
| |
| /** Rank correlation */ |
| private final PearsonsCorrelation rankCorrelation; |
| |
| /** |
| * Create a SpearmansCorrelation with the given input data matrix |
| * and ranking algorithm. |
| * |
| * @param dataMatrix matrix of data with columns representing |
| * variables to correlate |
| * @param rankingAlgorithm ranking algorithm |
| */ |
| public SpearmansCorrelation(final RealMatrix dataMatrix, final RankingAlgorithm rankingAlgorithm) { |
| this.data = dataMatrix.copy(); |
| this.rankingAlgorithm = rankingAlgorithm; |
| rankTransform(data); |
| rankCorrelation = new PearsonsCorrelation(data); |
| } |
| |
| /** |
| * Create a SpearmansCorrelation from the given data matrix. |
| * |
| * @param dataMatrix matrix of data with columns representing |
| * variables to correlate |
| */ |
| public SpearmansCorrelation(final RealMatrix dataMatrix) { |
| this(dataMatrix, new NaturalRanking()); |
| } |
| |
| /** |
| * Create a SpearmansCorrelation without data. |
| */ |
| public SpearmansCorrelation() { |
| data = null; |
| this.rankingAlgorithm = new NaturalRanking(); |
| rankCorrelation = null; |
| } |
| |
| /** |
| * Calculate the Spearman Rank Correlation Matrix. |
| * |
| * @return Spearman Rank Correlation Matrix |
| */ |
| public RealMatrix getCorrelationMatrix() { |
| return rankCorrelation.getCorrelationMatrix(); |
| } |
| |
| /** |
| * Returns a {@link PearsonsCorrelation} instance constructed from the |
| * ranked input data. That is, |
| * <code>new SpearmansCorrelation(matrix).getRankCorrelation()</code> |
| * is equivalent to |
| * <code>new PearsonsCorrelation(rankTransform(matrix))</code> where |
| * <code>rankTransform(matrix)</code> is the result of applying the |
| * configured <code>RankingAlgorithm</code> to each of the columns of |
| * <code>matrix.</code> |
| * |
| * @return PearsonsCorrelation among ranked column data |
| */ |
| public PearsonsCorrelation getRankCorrelation() { |
| return rankCorrelation; |
| } |
| |
| /** |
| * Computes the Spearman's rank correlation matrix for the columns of the |
| * input matrix. |
| * |
| * @param matrix matrix with columns representing variables to correlate |
| * @return correlation matrix |
| */ |
| public RealMatrix computeCorrelationMatrix(RealMatrix matrix) { |
| RealMatrix matrixCopy = matrix.copy(); |
| rankTransform(matrixCopy); |
| return new PearsonsCorrelation().computeCorrelationMatrix(matrixCopy); |
| } |
| |
| /** |
| * Computes the Spearman's rank correlation matrix for the columns of the |
| * input rectangular array. The columns of the array represent values |
| * of variables to be correlated. |
| * |
| * @param matrix matrix with columns representing variables to correlate |
| * @return correlation matrix |
| */ |
| public RealMatrix computeCorrelationMatrix(double[][] matrix) { |
| return computeCorrelationMatrix(new BlockRealMatrix(matrix)); |
| } |
| |
| /** |
| * Computes the Spearman's rank correlation coefficient between the two arrays. |
| * |
| * </p>Throws IllegalArgumentException if the arrays do not have the same length |
| * or their common length is less than 2</p> |
| * |
| * @param xArray first data array |
| * @param yArray second data array |
| * @return Returns Spearman's rank correlation coefficient for the two arrays |
| * @throws IllegalArgumentException if the arrays lengths do not match or |
| * there is insufficient data |
| */ |
| public double correlation(final double[] xArray, final double[] yArray) |
| throws IllegalArgumentException { |
| if (xArray.length != yArray.length) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.DIMENSIONS_MISMATCH_SIMPLE, xArray.length, yArray.length); |
| } else if (xArray.length < 2) { |
| throw MathRuntimeException.createIllegalArgumentException( |
| LocalizedFormats.INSUFFICIENT_DIMENSION, xArray.length, 2); |
| } else { |
| return new PearsonsCorrelation().correlation(rankingAlgorithm.rank(xArray), |
| rankingAlgorithm.rank(yArray)); |
| } |
| } |
| |
| /** |
| * Applies rank transform to each of the columns of <code>matrix</code> |
| * using the current <code>rankingAlgorithm</code> |
| * |
| * @param matrix matrix to transform |
| */ |
| private void rankTransform(RealMatrix matrix) { |
| for (int i = 0; i < matrix.getColumnDimension(); i++) { |
| matrix.setColumn(i, rankingAlgorithm.rank(matrix.getColumn(i))); |
| } |
| } |
| } |