Author: celestin
Date: Mon Apr 30 06:38:00 2012
New Revision: 1332076
URL: http://svn.apache.org/viewvc?rev=1332076&view=rev
Log:
In o.a.c.m3.optimization.general.AbstractLeastSquaresOptimizer, improved the
javadoc of guessParametersErrors() (MATH-784).
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java
Modified:
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java
URL:
http://svn.apache.org/viewvc/commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java?rev=1332076&r1=1332075&r2=1332076&view=diff
==============================================================================
---
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java
(original)
+++
commons/proper/math/trunk/src/main/java/org/apache/commons/math3/optimization/general/AbstractLeastSquaresOptimizer.java
Mon Apr 30 06:38:00 2012
@@ -224,12 +224,27 @@ public abstract class AbstractLeastSquar
}
/**
- * Guess the errors in optimized parameters.
- * Guessing is covariance-based: It only gives a rough order of magnitude.
+ * <p>
+ * Returns an estimate of the standard deviation of each parameter. The
+ * returned values are the so-called (asymptotic) standard errors on the
+ * parameters, defined as {@code sd(a[i]) = sqrt(S / (n - m) * C[i][i])},
+ * where {@code a[i]} is the optimized value of the {@code i}-th parameter,
+ * {@code S} is the minimized value of the sum of squares objective
function
+ * (as returned by {@link #getChiSquare()}), {@code n} is the number of
+ * observations, {@code m} is the number of parameters and {@code C} is the
+ * covariance matrix.
+ * </p>
+ * <p>
+ * See also
+ * <a href="http://en.wikipedia.org/wiki/Least_squares";>Wikipedia</a>,
+ * or
+ * <a
href="http://mathworld.wolfram.com/LeastSquaresFitting.html";>MathWorld</a>,
+ * equations (34) and (35) for a particular case.
+ * </p>
*
- * @return errors in optimized parameters
+ * @return an estimate of the standard deviation of the optimized
parameters
* @throws org.apache.commons.math3.linear.SingularMatrixException
- * if the covariances matrix cannot be computed.
+ * if the covariance matrix cannot be computed.
* @throws NumberIsTooSmallException if the number of degrees of freedom
is not
* positive, i.e. the number of measurements is less or equal to the
number of
* parameters.
