Raymond | dee0849 | 2015-04-02 10:43:13 -0700 | [diff] [blame] | 1 | /* |
| 2 | * Licensed to the Apache Software Foundation (ASF) under one or more |
| 3 | * contributor license agreements. See the NOTICE file distributed with |
| 4 | * this work for additional information regarding copyright ownership. |
| 5 | * The ASF licenses this file to You under the Apache License, Version 2.0 |
| 6 | * (the "License"); you may not use this file except in compliance with |
| 7 | * the License. You may obtain a copy of the License at |
| 8 | * |
| 9 | * http://www.apache.org/licenses/LICENSE-2.0 |
| 10 | * |
| 11 | * Unless required by applicable law or agreed to in writing, software |
| 12 | * distributed under the License is distributed on an "AS IS" BASIS, |
| 13 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| 14 | * See the License for the specific language governing permissions and |
| 15 | * limitations under the License. |
| 16 | */ |
| 17 | |
| 18 | package org.apache.commons.math.stat.regression; |
| 19 | import java.io.Serializable; |
| 20 | |
| 21 | import org.apache.commons.math.MathException; |
| 22 | import org.apache.commons.math.MathRuntimeException; |
| 23 | import org.apache.commons.math.distribution.TDistribution; |
| 24 | import org.apache.commons.math.distribution.TDistributionImpl; |
| 25 | import org.apache.commons.math.exception.util.LocalizedFormats; |
| 26 | import org.apache.commons.math.util.FastMath; |
| 27 | |
| 28 | /** |
| 29 | * Estimates an ordinary least squares regression model |
| 30 | * with one independent variable. |
| 31 | * <p> |
| 32 | * <code> y = intercept + slope * x </code></p> |
| 33 | * <p> |
| 34 | * Standard errors for <code>intercept</code> and <code>slope</code> are |
| 35 | * available as well as ANOVA, r-square and Pearson's r statistics.</p> |
| 36 | * <p> |
| 37 | * Observations (x,y pairs) can be added to the model one at a time or they |
| 38 | * can be provided in a 2-dimensional array. The observations are not stored |
| 39 | * in memory, so there is no limit to the number of observations that can be |
| 40 | * added to the model.</p> |
| 41 | * <p> |
| 42 | * <strong>Usage Notes</strong>: <ul> |
| 43 | * <li> When there are fewer than two observations in the model, or when |
| 44 | * there is no variation in the x values (i.e. all x values are the same) |
| 45 | * all statistics return <code>NaN</code>. At least two observations with |
| 46 | * different x coordinates are requred to estimate a bivariate regression |
| 47 | * model. |
| 48 | * </li> |
| 49 | * <li> getters for the statistics always compute values based on the current |
| 50 | * set of observations -- i.e., you can get statistics, then add more data |
| 51 | * and get updated statistics without using a new instance. There is no |
| 52 | * "compute" method that updates all statistics. Each of the getters performs |
| 53 | * the necessary computations to return the requested statistic.</li> |
| 54 | * </ul></p> |
| 55 | * |
| 56 | * @version $Revision: 1042336 $ $Date: 2010-12-05 13:40:48 +0100 (dim. 05 déc. 2010) $ |
| 57 | */ |
| 58 | public class SimpleRegression implements Serializable { |
| 59 | |
| 60 | /** Serializable version identifier */ |
| 61 | private static final long serialVersionUID = -3004689053607543335L; |
| 62 | |
| 63 | /** the distribution used to compute inference statistics. */ |
| 64 | private TDistribution distribution; |
| 65 | |
| 66 | /** sum of x values */ |
| 67 | private double sumX = 0d; |
| 68 | |
| 69 | /** total variation in x (sum of squared deviations from xbar) */ |
| 70 | private double sumXX = 0d; |
| 71 | |
| 72 | /** sum of y values */ |
| 73 | private double sumY = 0d; |
| 74 | |
| 75 | /** total variation in y (sum of squared deviations from ybar) */ |
| 76 | private double sumYY = 0d; |
| 77 | |
| 78 | /** sum of products */ |
| 79 | private double sumXY = 0d; |
| 80 | |
| 81 | /** number of observations */ |
| 82 | private long n = 0; |
| 83 | |
| 84 | /** mean of accumulated x values, used in updating formulas */ |
| 85 | private double xbar = 0; |
| 86 | |
| 87 | /** mean of accumulated y values, used in updating formulas */ |
| 88 | private double ybar = 0; |
| 89 | |
| 90 | // ---------------------Public methods-------------------------------------- |
| 91 | |
| 92 | /** |
| 93 | * Create an empty SimpleRegression instance |
| 94 | */ |
| 95 | public SimpleRegression() { |
| 96 | this(new TDistributionImpl(1.0)); |
| 97 | } |
| 98 | |
| 99 | /** |
| 100 | * Create an empty SimpleRegression using the given distribution object to |
| 101 | * compute inference statistics. |
| 102 | * @param t the distribution used to compute inference statistics. |
| 103 | * @since 1.2 |
| 104 | * @deprecated in 2.2 (to be removed in 3.0). Please use the {@link |
| 105 | * #SimpleRegression(int) other constructor} instead. |
| 106 | */ |
| 107 | @Deprecated |
| 108 | public SimpleRegression(TDistribution t) { |
| 109 | super(); |
| 110 | setDistribution(t); |
| 111 | } |
| 112 | |
| 113 | /** |
| 114 | * Create an empty SimpleRegression. |
| 115 | * |
| 116 | * @param degrees Number of degrees of freedom of the distribution |
| 117 | * used to compute inference statistics. |
| 118 | * @since 2.2 |
| 119 | */ |
| 120 | public SimpleRegression(int degrees) { |
| 121 | setDistribution(new TDistributionImpl(degrees)); |
| 122 | } |
| 123 | |
| 124 | /** |
| 125 | * Adds the observation (x,y) to the regression data set. |
| 126 | * <p> |
| 127 | * Uses updating formulas for means and sums of squares defined in |
| 128 | * "Algorithms for Computing the Sample Variance: Analysis and |
| 129 | * Recommendations", Chan, T.F., Golub, G.H., and LeVeque, R.J. |
| 130 | * 1983, American Statistician, vol. 37, pp. 242-247, referenced in |
| 131 | * Weisberg, S. "Applied Linear Regression". 2nd Ed. 1985.</p> |
| 132 | * |
| 133 | * |
| 134 | * @param x independent variable value |
| 135 | * @param y dependent variable value |
| 136 | */ |
| 137 | public void addData(double x, double y) { |
| 138 | if (n == 0) { |
| 139 | xbar = x; |
| 140 | ybar = y; |
| 141 | } else { |
| 142 | double dx = x - xbar; |
| 143 | double dy = y - ybar; |
| 144 | sumXX += dx * dx * (double) n / (n + 1d); |
| 145 | sumYY += dy * dy * (double) n / (n + 1d); |
| 146 | sumXY += dx * dy * (double) n / (n + 1d); |
| 147 | xbar += dx / (n + 1.0); |
| 148 | ybar += dy / (n + 1.0); |
| 149 | } |
| 150 | sumX += x; |
| 151 | sumY += y; |
| 152 | n++; |
| 153 | |
| 154 | if (n > 2) { |
| 155 | distribution.setDegreesOfFreedom(n - 2); |
| 156 | } |
| 157 | } |
| 158 | |
| 159 | |
| 160 | /** |
| 161 | * Removes the observation (x,y) from the regression data set. |
| 162 | * <p> |
| 163 | * Mirrors the addData method. This method permits the use of |
| 164 | * SimpleRegression instances in streaming mode where the regression |
| 165 | * is applied to a sliding "window" of observations, however the caller is |
| 166 | * responsible for maintaining the set of observations in the window.</p> |
| 167 | * |
| 168 | * The method has no effect if there are no points of data (i.e. n=0) |
| 169 | * |
| 170 | * @param x independent variable value |
| 171 | * @param y dependent variable value |
| 172 | */ |
| 173 | public void removeData(double x, double y) { |
| 174 | if (n > 0) { |
| 175 | double dx = x - xbar; |
| 176 | double dy = y - ybar; |
| 177 | sumXX -= dx * dx * (double) n / (n - 1d); |
| 178 | sumYY -= dy * dy * (double) n / (n - 1d); |
| 179 | sumXY -= dx * dy * (double) n / (n - 1d); |
| 180 | xbar -= dx / (n - 1.0); |
| 181 | ybar -= dy / (n - 1.0); |
| 182 | sumX -= x; |
| 183 | sumY -= y; |
| 184 | n--; |
| 185 | |
| 186 | if (n > 2) { |
| 187 | distribution.setDegreesOfFreedom(n - 2); |
| 188 | } |
| 189 | } |
| 190 | } |
| 191 | |
| 192 | /** |
| 193 | * Adds the observations represented by the elements in |
| 194 | * <code>data</code>. |
| 195 | * <p> |
| 196 | * <code>(data[0][0],data[0][1])</code> will be the first observation, then |
| 197 | * <code>(data[1][0],data[1][1])</code>, etc.</p> |
| 198 | * <p> |
| 199 | * This method does not replace data that has already been added. The |
| 200 | * observations represented by <code>data</code> are added to the existing |
| 201 | * dataset.</p> |
| 202 | * <p> |
| 203 | * To replace all data, use <code>clear()</code> before adding the new |
| 204 | * data.</p> |
| 205 | * |
| 206 | * @param data array of observations to be added |
| 207 | */ |
| 208 | public void addData(double[][] data) { |
| 209 | for (int i = 0; i < data.length; i++) { |
| 210 | addData(data[i][0], data[i][1]); |
| 211 | } |
| 212 | } |
| 213 | |
| 214 | |
| 215 | /** |
| 216 | * Removes observations represented by the elements in <code>data</code>. |
| 217 | * <p> |
| 218 | * If the array is larger than the current n, only the first n elements are |
| 219 | * processed. This method permits the use of SimpleRegression instances in |
| 220 | * streaming mode where the regression is applied to a sliding "window" of |
| 221 | * observations, however the caller is responsible for maintaining the set |
| 222 | * of observations in the window.</p> |
| 223 | * <p> |
| 224 | * To remove all data, use <code>clear()</code>.</p> |
| 225 | * |
| 226 | * @param data array of observations to be removed |
| 227 | */ |
| 228 | public void removeData(double[][] data) { |
| 229 | for (int i = 0; i < data.length && n > 0; i++) { |
| 230 | removeData(data[i][0], data[i][1]); |
| 231 | } |
| 232 | } |
| 233 | |
| 234 | /** |
| 235 | * Clears all data from the model. |
| 236 | */ |
| 237 | public void clear() { |
| 238 | sumX = 0d; |
| 239 | sumXX = 0d; |
| 240 | sumY = 0d; |
| 241 | sumYY = 0d; |
| 242 | sumXY = 0d; |
| 243 | n = 0; |
| 244 | } |
| 245 | |
| 246 | /** |
| 247 | * Returns the number of observations that have been added to the model. |
| 248 | * |
| 249 | * @return n number of observations that have been added. |
| 250 | */ |
| 251 | public long getN() { |
| 252 | return n; |
| 253 | } |
| 254 | |
| 255 | /** |
| 256 | * Returns the "predicted" <code>y</code> value associated with the |
| 257 | * supplied <code>x</code> value, based on the data that has been |
| 258 | * added to the model when this method is activated. |
| 259 | * <p> |
| 260 | * <code> predict(x) = intercept + slope * x </code></p> |
| 261 | * <p> |
| 262 | * <strong>Preconditions</strong>: <ul> |
| 263 | * <li>At least two observations (with at least two different x values) |
| 264 | * must have been added before invoking this method. If this method is |
| 265 | * invoked before a model can be estimated, <code>Double,NaN</code> is |
| 266 | * returned. |
| 267 | * </li></ul></p> |
| 268 | * |
| 269 | * @param x input <code>x</code> value |
| 270 | * @return predicted <code>y</code> value |
| 271 | */ |
| 272 | public double predict(double x) { |
| 273 | double b1 = getSlope(); |
| 274 | return getIntercept(b1) + b1 * x; |
| 275 | } |
| 276 | |
| 277 | /** |
| 278 | * Returns the intercept of the estimated regression line. |
| 279 | * <p> |
| 280 | * The least squares estimate of the intercept is computed using the |
| 281 | * <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>. |
| 282 | * The intercept is sometimes denoted b0.</p> |
| 283 | * <p> |
| 284 | * <strong>Preconditions</strong>: <ul> |
| 285 | * <li>At least two observations (with at least two different x values) |
| 286 | * must have been added before invoking this method. If this method is |
| 287 | * invoked before a model can be estimated, <code>Double,NaN</code> is |
| 288 | * returned. |
| 289 | * </li></ul></p> |
| 290 | * |
| 291 | * @return the intercept of the regression line |
| 292 | */ |
| 293 | public double getIntercept() { |
| 294 | return getIntercept(getSlope()); |
| 295 | } |
| 296 | |
| 297 | /** |
| 298 | * Returns the slope of the estimated regression line. |
| 299 | * <p> |
| 300 | * The least squares estimate of the slope is computed using the |
| 301 | * <a href="http://www.xycoon.com/estimation4.htm">normal equations</a>. |
| 302 | * The slope is sometimes denoted b1.</p> |
| 303 | * <p> |
| 304 | * <strong>Preconditions</strong>: <ul> |
| 305 | * <li>At least two observations (with at least two different x values) |
| 306 | * must have been added before invoking this method. If this method is |
| 307 | * invoked before a model can be estimated, <code>Double.NaN</code> is |
| 308 | * returned. |
| 309 | * </li></ul></p> |
| 310 | * |
| 311 | * @return the slope of the regression line |
| 312 | */ |
| 313 | public double getSlope() { |
| 314 | if (n < 2) { |
| 315 | return Double.NaN; //not enough data |
| 316 | } |
| 317 | if (FastMath.abs(sumXX) < 10 * Double.MIN_VALUE) { |
| 318 | return Double.NaN; //not enough variation in x |
| 319 | } |
| 320 | return sumXY / sumXX; |
| 321 | } |
| 322 | |
| 323 | /** |
| 324 | * Returns the <a href="http://www.xycoon.com/SumOfSquares.htm"> |
| 325 | * sum of squared errors</a> (SSE) associated with the regression |
| 326 | * model. |
| 327 | * <p> |
| 328 | * The sum is computed using the computational formula</p> |
| 329 | * <p> |
| 330 | * <code>SSE = SYY - (SXY * SXY / SXX)</code></p> |
| 331 | * <p> |
| 332 | * where <code>SYY</code> is the sum of the squared deviations of the y |
| 333 | * values about their mean, <code>SXX</code> is similarly defined and |
| 334 | * <code>SXY</code> is the sum of the products of x and y mean deviations. |
| 335 | * </p><p> |
| 336 | * The sums are accumulated using the updating algorithm referenced in |
| 337 | * {@link #addData}.</p> |
| 338 | * <p> |
| 339 | * The return value is constrained to be non-negative - i.e., if due to |
| 340 | * rounding errors the computational formula returns a negative result, |
| 341 | * 0 is returned.</p> |
| 342 | * <p> |
| 343 | * <strong>Preconditions</strong>: <ul> |
| 344 | * <li>At least two observations (with at least two different x values) |
| 345 | * must have been added before invoking this method. If this method is |
| 346 | * invoked before a model can be estimated, <code>Double,NaN</code> is |
| 347 | * returned. |
| 348 | * </li></ul></p> |
| 349 | * |
| 350 | * @return sum of squared errors associated with the regression model |
| 351 | */ |
| 352 | public double getSumSquaredErrors() { |
| 353 | return FastMath.max(0d, sumYY - sumXY * sumXY / sumXX); |
| 354 | } |
| 355 | |
| 356 | /** |
| 357 | * Returns the sum of squared deviations of the y values about their mean. |
| 358 | * <p> |
| 359 | * This is defined as SSTO |
| 360 | * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a>.</p> |
| 361 | * <p> |
| 362 | * If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p> |
| 363 | * |
| 364 | * @return sum of squared deviations of y values |
| 365 | */ |
| 366 | public double getTotalSumSquares() { |
| 367 | if (n < 2) { |
| 368 | return Double.NaN; |
| 369 | } |
| 370 | return sumYY; |
| 371 | } |
| 372 | |
| 373 | /** |
| 374 | * Returns the sum of squared deviations of the x values about their mean. |
| 375 | * |
| 376 | * If <code>n < 2</code>, this returns <code>Double.NaN</code>.</p> |
| 377 | * |
| 378 | * @return sum of squared deviations of x values |
| 379 | */ |
| 380 | public double getXSumSquares() { |
| 381 | if (n < 2) { |
| 382 | return Double.NaN; |
| 383 | } |
| 384 | return sumXX; |
| 385 | } |
| 386 | |
| 387 | /** |
| 388 | * Returns the sum of crossproducts, x<sub>i</sub>*y<sub>i</sub>. |
| 389 | * |
| 390 | * @return sum of cross products |
| 391 | */ |
| 392 | public double getSumOfCrossProducts() { |
| 393 | return sumXY; |
| 394 | } |
| 395 | |
| 396 | /** |
| 397 | * Returns the sum of squared deviations of the predicted y values about |
| 398 | * their mean (which equals the mean of y). |
| 399 | * <p> |
| 400 | * This is usually abbreviated SSR or SSM. It is defined as SSM |
| 401 | * <a href="http://www.xycoon.com/SumOfSquares.htm">here</a></p> |
| 402 | * <p> |
| 403 | * <strong>Preconditions</strong>: <ul> |
| 404 | * <li>At least two observations (with at least two different x values) |
| 405 | * must have been added before invoking this method. If this method is |
| 406 | * invoked before a model can be estimated, <code>Double.NaN</code> is |
| 407 | * returned. |
| 408 | * </li></ul></p> |
| 409 | * |
| 410 | * @return sum of squared deviations of predicted y values |
| 411 | */ |
| 412 | public double getRegressionSumSquares() { |
| 413 | return getRegressionSumSquares(getSlope()); |
| 414 | } |
| 415 | |
| 416 | /** |
| 417 | * Returns the sum of squared errors divided by the degrees of freedom, |
| 418 | * usually abbreviated MSE. |
| 419 | * <p> |
| 420 | * If there are fewer than <strong>three</strong> data pairs in the model, |
| 421 | * or if there is no variation in <code>x</code>, this returns |
| 422 | * <code>Double.NaN</code>.</p> |
| 423 | * |
| 424 | * @return sum of squared deviations of y values |
| 425 | */ |
| 426 | public double getMeanSquareError() { |
| 427 | if (n < 3) { |
| 428 | return Double.NaN; |
| 429 | } |
| 430 | return getSumSquaredErrors() / (n - 2); |
| 431 | } |
| 432 | |
| 433 | /** |
| 434 | * Returns <a href="http://mathworld.wolfram.com/CorrelationCoefficient.html"> |
| 435 | * Pearson's product moment correlation coefficient</a>, |
| 436 | * usually denoted r. |
| 437 | * <p> |
| 438 | * <strong>Preconditions</strong>: <ul> |
| 439 | * <li>At least two observations (with at least two different x values) |
| 440 | * must have been added before invoking this method. If this method is |
| 441 | * invoked before a model can be estimated, <code>Double,NaN</code> is |
| 442 | * returned. |
| 443 | * </li></ul></p> |
| 444 | * |
| 445 | * @return Pearson's r |
| 446 | */ |
| 447 | public double getR() { |
| 448 | double b1 = getSlope(); |
| 449 | double result = FastMath.sqrt(getRSquare()); |
| 450 | if (b1 < 0) { |
| 451 | result = -result; |
| 452 | } |
| 453 | return result; |
| 454 | } |
| 455 | |
| 456 | /** |
| 457 | * Returns the <a href="http://www.xycoon.com/coefficient1.htm"> |
| 458 | * coefficient of determination</a>, |
| 459 | * usually denoted r-square. |
| 460 | * <p> |
| 461 | * <strong>Preconditions</strong>: <ul> |
| 462 | * <li>At least two observations (with at least two different x values) |
| 463 | * must have been added before invoking this method. If this method is |
| 464 | * invoked before a model can be estimated, <code>Double,NaN</code> is |
| 465 | * returned. |
| 466 | * </li></ul></p> |
| 467 | * |
| 468 | * @return r-square |
| 469 | */ |
| 470 | public double getRSquare() { |
| 471 | double ssto = getTotalSumSquares(); |
| 472 | return (ssto - getSumSquaredErrors()) / ssto; |
| 473 | } |
| 474 | |
| 475 | /** |
| 476 | * Returns the <a href="http://www.xycoon.com/standarderrorb0.htm"> |
| 477 | * standard error of the intercept estimate</a>, |
| 478 | * usually denoted s(b0). |
| 479 | * <p> |
| 480 | * If there are fewer that <strong>three</strong> observations in the |
| 481 | * model, or if there is no variation in x, this returns |
| 482 | * <code>Double.NaN</code>.</p> |
| 483 | * |
| 484 | * @return standard error associated with intercept estimate |
| 485 | */ |
| 486 | public double getInterceptStdErr() { |
| 487 | return FastMath.sqrt( |
| 488 | getMeanSquareError() * ((1d / (double) n) + (xbar * xbar) / sumXX)); |
| 489 | } |
| 490 | |
| 491 | /** |
| 492 | * Returns the <a href="http://www.xycoon.com/standerrorb(1).htm">standard |
| 493 | * error of the slope estimate</a>, |
| 494 | * usually denoted s(b1). |
| 495 | * <p> |
| 496 | * If there are fewer that <strong>three</strong> data pairs in the model, |
| 497 | * or if there is no variation in x, this returns <code>Double.NaN</code>. |
| 498 | * </p> |
| 499 | * |
| 500 | * @return standard error associated with slope estimate |
| 501 | */ |
| 502 | public double getSlopeStdErr() { |
| 503 | return FastMath.sqrt(getMeanSquareError() / sumXX); |
| 504 | } |
| 505 | |
| 506 | /** |
| 507 | * Returns the half-width of a 95% confidence interval for the slope |
| 508 | * estimate. |
| 509 | * <p> |
| 510 | * The 95% confidence interval is</p> |
| 511 | * <p> |
| 512 | * <code>(getSlope() - getSlopeConfidenceInterval(), |
| 513 | * getSlope() + getSlopeConfidenceInterval())</code></p> |
| 514 | * <p> |
| 515 | * If there are fewer that <strong>three</strong> observations in the |
| 516 | * model, or if there is no variation in x, this returns |
| 517 | * <code>Double.NaN</code>.</p> |
| 518 | * <p> |
| 519 | * <strong>Usage Note</strong>:<br> |
| 520 | * The validity of this statistic depends on the assumption that the |
| 521 | * observations included in the model are drawn from a |
| 522 | * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html"> |
| 523 | * Bivariate Normal Distribution</a>.</p> |
| 524 | * |
| 525 | * @return half-width of 95% confidence interval for the slope estimate |
| 526 | * @throws MathException if the confidence interval can not be computed. |
| 527 | */ |
| 528 | public double getSlopeConfidenceInterval() throws MathException { |
| 529 | return getSlopeConfidenceInterval(0.05d); |
| 530 | } |
| 531 | |
| 532 | /** |
| 533 | * Returns the half-width of a (100-100*alpha)% confidence interval for |
| 534 | * the slope estimate. |
| 535 | * <p> |
| 536 | * The (100-100*alpha)% confidence interval is </p> |
| 537 | * <p> |
| 538 | * <code>(getSlope() - getSlopeConfidenceInterval(), |
| 539 | * getSlope() + getSlopeConfidenceInterval())</code></p> |
| 540 | * <p> |
| 541 | * To request, for example, a 99% confidence interval, use |
| 542 | * <code>alpha = .01</code></p> |
| 543 | * <p> |
| 544 | * <strong>Usage Note</strong>:<br> |
| 545 | * The validity of this statistic depends on the assumption that the |
| 546 | * observations included in the model are drawn from a |
| 547 | * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html"> |
| 548 | * Bivariate Normal Distribution</a>.</p> |
| 549 | * <p> |
| 550 | * <strong> Preconditions:</strong><ul> |
| 551 | * <li>If there are fewer that <strong>three</strong> observations in the |
| 552 | * model, or if there is no variation in x, this returns |
| 553 | * <code>Double.NaN</code>. |
| 554 | * </li> |
| 555 | * <li><code>(0 < alpha < 1)</code>; otherwise an |
| 556 | * <code>IllegalArgumentException</code> is thrown. |
| 557 | * </li></ul></p> |
| 558 | * |
| 559 | * @param alpha the desired significance level |
| 560 | * @return half-width of 95% confidence interval for the slope estimate |
| 561 | * @throws MathException if the confidence interval can not be computed. |
| 562 | */ |
| 563 | public double getSlopeConfidenceInterval(double alpha) |
| 564 | throws MathException { |
| 565 | if (alpha >= 1 || alpha <= 0) { |
| 566 | throw MathRuntimeException.createIllegalArgumentException( |
| 567 | LocalizedFormats.OUT_OF_BOUND_SIGNIFICANCE_LEVEL, |
| 568 | alpha, 0.0, 1.0); |
| 569 | } |
| 570 | return getSlopeStdErr() * |
| 571 | distribution.inverseCumulativeProbability(1d - alpha / 2d); |
| 572 | } |
| 573 | |
| 574 | /** |
| 575 | * Returns the significance level of the slope (equiv) correlation. |
| 576 | * <p> |
| 577 | * Specifically, the returned value is the smallest <code>alpha</code> |
| 578 | * such that the slope confidence interval with significance level |
| 579 | * equal to <code>alpha</code> does not include <code>0</code>. |
| 580 | * On regression output, this is often denoted <code>Prob(|t| > 0)</code> |
| 581 | * </p><p> |
| 582 | * <strong>Usage Note</strong>:<br> |
| 583 | * The validity of this statistic depends on the assumption that the |
| 584 | * observations included in the model are drawn from a |
| 585 | * <a href="http://mathworld.wolfram.com/BivariateNormalDistribution.html"> |
| 586 | * Bivariate Normal Distribution</a>.</p> |
| 587 | * <p> |
| 588 | * If there are fewer that <strong>three</strong> observations in the |
| 589 | * model, or if there is no variation in x, this returns |
| 590 | * <code>Double.NaN</code>.</p> |
| 591 | * |
| 592 | * @return significance level for slope/correlation |
| 593 | * @throws MathException if the significance level can not be computed. |
| 594 | */ |
| 595 | public double getSignificance() throws MathException { |
| 596 | return 2d * (1.0 - distribution.cumulativeProbability( |
| 597 | FastMath.abs(getSlope()) / getSlopeStdErr())); |
| 598 | } |
| 599 | |
| 600 | // ---------------------Private methods----------------------------------- |
| 601 | |
| 602 | /** |
| 603 | * Returns the intercept of the estimated regression line, given the slope. |
| 604 | * <p> |
| 605 | * Will return <code>NaN</code> if slope is <code>NaN</code>.</p> |
| 606 | * |
| 607 | * @param slope current slope |
| 608 | * @return the intercept of the regression line |
| 609 | */ |
| 610 | private double getIntercept(double slope) { |
| 611 | return (sumY - slope * sumX) / n; |
| 612 | } |
| 613 | |
| 614 | /** |
| 615 | * Computes SSR from b1. |
| 616 | * |
| 617 | * @param slope regression slope estimate |
| 618 | * @return sum of squared deviations of predicted y values |
| 619 | */ |
| 620 | private double getRegressionSumSquares(double slope) { |
| 621 | return slope * slope * sumXX; |
| 622 | } |
| 623 | |
| 624 | /** |
| 625 | * Modify the distribution used to compute inference statistics. |
| 626 | * @param value the new distribution |
| 627 | * @since 1.2 |
| 628 | * @deprecated in 2.2 (to be removed in 3.0). |
| 629 | */ |
| 630 | @Deprecated |
| 631 | public void setDistribution(TDistribution value) { |
| 632 | distribution = value; |
| 633 | |
| 634 | // modify degrees of freedom |
| 635 | if (n > 2) { |
| 636 | distribution.setDegreesOfFreedom(n - 2); |
| 637 | } |
| 638 | } |
| 639 | } |