minor tweaks

inst/randtest.m

@@ -79,6 +79,7 @@
 
 function [pval, stat, fpr, STATS] = randtest (x, y, nreps, func, seed)
 
+
   % Check if we are running Octave or Matlab
   info = ver; 
   ISOCTAVE = any (ismember ({info.Name}, 'Octave'));
@@ -104,7 +105,7 @@
 
   % Check the number of function arguments
   if (nargin < 2)
-    error ('randtest: X and Y must be provided');
+    error ('randtest: X and Y must be provided')
   end
   if (nargin > 6)
     error ('randtest: Too many input arguments')

inst/randtest1.m

@@ -15,43 +15,43 @@
 %     to ascertain whether data sample in the column vector A comes from a
 %     population with mean equal to the value M. The value returned is a 2-
 %     tailed p-value against the null hypothesis computed using the absolute
-%     values of the test statistics. This function works by subtracting the
-%     value M from A
+%     values of the mean. This function generates resamples by independently
+%     and randomly flipping the signs of values in (A - M).
 %
 %     'PVAL = randtest1 (A, M, NREPS)' specifies the number of resamples to
 %     take in the randomization test. By default, NREPS is 5000. If the number
 %     of possible permutations is smaller than NREPS, the test becomes exact.
-%     For example, if the number of sampling units in the sample is 12, then
-%     the number of possible permutations is 2^12 = 4096, so NREPS will be
-%     truncated at 4096 and sampling will systematically evaluate all possible
-%     permutations. 
+%     For example, if the number of sampling units (i.e. rows) in the sample
+%     is 12, then the number of possible permutations is 2^12 = 4096, so NREPS
+%     will be truncated at 4096 and sampling will systematically evaluate all
+%     possible permutations. 
 %
-%     'PVAL = randtest1 (A, M, NREPS, FUNC)' also specifies a custom
-%     function calculated on the original samples, and the permuted or
-%     randomized resamples. Note that FUNC must compute a location parameter
-%     and should either be a:
+%     'PVAL = randtest1 (A, M, NREPS, FUNC)' specifies a custom function
+%     calculated on the original samples, and the permuted or randomized
+%     resamples. Note that FUNC must compute a location parameter and
+%     should either be a:
 %        o function handle or anonymous function,
 %        o string of function name, or
 %        o a cell array where the first cell is one of the above function
 %          definitions and the remaining cells are (additional) input arguments 
 %          to that function (other than the data arguments).
 %        See the built-in demos for example usage using the mean.
 %
-%     'PVAL = randtest1 (A, M, NREPS, FUNC, SEED)' initialises the
-%     Mersenne Twister random number generator using an integer SEED value so
-%     that the results of 'randtest1' results are reproducible when the test
-%     is approximate (i.e. when using randomization if not all permutations
-%     can be evaluated systematically).
+%     'PVAL = randtest1 (A, M, NREPS, FUNC, SEED)' initialises the Mersenne
+%     Twister random number generator using an integer SEED value so that
+%     the results of 'randtest1' are reproducible when the test is approximate
+%     (i.e. when using randomization if not all permutations can be 
+%     evaluated systematically).
 %
 %     'PVAL = randtest1 ([A, GA], M, ...)' also specifies the sampling
 %     units (i.e. clusters) using consecutive positive integers in GA for A.
 %     Defining the sampling units has applications for clustered resampling,
 %     for example in the cases of nested experimental designs. Note that when
 %     sampling units contain different numbers of values, function evaluations
 %     after sampling cannot be vectorized. If the parallel computing toolbox
-%     (Matlab) or package (Octave) is installed and loaded, then the function
-%     evaluations will be automatically accelerated by parallel processing
-%     on platforms with multiple processors.
+%     (Matlab) or parallel package (Octave) is installed and loaded, then the
+%     function evaluations will be automatically accelerated by parallel
+%     processing on platforms with multiple processors.
 %
 %     '[PVAL, STAT] = randtest1 (...)' also returns the test statistic.
 %
@@ -89,7 +89,7 @@
 
   % Check the number of function arguments
   if (nargin < 2)
-    error ('randtest1: A and m must be provided');
+    error ('randtest1: A and m must be provided')
   end
   if (nargin > 5)
     error ('randtest1: Too many input arguments')
@@ -203,6 +203,4 @@
 %! pval3 = randtest1 (A, M, 5000);
 %! pval4 = randtest1 (A, M, [], [], 1);
 %! pval5 = randtest1 (A, M, [], 'mean', 1);
-%! pval6 = randtest1 (A, M, [], {@mean,'omitnan'}, 1);
-%! pval7 = randtest1 (A, M, [], {'mean','omitnan'}, 1);
-%! pval8 = randtest1 (A, M, [], @smoothmedian, 1);
+%! pval6 = randtest1 (A, M, [], @smoothmedian, 1);

inst/randtest2.m

@@ -33,9 +33,6 @@
 %                across A and B corresponds to a pair of matched observations.
 %                Within each pair, the allocation of data to samples A or B is
 %                permuted or randomized.
-%        See the built-in demo 2 for example usage on how to use 'randtest2'
-%        with paired == true to perform a one-sample randomization/permutation
-%        test.
 %
 %     'PVAL = randtest2 (A, B, PAIRED, NREPS)' specifies the number of resamples
 %     without replacement to take in the randomization test. By default, NREPS
@@ -77,9 +74,9 @@
 %     and B only where the clusters have the same identifier. Note that when
 %     sampling units contain different numbers of values, function evaluations
 %     after sampling cannot be vectorized. If the parallel computing toolbox
-%     (Matlab) or package (Octave) is installed and loaded, then the function
-%     evaluations will be automatically accelerated by parallel processing
-%     on platforms with multiple processors.
+%     (Matlab) or Parallel package (Octave) is installed and loaded, then the
+%     function evaluations will be automatically accelerated by parallel
+%     processing on platforms with multiple processors.
 %
 %     '[PVAL, STAT] = randtest2 (...)' also returns the test statistic.
 %
@@ -147,7 +144,7 @@
 
   % Check the number of function arguments
   if (nargin < 2)
-    error ('randtest2: A and B must be provided');
+    error ('randtest2: A and B must be provided')
   end
   if (nargin > 6)
     error ('randtest2: Too many input arguments')
@@ -526,17 +523,6 @@
 %! % paired or matching samples (assuming i.i.d and exchangeability). (Note
 %! % that the log transformation is necessary to make the p-value two-tailed)
 %! pval = randtest2 (A, B, true, 5000, @(A, B) log (var (A) ./ var (B)))
-%!
-%! % One-sample randomization test comparing the difference in the mean between
-%! % a sample (in this case B) and a population value (in this case 120, which
-%! % we have called m). As we can see from this example, a one-sample
-%! % randomization/permutation test is a special case of a paired two-sample
-%! % test designed to flip the sign of the sample values. Note that the function
-%! % should be in mean (as demonstrated below), not the default Wasserstein
-%! % metric function.
-%! m = 120;    % population value of the mean
-%! C = B - m;  % subtract population value from the sample
-%! pval = randtest2 (C, -C, true, 5000, @(A, B) mean (A))
 
 %!demo
 %!

test/test_script.m

@@ -127,7 +127,7 @@
   bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'stud', 'nbootstd', 100);
   bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'cal');
   bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'cal', 'nbootcal', 199);
-  g = reshape (repmat ([1:5], 4, 1), 20, []);
+  g = reshape (repmat ((1:5), 4, 1), 20, []);
   bootci (1999, {@mean, y}, 'alpha', 0.1, 'strata', []);
   bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'norm', 'strata', g);
   bootci (1999, {@mean, y}, 'alpha', 0.1, 'type', 'per', 'strata', g);
@@ -718,9 +718,7 @@
   pval3 = randtest1 (A, M, 5000);
   pval4 = randtest1 (A, M, [], [], 1);
   pval5 = randtest1 (A, M, [], 'mean', 1);
-  pval6 = randtest1 (A, M, [], {@mean,'omitnan'}, 1);
-  pval7 = randtest1 (A, M, [], {'mean','omitnan'}, 1);
-  pval8 = randtest1 (A, M, [], @smoothmedian, 1);
+  pval6 = randtest1 (A, M, [], @smoothmedian, 1);
 
   % randtest2:test:1
   X = randn (3,1);