Mercurial > octave
view scripts/statistics/iqr.m @ 30997:5330efaf9476
Add optional second output to var and std (bug #62395)
* scripts/statistics/var.m: Add optional second output containing the mean
used to calculate the variance. Move weight isempty check ahead of vector
dimension isscalar check to avoid triggering incompatability error. Add BISTs
testing second output with different calling options. Add BIST testing empty
value passed as variance weight treated as zero. Add new output behavior to
docstring, and update function definitions to show the primary variable.
* scripts/statistics/std.m: Add passthrough for second output from var when
std called with two outputs. Add BISTs testing second output with different
calling options. Update docstring noting new output behavior.
* etc/NEWS.8.md: Note output changes to var and std under Matlab Compatability.
author | Nicholas R. Jankowski <jankowski.nicholas@gmail.com> |
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date | Thu, 12 May 2022 13:10:52 -0400 |
parents | 796f54d4ddbf |
children | a40c0b7aa376 |
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######################################################################## ## ## Copyright (C) 1995-2022 The Octave Project Developers ## ## See the file COPYRIGHT.md in the top-level directory of this ## distribution or <https://octave.org/copyright/>. ## ## This file is part of Octave. ## ## Octave is free software: you can redistribute it and/or modify it ## under the terms of the GNU General Public License as published by ## the Free Software Foundation, either version 3 of the License, or ## (at your option) any later version. ## ## Octave is distributed in the hope that it will be useful, but ## WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## You should have received a copy of the GNU General Public License ## along with Octave; see the file COPYING. If not, see ## <https://www.gnu.org/licenses/>. ## ######################################################################## ## -*- texinfo -*- ## @deftypefn {} {@var{Z} =} iqr (@var{x}) ## @deftypefnx {} {@var{Z} =} iqr (@var{x}, @var{dim}) ## @deftypefnx {} {@var{Z} =} iqr (@var{x}, @qcode{"ALL"}) ## Return the interquartile range of @var{x}, defined as the distance between ## the 25th and 75th percentile values of @var{x} calculated using: ## quantile (x, [0.25 0.75]) ## ## If @var{x} is a vector, @code{iqr (@var{x})} will operate on the data in ## @var{x}. ## ## If @var{x} is a matrix, @code{iqr (@var{x})} will operate independently on ## each column in @var{x} returning a row vector @var{Z}. ## ## If @var{x} is a n-dimensional array, @code{iqr (@var{x})} will operate ## independently on the first non-singleton dimension in @var{x}, returning an ## array @var{Z} the same shape as @var{x} with the non-singleton dimenion ## reduced to 1. ## ## The optional variable @var{dim} can be used to force @code{iqr} to operate ## over the specified dimension. @var{dim} can either be a scalar dimension or ## a vector of non-repeating dimensions over which to operate. In either case ## @var{dim} must be positive integers. A vector @var{dim} concatenates all ## specified dimensions for independent operation by @code{iqr}. ## ## Specifying dimension @qcode{"ALL"} will force @code{iqr} to operate ## on all elements of @var{x}, and is equivalent to @code{iqr (@var{x}(:))}. ## Similarly, specifying a vector dimension including all non-singleton ## dimensions of @var{x} is equivalent to @code{iqr (@var{x}, @qcode{"ALL"})}. ## ## If @var{x} is a scalar, or only singleton dimensions are specified for ## @var{dim}, the output will be @code{zeros (size (@var{x}))}. ## ## As a measure of dispersion, the interquartile range is less affected by ## outliers than either @code{range} or @code{std}. ## ## @seealso{bounds, mad, range, std, prctile, quantile} ## @end deftypefn ## TODO: When Probability Distribution Objects are implemented, enable ## handling for those object types. function z = iqr (x, dim) ## input checks if (nargin < 1) print_usage (); elseif (nargin < 2) dim = []; endif if (! (isnumeric (x) || islogical (x))) error ("iqr: X must be a numeric vector or matrix"); endif vecdim_flag = false; nd = ndims (x); sz = size (x); if (isempty (dim)) ## Find first non-singleton dimension. if (max (sz) == 1) dim = 2; else dim = find ((sz > 1), 1); endif else if (isvector (dim) && isnumeric (dim) && all (dim > 0) && all (rem (dim, 1) == 0)) if (((num_vecdims = numel (dim)) > 1) && all (diff (sort (dim)))) ## DIM must be 1D and non repeating. ## Detect trivial case of DIM being all dimensions (same as "all"). highest_dim = (max (nd, max (dim))); if ((num_vecdims == nd) && (highest_dim == nd)) x = x(:); sz = size (x); dim = 1; else ## Move dimensions for operation to the front, keeping the order of ## the remaining dimensions. ## Reshape those into a single dimension. ## Process as normal for a dim1 iqr on X, reshape when done. vecdim_flag = true; ## flag for final reshape if (iscolumn (dim)) dim = dim.'; endif ## Permutation vector with DIM at front perm = [1:highest_dim]; perm(dim) = []; perm = [dim, perm]; ## Reshape X to put dims to process at front. x = permute (x, perm); sz_x_new = size (x); ## Preserve trailing singletons when dim > ndims (x). sz_x_new = [sz_x_new, ones(1, highest_dim - numel (sz_x_new))]; newshape = [prod(sz_x_new(1:num_vecdims)), ... ones(1, (num_vecdims-1)), ... sz_x_new((num_vecdims+1):end)]; if (numel (newshape) == 1) newshape = [newshape, 1]; endif ## Collapse dimensions to be processses into single column. x = reshape (x, newshape); ## Operate column-wise. dim = 1; endif elseif (! isscalar (dim)) error ("iqr: vector DIM must contain non-repeating positive integers"); endif elseif (strcmp (tolower (dim), "all")) ## "ALL" simplifies to collapsing all elements to single vector x = x(:); dim = 1; sz = size (x); else error ("iqr: DIM must be a positive integer scalar, vector, or 'all'"); endif endif if (((dim > nd) || (sz(dim) == 1)) && all (isfinite (x))) ## shortcut easy zeros z = zeros (sz); elseif (iscolumn (x) && (dim == 1)) ## detect col vector with quantile/diff dim requirement mismatch z = abs (diff (quantile (x, [0.25, 0.75], 1), [], 2)); else z = abs (diff (quantile (x, [0.25, 0.75], dim), [], dim)); endif if (vecdim_flag) z = ipermute (z, perm); endif endfunction %!assert (iqr (17), 0) %!assert (iqr (17, 1), 0) %!assert (iqr (17, 4), 0) %!assert (iqr (1:3), 1.5) %!assert (iqr (1:4), 2) %!assert (iqr (1:5), 2.5) %!assert (iqr (1:10), 5) %!assert (iqr ((1:10).'), 5) %!assert (iqr (1:10, 2), 5) %!assert (iqr (1:10, 1), zeros (1, 10)) %!assert (iqr (1:10, 3), zeros (1, 10)) %!assert (iqr ([1:5; 2:6], "all"), 3) %!test %! x = reshape (1:6, [1 2 3]); %! assert (iqr (x), ones (1, 1, 3)); %! assert (iqr (x, 1), zeros (1, 2, 3)); %! assert (iqr (x, 2), ones (1, 1, 3)); %! assert (iqr (x, 3), [3 3]); ## n-D arrays %!test %! x = magic (4); x = cat (3,x, 2*x, 3*x); x = cat (4, x, 2*x); %! y = cat (3, 8*[1 1 1 1], 16*[1 1 1 1], 24*[1 1 1 1]); %! assert (iqr (x), cat (4, y, 2*y)); %! assert (iqr (x, 1), cat (4, y, 2*y)); %! y = cat (3, 4*[3 1 1 3].', 8*[3 1 1 3].', 12*[3 1 1 3].'); %! assert (iqr (x, 2), cat (4, y, 2*y)); %! y = [24 3 4.5 19.5; 7.5 16.5 15 12; 13.5 10.5 9, 18; 6 21 22.5 1.5]; %! assert (iqr (x, 3), cat (4, y, 2*y)); %! y = [16 2 3 13; 5 11 10 8; 9 7 6 12; 4 14 15 1]; %! assert (iqr (x, 4), cat (3, y, 2*y, 3*y)); %! assert (iqr (x, 5), zeros (size (x))); ## vector dimensions %!assert (iqr (17, [1 8]), 0) %!assert (iqr ([[1 2 5]; [2 5 6]], [1 2]), 3) %!assert (iqr (cat (3, [1 2 5; 2 5 6], [1 2 5; 2 5 6]), [1 2]), cat(3, 3, 3)) %!assert (iqr (cat (3, [1 2 5; 2 5 6], [1 2 5; 2 5 6]), [1 2]'), cat(3, 3, 3)) %!test %! x = magic (4); x = cat (3, x, 2*x, 3*x); x = cat (4, x, 2*x); %! y = cat (3, 8, 16, 24); %! assert (iqr (x, [1 2]), cat (4, y, 2*y)); %! y = [14, 18.5, 17.5 19.5]; %! assert (iqr (x, [1 3]), cat (4, y, 2*y)); %! y = [10.5 12.5 11.5 15.0000]; %! assert (iqr (x, [1 4]), cat (3, y, 2*y, 3*y)); %! assert (iqr (x, [1 5]), iqr (x, 1)); %! y = [24 13 12 25.5]'; %! assert (iqr (x, [2 3]), cat (4, y, 2*y)); %! y = [17.5, 9, 8, 18.5]'; %! assert (iqr (x, [2 4]), cat (3, y, 2*y, 3*y)); %! assert (iqr (x, [3 4]), [32 4 6 26; 10 22 20 16; 18 14 12 24; 8 28 30 2]); %! assert (iqr (x, [3 4]), iqr (x, [4 3])); %! assert (iqr (x, [1 2 3]), cat (4, 17.5, 35)); %! assert (iqr (x, [2 3 4]), [29.5 19.5 23 31]'); %! assert (iqr (x, [1 3 4]), [22 28 22 30.5]); %! assert (iqr (x, [1 2 4]), cat (3, 11, 22, 33)); %! assert (iqr (x, [1 2 5]), iqr (x, [1 2])); %! assert (iqr (x, [5 6]), zeros (size (x))); ## Inf, NaN %!assert (iqr (Inf), NaN) %!assert (iqr (-Inf), NaN) %!assert (iqr (NaN), NaN) %!assert (iqr (NaN), NaN) %!assert (iqr ([1 2 Inf], 1), [0 0 NaN]) %!assert (iqr ([1 2 Inf], 2), Inf) %!assert (iqr ([1 2 -Inf], 1), [0 0 NaN]) %!assert (iqr ([1 2 -Inf], 2), Inf) %!assert (iqr ([1 2 3 NaN], 1), [0 0 0 NaN]) %!assert (iqr ([1 2 3 NaN], 2), 1.5) %!assert (iqr ([1 NaN 2 3], 2), 1.5) %!assert (iqr (NaN (2), 1), [NaN, NaN]) %!assert (iqr (NaN (2), 2), [NaN; NaN]) %!assert (iqr (NaN (2), 3), NaN (2)) %!assert (iqr ([[1 2 5], [2 NaN 6]], "all"), 3.5) ## input validation %!error iqr () %!error iqr (1, 2, 3) %!error <X .* numeric> iqr (['A'; 'B']) %!error <DIM .* positive integer> iqr (1, 'A') %!error <DIM .* positive integer> iqr (1, 0) %!error <DIM .* positive integer> iqr (1, -2) %!error <DIM .* positive integer> iqr (1, 1.4) %!error <DIM .* positive integer> iqr (1, [1 -2]) %!error <DIM .* positive integer> iqr (1, [1 1.4]) %!error <DIM .* positive integer> iqr ([1 2 3], NaN) %!error <DIM .* positive integer> iqr ([1 2 3], [2 NaN]) %!error <DIM .* positive integer> iqr ([1 2 3], Inf) %!error <DIM .* positive integer> iqr ([1 2 3], [2 Inf]) %!error <vector DIM .* non-repeating> iqr ([1 2 3], [1 2 1]) %!error <DIM .* vector> iqr (1, [1 2; 3 4])