Mercurial > hg > octave-nkf
annotate scripts/statistics/base/cov.m @ 12856:cad4cba03f19
Deprecate corrcoef, cor and replace with Matlab equivalent corr
The value calculated by Octave's corrcoef and cor are the
same as the value calculated by the Matlab function corr.
Use MathWorks naming convention for this functionality.
* corr.m: New file with functionality of corrcoef.m
* cov.m, kendall.m, spearman.m, cor_test.m: Adjust scripts to call corr()
* statistics/base/module.mk, deprecated/module.mk: Inform Automake about
deprecated functions
* NEWS: Inform users about deprecation
* stats.txi: Add corr() to documentation.
author | Rik <octave@nomad.inbox5.com> |
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date | Sat, 16 Jul 2011 20:38:00 -0700 |
parents | 6b2f14af2360 |
children | 72c96de7a403 |
rev | line source |
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11523 | 1 ## Copyright (C) 1995-2011 Kurt Hornik |
3426 | 2 ## |
3922 | 3 ## This file is part of Octave. |
4 ## | |
5 ## Octave is free software; you can redistribute it and/or modify it | |
6 ## under the terms of the GNU General Public License as published by | |
7016 | 7 ## the Free Software Foundation; either version 3 of the License, or (at |
8 ## your option) any later version. | |
3426 | 9 ## |
3922 | 10 ## Octave is distributed in the hope that it will be useful, but |
3200 | 11 ## WITHOUT ANY WARRANTY; without even the implied warranty of |
12 ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU | |
3426 | 13 ## General Public License for more details. |
14 ## | |
3200 | 15 ## You should have received a copy of the GNU General Public License |
7016 | 16 ## along with Octave; see the file COPYING. If not, see |
17 ## <http://www.gnu.org/licenses/>. | |
3200 | 18 |
3367 | 19 ## -*- texinfo -*- |
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20 ## @deftypefn {Function File} {} cov (@var{x}) |
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21 ## @deftypefnx {Function File} {} cov (@var{x}, @var{opt}) |
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22 ## @deftypefnx {Function File} {} cov (@var{x}, @var{y}) |
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23 ## @deftypefnx {Function File} {} cov (@var{x}, @var{y}, @var{opt}) |
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24 ## Compute the covariance matrix. |
6754 | 25 ## |
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26 ## If each row of @var{x} and @var{y} is an observation, and each column is |
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27 ## a variable, then the @w{(@var{i}, @var{j})-th} entry of |
3367 | 28 ## @code{cov (@var{x}, @var{y})} is the covariance between the @var{i}-th |
6754 | 29 ## variable in @var{x} and the @var{j}-th variable in @var{y}. |
30 ## @tex | |
31 ## $$ | |
32 ## \sigma_{ij} = {1 \over N-1} \sum_{i=1}^N (x_i - \bar{x})(y_i - \bar{y}) | |
33 ## $$ | |
34 ## where $\bar{x}$ and $\bar{y}$ are the mean values of $x$ and $y$. | |
35 ## @end tex | |
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36 ## @ifnottex |
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37 ## |
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38 ## @example |
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39 ## cov (x) = 1/N-1 * SUM_i (x(i) - mean(x)) * (y(i) - mean(y)) |
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40 ## @end example |
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41 ## |
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42 ## @end ifnottex |
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43 ## |
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44 ## If called with one argument, compute @code{cov (@var{x}, @var{x})}, the |
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45 ## covariance between the columns of @var{x}. |
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46 ## |
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47 ## The argument @var{opt} determines the type of normalization to use. |
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48 ## Valid values are |
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49 ## |
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50 ## @table @asis |
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51 ## @item 0: |
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52 ## normalize with @math{N-1}, provides the best unbiased estimator of the |
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53 ## covariance [default] |
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54 ## |
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55 ## @item 1: |
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56 ## normalize with @math{N}, this provides the second moment around the mean |
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57 ## @end table |
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58 ## @seealso{corr} |
3367 | 59 ## @end deftypefn |
3200 | 60 |
5428 | 61 ## Author: KH <Kurt.Hornik@wu-wien.ac.at> |
3456 | 62 ## Description: Compute covariances |
3200 | 63 |
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64 function c = cov (x, y = [], opt = 0) |
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65 |
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66 if (nargin < 1 || nargin > 3) |
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67 print_usage (); |
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68 endif |
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69 |
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70 if ( ! (isnumeric (x) || islogical (x)) |
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71 || ! (isnumeric (y) || islogical (y))) |
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72 error ("cov: X and Y must be numeric matrices or vectors"); |
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73 endif |
3200 | 74 |
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75 if (ndims (x) != 2 || ndims (y) != 2) |
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76 error ("cov: X and Y must be 2-D matrices or vectors"); |
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77 endif |
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78 |
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79 if (nargin == 2 && isscalar (y)) |
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80 opt = y; |
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81 endif |
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82 |
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83 if (opt != 0 && opt != 1) |
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84 error ("cov: normalization OPT must be 0 or 1"); |
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85 endif |
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86 |
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87 ## Special case, scalar has zero covariance |
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88 if (isscalar (x)) |
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89 if (isa (x, 'single')) |
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90 c = single (0); |
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91 else |
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92 c = 0; |
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93 endif |
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94 return; |
3200 | 95 endif |
96 | |
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97 if (isrow (x)) |
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98 x = x.'; |
3200 | 99 endif |
100 n = rows (x); | |
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101 |
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102 if (nargin == 1 || isscalar (y)) |
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103 x = center (x, 1); |
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104 c = conj (x' * x / (n - 1 + opt)); |
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105 else |
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106 if (isrow (y)) |
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107 y = y.'; |
3200 | 108 endif |
109 if (rows (y) != n) | |
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110 error ("cov: X and Y must have the same number of observations"); |
3200 | 111 endif |
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112 x = center (x, 1); |
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113 y = center (y, 1); |
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114 c = conj (x' * y / (n - 1 + opt)); |
3200 | 115 endif |
116 | |
117 endfunction | |
7411 | 118 |
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119 |
7411 | 120 %!test |
121 %! x = rand (10); | |
122 %! cx1 = cov (x); | |
123 %! cx2 = cov (x, x); | |
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124 %! assert(size (cx1) == [10, 10] && size (cx2) == [10, 10]); |
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125 %! assert(cx1, cx2, 1e1*eps); |
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126 |
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127 %!test |
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128 %! x = [1:3]'; |
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129 %! y = [3:-1:1]'; |
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130 %! assert (cov (x,y), -1, 5*eps) |
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131 %! assert (cov (x,flipud (y)), 1, 5*eps) |
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132 %! assert (cov ([x, y]), [1 -1; -1 1], 5*eps) |
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133 |
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134 %!test |
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135 %! x = single ([1:3]'); |
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136 %! y = single ([3:-1:1]'); |
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137 %! assert (cov (x,y), single (-1), 5*eps) |
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138 %! assert (cov (x,flipud (y)), single (1), 5*eps) |
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139 %! assert (cov ([x, y]), single ([1 -1; -1 1]), 5*eps) |
7411 | 140 |
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141 %!test |
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142 %! x = [1:5]; |
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143 %! c = cov (x); |
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144 %! assert (isscalar (c)); |
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145 %! assert (c, 2.5); |
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146 |
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147 %!assert(cov (5), 0); |
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148 %!assert(cov (single(5)), single(0)); |
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149 |
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150 %!test |
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151 %! x = [1:5]; |
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152 %! c = cov (x, 0); |
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153 %! assert(c, 2.5); |
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154 %! c = cov (x, 1); |
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155 %! assert(c, 2); |
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157 %% Test input validation |
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158 %!error cov (); |
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159 %!error cov (1, 2, 3, 4); |
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160 %!error cov ([1; 2], ["A", "B"]); |
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161 %!error cov (ones (2,2,2)); |
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162 %!error cov (ones (2,2), ones (2,2,2)); |
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163 %!error cov (1, 3); |
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164 %!error cov (ones (2,2), ones (3,2)); |
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165 |