PATCH: Statistics function incorrectly computing median

[Ben Abbott]

Here's the ChangeLog, patch, and new files (I've zipped them with  
their directory structure in place).

	2008-01-23  Ben Abbott <bpabbott at mac.com>

	* statistics/base/statistics.m: Modified to calculate median and  
quantiles
	  in a manner consistent with method #7 used by GNU's R.
	* statistics/base/__quantile__.m: New function.
	* statistics/base/quantile.m: New function. Matlab compatible.
	* statistics/base/prctile.m: New function. Matlab compatible.


Index: Makefile.in
===================================================================
RCS file: /cvs/octave/scripts/statistics/base/Makefile.in,v
retrieving revision 1.14
diff -u -r1.14 Makefile.in
--- Makefile.in	12 Oct 2007 21:27:26 -0000	1.14
+++ Makefile.in	29 Jan 2008 03:42:57 -0000
@@ -32,11 +32,11 @@
  INSTALL_PROGRAM = @INSTALL_PROGRAM@
  INSTALL_DATA = @INSTALL_DATA@

-SOURCES = center.m cloglog.m cor.m corrcoef.m cov.m cut.m gls.m \
-  iqr.m kendall.m kurtosis.m logit.m mahalanobis.m mean.m meansq.m \
-  median.m mode.m moment.m ols.m ppplot.m probit.m qqplot.m range.m \
-  ranks.m run_count.m skewness.m spearman.m statistics.m std.m \
-  studentize.m table.m values.m var.m
+SOURCES = __quantile__.m center.m cloglog.m cor.m corrcoef.m cov.m \
+  cut.m gls.m iqr.m kendall.m kurtosis.m logit.m mahalanobis.m mean.m \
+  meansq.m median.m mode.m moment.m ols.m ppplot.m prctile.m probit.m \
+  qqplot.m quantile.m range.m ranks.m run_count.m skewness.m  
spearman.m \
+  statistics.m std.m studentize.m table.m values.m var.m

  DISTFILES = $(addprefix $(srcdir)/, Makefile.in $(SOURCES))

Index: statistics.m
===================================================================
RCS file: /cvs/octave/scripts/statistics/base/statistics.m,v
retrieving revision 1.14
diff -u -r1.14 statistics.m
--- statistics.m	12 Oct 2007 21:27:27 -0000	1.14
+++ statistics.m	29 Jan 2008 03:42:57 -0000
@@ -74,7 +74,7 @@
      endwhile
      rng = [0 : c-1] * stride + offset + offset2 * stride * c;
      rng2 = [0 : 2] * stride + offset + offset2 * stride * 3;
-    emp_inv(rng2) = empirical_inv ([0.25; 0.5; 0.75], X(rng));
+    emp_inv(rng2) = __quantile__ ([0.25; 0.5; 0.75], X(rng), 7);
    endfor

    S = cat (dim, min (X, [], dim), emp_inv, max (X, [], dim), mean  
(X, dim),


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