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If not, see ## . ## ######################################################################## ## -*- texinfo -*- ## @deftypefn {} {@var{R} =} rmmissing (@var{A}) ## @deftypefnx {} {@var{R} =} rmmissing (@var{A}, @var{dim}) ## @deftypefnx {} {@var{R} =} rmmissing (@dots{}, @var{Name}, @var{Value}) ## @deftypefnx {} {[@var{R} @var{TF}] =} rmmissing (@dots{}) ## ## Remove missing or incomplete data from an array. ## ## Given an input vector or matrix (2-D array) @var{A}, remove missing data ## from a vector or missing rows or columns from a matrix. @var{A} ## can be a numeric array, char array, or an array of cell strings. ## @var{R} returns the array after removal of missing data. ## ## The values which represent missing data depend on the data type of @var{A}: ## ## @itemize ## @item ## @code{NaN}: @code{single}, @code{double}. ## ## @item ## @code{' '} (white space): @code{char}. ## ## @item ## @code{@{''@}}: string cells. ## @end itemize ## ## Choose to remove rows (default) or columns by setting optional input ## @var{dim}: ## ## @itemize ## @item ## @code{1}: rows. ## ## @item ## @code{2}: columns. ## @end itemize ## ## Note: data types with no default 'missing' value will always result in ## @code{R == A} and a TF output of @code{false(size(@var{A}))}. ## ## Additional optional parameters are set by @var{Name}-@var{Value} pairs. ## These are: ## ## @itemize ## @item ## @code{MinNumMissing}: minimum number of missing values to remove an entry, ## row or column, defined as a positive integer number. E.g.: if ## @code{MinNumMissing} is set to @code{2}, remove the row of a numeric matrix ## only if it includes 2 or more NaN. ## @end itemize ## ## Optional return value @var{TF} is a logical array where @code{true} values ## represent removed entries, rows or columns from the original data @var{A}. ## ## @end deftypefn ## ## @seealso{ismissing, standardizeMissing} function [R, TF] = rmmissing (A, varargin) if ((nargin < 1) || (nargin > 4)) print_usage (); endif if ndims(A) > 2 error ("rmmissing: input dimension cannot exceed 2"); endif optDimensionI = 2; # default dimension: rows optMinNumMissingI = 1; ## parse options if (nargin > 1) if (isnumeric (varargin{1})) ## option "dim" switch (varargin{1}) case 1 optDimensionI = 2; case 2 optDimensionI = 1; otherwise error ("rmmissing: 'dim' must be either 1 or 2"); endswitch pair_index = 2; else [r, c] = size (A); ## first non singleton dimension, but only two dimensions considered if (r == 1 && c != 1) optDimensionI = 1; endif pair_index = 1; endif ## parse name-value parameters while (pair_index <= (nargin - 1)) switch (lower (varargin{pair_index})) ## minimum number of missing values to remove entries; ## it must be a positive integer number case "minnummissing" if (! isnumeric (varargin{pair_index + 1}) || ! isscalar (varargin{pair_index + 1}) || floor (varargin{pair_index + 1}) != varargin{pair_index + 1} || varargin{pair_index + 1} < 1) error (["rmmissing: 'MinNumMissing' requires a positive integer"... " number as value"]); endif optMinNumMissingI = varargin{pair_index + 1}; otherwise error ("rmmissing: unknown parameter name '%s'", ... varargin{pair_index}); endswitch pair_index += 2; endwhile endif ## main logic TF = ismissing (A); if (isvector (A)) R = A(TF == 0); elseif (iscellstr(A) || ismatrix (A)) ## matrix: ismissing returns an array, so it must be converted to a row or ## column vector according to the "dim" of choice if (optMinNumMissingI > 1) TF = sum (TF, optDimensionI); TF(TF < optMinNumMissingI) = 0; TF = logical (TF); else TF = any (TF, optDimensionI); endif if (optDimensionI == 2) ## remove the rows R = A((TF == 0), :); else ## remove the columns R = A(:, (TF == 0)); endif else error ("rmmissing: unsupported data"); endif endfunction %!assert (rmmissing ([1,NaN,3]), [1,3]) %!assert (rmmissing ('abcd f'), 'abcdf') %!assert (rmmissing ({'xxx','','xyz'}), {'xxx','xyz'}) %!assert (rmmissing ({'xxx','';'xyz','yyy'}), {'xyz','yyy'}) %!assert (rmmissing ({'xxx','';'xyz','yyy'}, 2), {'xxx';'xyz'}) %!assert (rmmissing ([1,2;NaN,2]), [1,2]) %!assert (rmmissing ([1,2;NaN,2], 2), [2,2]') %!assert (rmmissing ([1,2;NaN,4;NaN,NaN],"MinNumMissing", 2), [1,2;NaN,4]) ## Test second output %!test %! x = [1:6]; %! x([2,4]) = NaN; %! [~, idx] = rmmissing (x); %! assert (idx, logical ([0, 1, 0, 1, 0, 0])); %! assert (class(idx), 'logical'); %! x = reshape (x, [2, 3]); %! [~, idx] = rmmissing (x); %! assert (idx, logical ([0; 1])); %! assert (class(idx), 'logical'); %! [~, idx] = rmmissing (x, 2); %! assert (idx, logical ([1, 1, 0])); %! assert (class(idx), 'logical'); %! [~, idx] = rmmissing (x, 1, "MinNumMissing", 2); %! assert (idx, logical ([0; 1])); %! assert (class(idx), 'logical'); %! [~, idx] = rmmissing (x, 2, "MinNumMissing", 2); %! assert (idx, logical ([0, 0, 0])); %! assert (class(idx), 'logical'); ## Test data type handling %!assert (rmmissing (single ([1 2 NaN; 3 4 5])), single ([3 4 5])) %!assert (rmmissing (logical (ones (3))), logical (ones (3))) %!assert (rmmissing (int32 (ones (3))), int32 (ones (3))) %!assert (rmmissing (uint32 (ones (3))), uint32 (ones (3))) %!assert (rmmissing ({1, 2, 3}), {1, 2, 3}) %!assert (rmmissing ([struct, struct, struct]), [struct, struct, struct]) ## Test empty input handling %!assert (rmmissing ([]), []) %!assert (rmmissing (ones (1,0)), ones (1,0)) %!assert (rmmissing (ones (1,0), 1), ones (1,0)) %!assert (rmmissing (ones (1,0), 2), ones (1,0)) %!assert (rmmissing (ones (0,1)), ones (0,1)) %!assert (rmmissing (ones (0,1), 1), ones (0,1)) %!assert (rmmissing (ones (0,1), 2), ones (0,1)) %!error rmmissing (ones (0,1,2)) ## Test input validation %!error rmmissing () %!error rmmissing (ones(2,2,2)) %!error rmmissing ([1 2; 3 4], 5) %!error rmmissing ([1 2; 3 4], "XXX", 1) %!error <'MinNumMissing'> rmmissing ([1 2; 3 4], 2, "MinNumMissing", -2) %!error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 3.8) %!error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", [1 2 3]) %!error <'MinNumMissing'> rmmissing ([1 2; 3 4], "MinNumMissing", 'xxx')