changed demos and tests of case resampling for regression to use buil…

…tin function @mldivide
gnu-octave · Apr 13, 2024 · 786200d · 786200d
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 name: statistics-resampling
-version: 5.5.8
-date: 2024-04-02
+version: 5.5.9
+date: 2024-04-12
 author: Andrew Penn <[email protected]>
 maintainer: Andrew Penn <[email protected]>
 title: A statistics package with a variety of resampling tools

diff --git a/inst/bootci.m b/inst/bootci.m
@@ -431,6 +431,8 @@
 %!
 %! % 95% BCa bootstrap confidence intervals for the mean
 %! ci = bootci (1999, @mean, data)
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -463,6 +465,8 @@
 %!
 %! % 90% percentile bootstrap confidence intervals for the variance
 %! ci = bootci (1999, {{@var,1}, data}, 'type', 'per', 'alpha', 0.1)
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -472,6 +476,8 @@
 %!
 %! % 90% BCa bootstrap confidence intervals for the variance
 %! ci = bootci (1999, {{@var,1}, data}, 'type', 'bca', 'alpha', 0.1)
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -606,6 +612,21 @@
 %!   %<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 %! 
 %! end
+%!
+%! % Please be patient, the calculations will be completed soon...
+
+%!demo
+%!
+%! % Input dataset
+%! y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
+%!
+%! % 95% BCa confidence interval for regression coefficients 
+%! ci = bootci (1999, @mldivide, X, y)
+%!
+%! % N.B: Consider using either the 'bootwild', 'bootbayes' or 'bootlm'
+%! % functions for bootstrapping linear regression problems instead
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -780,15 +801,15 @@
 %!   y = randn (20,1); x = randn (20,1); X = [ones(20,1),x];
 %!   bootci (1999, @cor, x, y);
 %!   bootci (1999, {@cor, x, y}, 'strata', g);
-%!   bootci (1999, @(y,X) pinv(X)*y, y, X);
-%!   bootci (1999, @(y,X) pinv(X)*y, y, X, 'alpha', 0.1);
-%!   bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1);
-%!   bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'norm');
-%!   bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'per');
-%!   bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'basic');
-%!   bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'bca');
-%!   bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'stud');
-%!   bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'cal');
+%!   bootci (1999, @mldivide, X, y);
+%!   bootci (1999, @mldivide, X, y, 'alpha', 0.1);
+%!   bootci (1999, {@mldivide, X, y}, 'alpha', 0.1);
+%!   bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'norm');
+%!   bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'per');
+%!   bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'basic');
+%!   bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'bca');
+%!   bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'stud');
+%!   bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'cal');
 %! catch
 %!   warning ('on', 'bootknife:parallel')
 %!   rethrow (lasterror)

diff --git a/inst/bootclust.m b/inst/bootclust.m
@@ -507,7 +507,7 @@
   % and skewness
   switch (nalpha)
     case 1
-      % No adjustements made
+      % No adjustments made
       probs = repmat (probs, m, 1);
     case 2
       % Create distribution functions
@@ -800,6 +800,8 @@ function print_output (stats, nboot, nalpha, alpha, probs, m, bootfun_str, ...
 %!
 %! % 95% expanded BCa bootstrap confidence intervals for the mean
 %! bootclust (data, 1999, @mean);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -812,6 +814,8 @@ function print_output (stats, nboot, nalpha, alpha, probs, m, bootfun_str, ...
 %! % 95% expanded BCa bootstrap confidence intervals for the mean with
 %! % cluster resampling
 %! bootclust (data, 1999, @mean, [0.025,0.975], clustid);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -822,6 +826,8 @@ function print_output (stats, nboot, nalpha, alpha, probs, m, bootfun_str, ...
 %! % 90% equal-tailed percentile bootstrap confidence intervals for
 %! % the variance
 %! bootclust (data, 1999, {@var, 1}, 0.1);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -834,6 +840,8 @@ function print_output (stats, nboot, nalpha, alpha, probs, m, bootfun_str, ...
 %! % 90% equal-tailed percentile bootstrap confidence intervals for
 %! % the variance
 %! bootclust (data, 1999, {@var, 1}, 0.1, clustid);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -843,6 +851,8 @@ function print_output (stats, nboot, nalpha, alpha, probs, m, bootfun_str, ...
 %!
 %! % 90% BCa bootstrap confidence intervals for the variance
 %! bootclust (data, 1999, {@var, 1}, [0.05 0.95]);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -854,14 +864,18 @@ function print_output (stats, nboot, nalpha, alpha, probs, m, bootfun_str, ...
 %!
 %! % 90% BCa bootstrap confidence intervals for the variance
 %! bootclust (data, 1999, {@var, 1}, [0.05 0.95], clustid);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
 %! % Input dataset
 %! y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
 %!
 %! % 90% BCa confidence interval for regression coefficients 
-%! bootclust ({y,X}, 1999, @(y,X) X\y, [0.05 0.95]); % Could also use @regress
+%! bootclust ({X,y}, 1999, @mldivide, [0.05 0.95]);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -870,7 +884,9 @@ function print_output (stats, nboot, nalpha, alpha, probs, m, bootfun_str, ...
 %! clustid = [1;1;1;1;2;2;2;3;3;3;3;4;4;4;4;4;5;5;5;6];
 %!
 %! % 90% BCa confidence interval for regression coefficients 
-%! bootclust ({y,X}, 1999, @(y,X) X\y, [0.05 0.95], clustid);
+%! bootclust ({X,y}, 1999, @mldivide, [0.05 0.95], clustid);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -951,12 +967,12 @@ function print_output (stats, nboot, nalpha, alpha, probs, m, bootfun_str, ...
 %! y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
 %! stats = bootclust ({x,y}, 1999, @cor);
 %! stats = bootclust ({x,y}, 1999, @cor, [], clustid);
-%! stats = bootclust ({y,x}, 1999, @(y,x) pinv(x)*y); % Could use @regress
-%! stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y);
-%! stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y, [], clustid);
-%! stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y, [], clustid, true);
-%! stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y, [], clustid, true, 1);
-%! stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y, [.05,.95], clustid);
+%! stats = bootclust ({x,y}, 1999, @mldivide);
+%! stats = bootclust ({X,y}, 1999, @mldivide);
+%! stats = bootclust ({X,y}, 1999, @mldivide, [], clustid);
+%! stats = bootclust ({X,y}, 1999, @mldivide, [], clustid, true);
+%! stats = bootclust ({X,y}, 1999, @mldivide, [], clustid, true, 1);
+%! stats = bootclust ({X,y}, 1999, @mldivide, [.05,.95], clustid);
 
 %!test
 %! % Air conditioning failure times in Table 1.2 of Davison A.C. and

diff --git a/inst/bootknife.m b/inst/bootknife.m
@@ -1048,6 +1048,8 @@ function print_output (stats, nboot, alpha, l, m, bootfun_str, strata)
 %!
 %! % 95% expanded BCa bootstrap confidence intervals for the mean
 %! bootknife (data, 1999, @mean);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -1081,6 +1083,8 @@ function print_output (stats, nboot, alpha, l, m, bootfun_str, strata)
 %! % 90% equal-tailed percentile bootstrap confidence intervals for
 %! % the variance
 %! bootknife (data, 1999, {@var, 1}, 0.1);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -1090,6 +1094,8 @@ function print_output (stats, nboot, alpha, l, m, bootfun_str, strata)
 %!
 %! % 90% BCa bootstrap confidence intervals for the variance
 %! bootknife (data, 1999, {@var, 1}, [0.05 0.95]);
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -1120,7 +1126,12 @@ function print_output (stats, nboot, alpha, l, m, bootfun_str, strata)
 %! y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
 %!
 %! % 90% BCa confidence interval for regression coefficients 
-%! bootknife ({y,X}, 1999, @(y,X) X\y, [0.05 0.95]); % Could also use @regress
+%! bootknife ({X,y}, 1999, @mldivide, [0.05 0.95]);
+%!
+%! % N.B: Consider using either the 'bootwild', 'bootbayes' or 'bootlm'
+%! % functions for bootstrapping linear regression problems instead
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -1226,6 +1237,8 @@ function print_output (stats, nboot, alpha, l, m, bootfun_str, strata)
 %!   %<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
 %! 
 %! end
+%!
+%! % Please be patient, the calculations will be completed soon...
 
 %!demo
 %!
@@ -1496,11 +1509,11 @@ function print_output (stats, nboot, alpha, l, m, bootfun_str, strata)
 %!   y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
 %!   stats = bootknife ({x,y}, 1999, @cor);
 %!   stats = bootknife ({x,y}, 1999, @cor, [], strata);
-%!   stats = bootknife ({y,x}, 1999, @(y,x) pinv(x)*y); % Could use @regress
-%!   stats = bootknife ({y,X}, 1999, @(y,X) pinv(X)*y);
-%!   stats = bootknife ({y,X}, 1999, @(y,X) pinv(X)*y, [], strata);
-%!   stats = bootknife ({y,X}, 1999, @(y,X) pinv(X)*y, [], strata, 2);
-%!   stats = bootknife ({y,X}, 1999, @(y,X) pinv(X)*y, [.05,.95], strata);
+%!   stats = bootknife ({x,y}, 1999, @mldivide);
+%!   stats = bootknife ({X,y}, 1999, @mldivide);
+%!   stats = bootknife ({X,y}, 1999, @mldivide, [], strata);
+%!   stats = bootknife ({X,y}, 1999, @mldivide, [], strata, 2);
+%!   stats = bootknife ({X,y}, 1999, @mldivide, [.05,.95], strata);
 %! catch
 %!   warning ('on', 'bootknife:parallel')
 %!   rethrow (lasterror)

diff --git a/test/test_script.m b/test/test_script.m
@@ -55,11 +55,11 @@
   y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
   stats = bootknife ({x,y}, 2000, @cor);
   stats = bootknife ({x,y}, 2000, @cor, [], strata);
-  stats = bootknife ({y,x}, 2000, @(y,x) pinv(x)*y); % Could also use @regress
-  stats = bootknife ({y,X}, 2000, @(y,X) pinv(X)*y);
-  stats = bootknife ({y,X}, 2000, @(y,X) pinv(X)*y, [], strata);
-  stats = bootknife ({y,X}, 2000, @(y,X) pinv(X)*y, [], strata, 2);
-  stats = bootknife ({y,X}, 2000, @(y,X) pinv(X)*y, [.05,.95], strata);
+  stats = bootknife ({x,y}, 2000, @mldivide);
+  stats = bootknife ({X,y}, 2000, @mldivide);
+  stats = bootknife ({X,y}, 2000, @mldivide, [], strata);
+  stats = bootknife ({X,y}, 2000, @mldivide, [], strata, 2);
+  stats = bootknife ({X,y}, 2000, @mldivide, [.05,.95], strata);
 
   % bootclust 
   % bootclust:test:1
@@ -101,12 +101,12 @@
   y = randn (20,1); x = randn (20,1); X = [ones(20,1), x];
   stats = bootclust ({x,y}, 1999, @cor);
   stats = bootclust ({x,y}, 1999, @cor, [], clustid);
-  stats = bootclust ({y,x}, 1999, @(y,x) pinv(x)*y); % Could use @regress
-  stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y);
-  stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y, [], clustid);
-  stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y, [], clustid, true);
-  stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y, [], clustid, true, 1);
-  stats = bootclust ({y,X}, 1999, @(y,X) pinv(X)*y, [.05,.95], clustid);
+  stats = bootclust ({x,y}, 1999, @mldivide);
+  stats = bootclust ({X,y}, 1999, @mldivide);
+  stats = bootclust ({X,y}, 1999, @mldivide, [], clustid);
+  stats = bootclust ({X,y}, 1999, @mldivide, [], clustid, true);
+  stats = bootclust ({X,y}, 1999, @mldivide, [], clustid, true, 1);
+  stats = bootclust ({X,y}, 1999, @mldivide, [.05,.95], clustid);
 
   % bootci
   % bootci:test:1
@@ -155,15 +155,15 @@
   y = randn (20,1); x = randn (20,1); X = [ones(20,1),x];
   bootci (1999, @cor, x, y);
   bootci (1999, {@cor, x, y}, 'strata', g);
-  bootci (1999, @(y,X) pinv(X)*y, y, X);
-  bootci (1999, @(y,X) pinv(X)*y, y, X, 'alpha', 0.1);
-  bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1);
-  bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'norm');
-  bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'per');
-  bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'basic');
-  bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'bca');
-  bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'stud');
-  bootci (1999, {@(y,X) pinv(X)*y, y, X}, 'alpha', 0.1, 'type', 'cal');
+  bootci (1999, @mldivide, X, y);
+  bootci (1999, @mldivide, X, y, 'alpha', 0.1);
+  bootci (1999, {@mldivide, X, y}, 'alpha', 0.1);
+  bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'norm');
+  bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'per');
+  bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'basic');
+  bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'bca');
+  bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'stud');
+  bootci (1999, {@mldivide, X, y}, 'alpha', 0.1, 'type', 'cal');
 
   % bootstrp
   y = randn (20,1);