| [ptsa, nptsa, rt_stats, other_stats, rt_outlier_stats, other_outlier_stats]=data_outliers3(flag1, rta, dep_var, round_kind, round_digits, abs_rta, abs_other, sod, outfile, save_file, row_names, row_unit, col_name, array_names, array_units, varargin)
|
function [ptsa, nptsa, rt_stats, other_stats, rt_outlier_stats, other_outlier_stats]=data_outliers3(flag1, rta, dep_var, round_kind, round_digits, abs_rta, abs_other, sod, outfile, save_file, row_names, row_unit, col_name, array_names, array_units, varargin)
% % data_outliers: Determine the outliers and return descriptive statistics
% %
% % Syntax:
% %
% % [ptsa, nptsa, rt_stats, other_stats, rt_outlier_stats, ...
% % other_outlier_stats]=data_outliers3(rta, dep_var, round_kind, ...
% % round_digits, abs_rta, abs_other, sod, outfile, save_file, ...
% % row_names, row_unit, col_name, array_names, array_units, varargin);
% %
% % ********************************************************************
% %
% % Description
% %
% % Program finds outliers based on the first input variable data array rta.
% %
% % Descriptive statistics of the data excluding the outliers are
% % calculated for the rta array and the varargin data arrays.
% %
% % Descriptive statistics of the outliers fo teh data based on the rta
% % array are calculated for the rta array and the varargin data arrays.
% %
% % The output descriptive statistics are namely:
% % 1) Arithmetic Mean
% % 2) Robust Mean
% % 3) Standard Deviation
% % 4) 95% confidence interval, two sided, t-distribution
% % 5) Median index
% % 6) Median
% % 7) Minimum
% % 8) Maximum
% %
% %
% % Data_outliers uses the robust mean for finding the outliers.
% % All data with excessive residuals can be detected as outliers.
% %
% % There are several input and output variables which are described in
% % more detail in the sections below respectively.
% %
% % ********************************************************************
% %
% % Input Variables
% %
% % flag1=0; % Boolean which selects Linear or Exponential
% % % scaling before calculating mean and median.
% %
% % rta=abs(randn(28,8)); % Data array to find outliers and primary
% % % data set to analyze with descriptive statistics
% % % default is abs_rta=0;
% %
% % dep_var=1; % Dependence of the varargin arrays on the rta
% % % arrays. Controls how outliers are chosen.
% % %
% % % 1 outliers are calculated only using the
% % % rta array then applied to all varargin arrays.
% % %
% % % 0 outliers are calculated for the rta array
% % % and independently for all varargin arrays.
% %
% % round_kind=1; % Array of values one element for the rta array
% % % and one element for each varargin array
% % % (see example)
% % % 1 round to specified number of significant
% % % digits
% % %
% % % 0 round to specified digits place
% % %
% % % default is round_kind=1;
% %
% % round_digits=3; % Array of values one element for the rta array
% % % and one element for each varargin array
% % % (see example)% Type of rounding depends on round_kind
% % %
% % % if round_kind==1 number of significant digits
% % % if round_kind==0 specified digits place
% % %
% % % default is round_digits=3;
% %
% % abs_rta=1; % 1 use the absolute value of the rta data to
% % % find the outliers and anlayze the descriptive statistics
% % % 0 or [] do not use absolute value
% % % default is abs_rta=0;
% %
% % abs_other=[]; % Logical array of values one element for the
% % % rta array and one element for each varargin array
% % % (see example)
% % % 1 for an element in the varargin will
% % % use the absolute value of the rta data to
% % % anlayze the descriptive statistics
% % % 0 or [] does not use absolute value
% % %
% % % default is abs_other=zeros(1+length(varargin), 1);
% %
% % sod=0; % 1 surpress outlier detection
% % % 0 find the outliers and remove them from the
% % % statistical analysis
% % % default is sod=1;
% %
% %
% % outfile='stat_of_data'; % string filename for saving analyzed data
% % % '.txt' extension is added if not present
% % %
% % % fid is another option if the outfile is
% % % already an existing file data will be written
% % % at the current position of file
% % % default is outfile='outliers_stats';
% %
% % save_file=1; % 1 to save data
% % % 0 for not saving data
% % % default is save_file=1;
% %
% % row_names=[ 20, 25, 31.5, 40, 50, 63, 80, 100, 125, 160, ...
% % 200, 250, 315, 400, 500, 630, 800, 1000, 1250, 1600, ...
% % 2000, 2500, 3150, 4000, 5000, 6300, 8000, 10000];
% % % Array of values one element for each row of
% % % data in the rta array.
% % % (see example)
% % % can be numbers or a cell array of strings
% % % if empty default is numbers
% % % from 1 to num_rows
% % %
% % % first element is the name of the first row
% % % in the rta array the next elements are the
% % % names of the subsequent row in the rta array.
% % % These same names are used to name the
% % % rows in the varargin arrays.
% % %
% % % default is row_names='';
% %
% % row_unit='Hz'; % Cell array of strings, Cell Arary of Numbers
% % % array of numbers, or single string
% % % One element for each row of
% % % data in the rta array. (see example)
% % % Can also be a single string
% % % one string all rows are assumed
% % % to have the same units
% %
% % % first element is the units of the rta array
% % % the next elements are the units in the
% % % varargin arrays.
% %
% % % default is row_unit='';
% %
% % col_name='Peak Number'; % Single string (see example)
% % % String describing a distinguishing characteristic
% % % of one column from the next column.
% % % could be someting like 'Peak Number'.
% % %
% % % default is col_name='';
% %
% % array_names={'Reverberation Times', 'Signal Levels', 'Background Levels'};
% % % Cell array of strings, Cell array of numbers
% % % array of numbers, or single string
% % % One element for the rta array and one element
% % % for each array in varargin (see example)
% % % Can also be a single string
% % % If one string, then all arrays are assumed
% % % to have the same name.
% % %
% % % first element is the units of the rta array
% % % the next elements are the units in the
% % % varargin arrays.
% % % default is array_names='';
% %
% % array_units={'s', 'dBA', 'dBA'};
% % % Cell array of strings, Cell array of numbers
% % % array of numbers, or single string
% % % One element for the rta array and one element
% % % for each array in varargin (see example)
% % % Can also be a single string
% % % If one string, then all arrays are assumed
% % % to have the same units.
% % %
% % % first element is the units of the rta array
% % % the next elements are the units in the
% % % varargin arrays.
% % %
% % % default is array_units='';
% %
% % varargin % List of arrays of associated with rta array
% % % to statistically describe and analyze.
% % %
% % % The varargin data arrays must have the same
% % % size as the rta array.
% % %
% % % varargin consists of a comma delimited
% % % list of arrays
% % %
% % % array1, array2, ..., arrayn
% % %
% % % array1 ... arrayn are data arrays that need
% % % statistical analysis using the same outliers
% % % as the rta
% % %
% % % units_other_data is a cell array of units for
% % % each of the data arrays. Each of the arrays
% % % has its own, so if there were
% % % arrays1 ... array3 then the
% % % units_other_data={'dB', 'Pascals', 's'};
% % % would be possible units.
% % % There is no default value for varargin.
% %
% % ********************************************************************
% %
% % Output Variables
% %
% % ptsa is the cell array of indices of the data points within the
% % specified number of standard deviations
% %
% % nptsa is the cell array of indices of the data points outside the
% % specified number of standard deviations
% %
% % rt_stats is a cell array of descriptive statistics for the input variable rta
% %
% % other_stats is a cell array of descriptive statistics for the
% % arbitrary input variables varargin
% %
% % rt_outlier_stats is a cell array of descriptive statistics for the
% % outliers from the rta input variable.
% %
% % other_outlier_stats is a cell array of descriptive statistics for the
% % outliers from the arbitrary input variables varargin.
% %
% % ********************************************************************
%
% Example='1';
%
% % Shows an array of random numbers.
%
%
% flag1=0; % Boolean which selects Linear or Exponential
% % scaling before calculating mean and median.
%
% rta=abs(randn(28,8)); % Data array to find outliers and primary
% % data set to analyze with descriptive statistics
% % default is rta=0;
%
% dep_var=1; % Dependence of the varargin arrays on the rta
% % arrays. Controls how outliers are chosen.
% %
% % 1 outliers are calculated only using the
% % rta array then applied to all varargin arrays.
% %
% % 0 outliers are calculated for the rta array
% % and independently for all varargin arrays.
%
% round_kind=[1 0 0]; % 1 round to specified number of significant
% % digits.
% %
% % 0 round to specifid digits place
%
% round_digits=[3 0 0]; % Type of rounding depends on round_kind
% %
% % if round_kind==1 number of significant digits
% % if round_kind==0 spcecified digits place
% % 3 round to 3 significant digits
% % 0 round to the ones place
%
% abs_rta=1; % Use the absolute value of the rta data to
% % find the outliers and anlayze the descriptive statistics
%
% abs_other=[]; % logical array
% % Do not use the absolute value of the rta
% % data to anlayze the descriptive statistics
%
% sod=0; % Allow removal of outliers
%
%
% outfile='stat_of_data'; % filename for saving analyzed data is
% % 'stat_of_data.txt'
%
% save_file=1; % Save data to a text file
%
% row_names=[ 20, 25, 31.5, 40, 50, 63, 80, 100, 125, 160, ...
% 200, 250, 315, 400, 500, 630, 800, 1000, 1250, 1600, ...
% 2000, 2500, 3150, 4000, 5000, 6300, 8000, 10000];
% % Row names can be a constant,
% % arrays of numbers
% % cell arrays of numbers
% % or cell arrays of strings
%
% row_unit='Hz'; % one string all rows are assumed
% % to have the same units
%
% col_name='Microphone Number';
% % string distinguishing one column from
% % the next column
%
% array_names={'Reverberation Times', 'Signal Levels', 'Background Levels'};
% % cell array of strings
% % One string for the rta array and one sring
% % for each array in varargin
%
% array_units={'s', 'dBA', 'dBA'};
% % cell array of strings
% % specifies the units for the rta array and
% % each data array in varargin
% % 1zt string is the units of rta
% % the next n strings are the units of varargin
% %
% % For reverberation times
% % rta is the reverberations times with units of
% % seconds
% %
% % varargin contains two arrays the siganl
% % levels dBA and the background levels in dBA
%
% sigl=100+randn(28,8); % signal levels dBA
% bgl=10+randn(28,8); % background levels dBA
%
% % varargin % List of arrays of associated with rta array
% % % to statistically describe and analyze.
% % %
% % % The varargin data arrays must have the same
% % % size as the rta array.
% % %
% % % varargin consists of a comma delimited
% % % list of arrays
% % %
% % % array1, array2, ..., arrayn
% % %
% % % array1 ... arrayn are data arrays that need
% % % statistical analysis using the same outliers
% % % as the rta
% % %
% % % units_other_data is a cell array of units for
% % % each of the data arrays. Each of the arrays
% % % has its own, so if there were
% % % arrays1 ... array3 then the
% % % units_other_data={'dB', 'Pascals', 's'};
% % % would be possible units.
% %
% %
% % Run the example with the code below.
% [ptsa, nptsa, rt_stats, other_stats, rt_outlier_stats, other_outlier_stats]=data_outliers3(flag1, rta, dep_var, round_kind, round_digits, abs_rta, abs_other, sod, outfile, save_file, row_names, row_unit, col_name, array_names, array_units, sigl, bgl);
%
%
% % In general, the calling arguments are
% % [ptsa, nptsa, rt_stats, other_stats, rt_outlier_stats, other_outlier_stats]=data_outliers3(flag1, rta, dep_var, round_kind, round_digits, abs_rta, abs_other, sod, outfile, save_file, row_names, row_unit, col_name, array_names, array_units, varargin);
%
%
% Example='2';
%
% % Using only 2 columns eliminates most of the descriptive ststistics.
% % Use above code then run the following
% flag1=1; % convert data to Linear units proportional to Pascals
% % before calculating the mean and median
% rta=abs(randn(28,2)); % Data array to find outliers and primary
% sigl=100+randn(28,2); % signal levels dBA
% bgl=10+randn(28,2); % background levels dBA
%
% [ptsa, nptsa, rt_stats, other_stats, rt_outlier_stats, other_outlier_stats]=data_outliers3(flag1, rta, dep_var, round_kind, round_digits, abs_rta, abs_other, sod, outfile, save_file, row_names, row_unit, col_name, array_names, array_units, sigl, bgl);
%
%
%
%
% Example='3';
%
% % Using sod=1. eliminates removing outliers.
% % Use above code then run the following.
%
% flag1=1;
% sod=1;
% rta=abs(randn(28,8)); % Data array to find outliers and primary
% sigl=100+randn(28,8); % signal levels dBA
% bgl=10+randn(28,8); % background levels dBA
%
% [ptsa, nptsa, rt_stats, other_stats, rt_outlier_stats, other_outlier_stats]=data_outliers3(flag1,rta, dep_var, round_kind, round_digits, abs_rta, abs_other, sod, outfile, save_file, row_names, row_unit, col_name, array_names, array_units, sigl, bgl);
%
%
%
% % ********************************************************************
% %
% %
% % Reference:
% % Rousseeuw PJ, Leroy AM (1987): Robust regression and outlier detection.
% % Wiley.
% %
% %
% % Alexandros Leontitsis
% % Institute of Mathematics and Statistics
% % University of Kent at Canterbury
% % Canterbury
% % Kent, CT2 7NF
% % U.K.
% %
% % University e-mail: al10@ukc.ac.uk (until December 2002)
% % Lifetime e-mail: leoaleq@yahoo.com
% % Homepage: http://www.geocities.com/CapeCanaveral/Lab/1421
% %
% %
% % ********************************************************************
% %
% %
% % Subprograms
% %
% %
% % List of Dependent Subprograms for
% % data_outliers3
% %
% % FEX ID# is the File ID on the Matlab Central File Exchange
% %
% %
% % Program Name Author FEX ID#
% % 1) fastlts Peter J. Rousseeuw NA
% % 2) fastmcd Peter J. Rousseeuw NA
% % 3) file_extension Edward L. Zechmann
% % 4) genHyper Ben Barrowes 6218
% % 5) m_round Edward L. Zechmann
% % 6) pow10_round Edward L. Zechmann
% % 7) rmean Edward L. Zechmann
% % 8) sd_round Edward L. Zechmann
% % 9) t_alpha Edward L. Zechmann
% % 10) t_confidence_interval Edward L. Zechmann
% % 11) t_icpbf Edward L. Zechmann
% %
% %
% % ********************************************************************
% %
% %
% % data_outliers is written by Edward L. Zechmann
% %
% % created 16 December 2007
% %
% % modified 20 December 2007 Added comments.
% % Added col_name field.
% % Improved ability to input data sets
% % of cell arrays.
% % Improved formatiting of output
% % text file.
% %
% % modified 6 January 2007 Added features abs_rta, abs_other, sod
% % and updated comments.
% %
% % modified 26 February 2007 Updated the row units and the array
% % units.
% % Replaced geomean with LMSloc robust
% % mean.
% % Modified code to print actual values
% % including negative numbers, when
% % the absolute value is used to process
% % the descriptive statistics (mean, std,...).
% %
% % modified 19 September 2008 Updated Comments
% %
% % modified 16 December 2008 Added Arithmetic Mean.
% %
% % modified 9 January 2009 Added Default values.
% % Added rounding the output.
% % Added Analysis of Outliers.
% %
% % modified 10 January 2009 Finished updates.
% %
% % modified 11 January 2009 Updated Comments
% %
% % modified 21 March 2009 Added a flag to data_outliers3 to
% % convert dB data to Pa before
% % calculating mean and median values.
% % Updated comments
% %
% % modified 6 October 2009 Updated comments
% %
% % modified 6 February 2011 Changed method of identifying outliers
% % to FASTLTS Updated comments
% %
% % modified 21 February 2011 Fixed a bugs in trasnposing the input
% % to fstlts into a column vector.
% % Updated comments.
% %
% % modified 5 Janaury 2012 Replace LMSloc with fastlts.
% % Updated comments
% %
% %
% %
% %
% % ********************************************************************
% %
% % Please feel free to modify this code.
% %
% % See Also: LMSloc, mean, std, t_confidence_interval
% %
num_vars=length(varargin);
% initialize flag1 if empty
if nargin < 1 || isempty(flag1) || ~isnumeric(flag1)
flag1=zeros(1+num_vars, 1);
elseif length(flag1) < 1+num_vars
flag2=zeros( 1+num_vars, 1);
flag2(1:length(flag1),1)=flag1;
flag1=flag2;
end
% initialize abs_rta if empty
if nargin < 2 || isempty(abs_rta)
abs_rta=0;
end
if nargin < 3 || isempty(dep_var) || ~isnumeric(dep_var)
dep_var=1;
end
if ~isequal(dep_var, 1)
dep_var=0;
end
if nargin < 4 || isempty(round_kind) || ~isnumeric(round_kind)
round_kind=ones(1+num_vars, 1);
end
if length(round_kind) < (num_vars+1)
for e1=(length(round_kind)+1):(num_vars+1);
round_kind(e1)=0;
end
end
if nargin < 5 || isempty(round_digits) || ~isnumeric(round_digits)
round_digits=ones(1+num_vars, 1);
end
if length(round_digits) < (num_vars+1)
for e1=(length(round_digits)+1):(num_vars+1);
if isequal(round_kind(e1), 1)
round_digits(e1)=3;
else
round_digits(e1)=0;
end
end
end
if nargin < 6 || isempty(abs_rta) || ~isnumeric(abs_rta)
abs_rta=0;
end
if nargin < 7 || isempty(abs_other) || ~isnumeric(abs_other)
abs_other=zeros(1+num_vars, 1);
end
% initialize abs_rta if shorter than varargin
if length(abs_other) < num_vars
for e1=(length(abs_other)+1):num_vars;
abs_other(e1)=0;
end
end
if nargin < 8 || isempty(sod) || ~isnumeric(sod)
sod=1;
end
% Value must be a scalar
sod=sod(1);
if ~isequal(sod, 1);
sod=0;
end
if nargin < 9 || isempty(outfile) || (~ischar(outfile) && ~isnumeric(outfile))
outfile='outliers_stats';
end
if nargin < 10 || isempty(save_file) || ~isnumeric(save_file)
save_file=1;
end
if nargin < 11 || isempty(row_names)
row_names='';
end
if nargin < 12 || isempty(row_unit)
row_unit='';
end
if nargin < 13 || isempty(col_name)
col_name='';
end
if nargin < 14 || isempty(array_names)
array_names='';
end
if nargin < 15 || isempty(array_units)
array_units='';
end
rta2=rta;
% For analyzing impulsive noise data
% num_data_rows would be the nnumber of frequency bands
% num_cols would be the number of microphone channels
[num_data_rows, buf]=size(rta);
% calculate the maximum number of columns
max_num_cols=1;
for e1=1:num_data_rows;
if iscell(rta)
max_num_cols=max(max_num_cols, length(rta{e1, :}));
else
max_num_cols=max(max_num_cols, length(rta(e1, :)));
end
end
% initialize row_names if shorter than num_data_rows
if length(row_names) < num_data_rows
if iscell(row_names)
% If it is not a string assume it is an array of numbers
if ischar(row_names{1})
for e1=(length(row_names)+1):num_data_rows;
row_names{e1}='';
end
else
for e1=(length(row_names)+1):num_data_rows;
row_names{e1}=0;
end
end
else
if ischar(row_names(1)) && ~isempty(row_names)
buf=row_names;
row_names=cell(num_data_rows);
for e1=1:num_data_rows;
row_names{e1}=[buf, ' ', num2str(e1)];
end
else
for e1=(length(row_names)+1):num_data_rows;
row_names(e1)=0;
end
end
end
end
% initialize row_unit if shorter than num_data_rows
if length(row_unit) < num_data_rows
if iscell(row_unit)
% If it is not a string assume it is an array of numbers
if ischar(row_unit{1})
for e1=(length(row_unit)+1):num_data_rows;
row_unit{e1}='';
end
else
for e1=(length(row_unit)+1):num_data_rows;
row_unit{e1}=0;
end
end
else
if ~isempty(row_unit) && ischar(row_unit(1)) && ~isempty(row_unit)
buf=row_unit;
row_unit=cell(num_data_rows);
for e1=1:num_data_rows;
row_unit{e1}=[buf];
end
else
for e1=(length(row_unit)+1):num_data_rows;
row_unit(e1)=0;
end
end
end
end
% initialize col_name if shorter than max_num_cols
% if length(col_name) < max_num_cols
% if iscell(col_name)
% % If it is not a string assume it is an array of numbers
% if ischar(col_name{1})
% for e1=(length(col_name)+1):max_num_cols;
% col_name{e1}='';
% end
% else
% for e1=(length(col_name)+1):max_num_cols;
% col_name{e1}=0;
% end
% end
% else
% if ischar(col_name(1)) && ~isempty(col_name)
% buf=col_name;
% col_name=cell(max_num_cols, 1);
% for e1=1:max_num_cols;
% col_name{e1}=[buf, ' ', num2str(e1)];
% end
% else
% for e1=(length(col_name)+1):max_num_cols;
% col_name(e1)=0;
% end
% end
% end
%end
% col_name
if iscell(col_name)
% If it is not a string assume it is an array of numbers
if ischar(col_name{1})
col_name=col_name{1};
else
col_name=num2str(col_name{1});
end
else
% If it is not a string assume it is an array of numbers
if ischar(col_name)
% Do nothing
else
col_name=num2str(col_name);
end
end
% initialize array_names if shorter than num_vars=length(varargin)
if length(array_names) < (num_vars+1)
if iscell(array_names)
% If it is not a string assume it is an array of numbers
if ischar(array_names{1})
for e1=(length(array_names)+1):(num_vars+1);
array_names{e1}='';
end
else
for e1=(length(array_names)+1):(num_vars+1);
array_names{e1}=0;
end
end
else
if ischar(array_names(1)) && ~isempty(array_names)
buf=array_names;
array_names=cell((num_vars+1), 1);
for e1=1:(num_vars+1);
array_names{e1}=[buf, ' ', num2str(e1)];
end
else
for e1=(length(array_names)+1):(num_vars+1);
array_names(e1)=0;
end
end
end
end
% initialize array_units if shorter than num_vars=length(varargin)
if length(array_units) < (num_vars+1)
if iscell(array_units)
% If it is not a string assume it is an array of numbers
if ischar(array_units{1})
for e1=(length(array_units)+1):(num_vars+1);
array_units{e1}='';
end
else
for e1=(length(array_units)+1):(num_vars+1);
array_units{e1}=0;
end
end
else
if ischar(array_units(1)) && ~isempty(array_units)
buf=array_units;
array_units=cell((num_vars+1), 1);
for e1=1:(num_vars+1);
array_units{e1}=[buf, ' ', num2str(e1)];
end
else
for e1=(length(array_units)+1):(num_vars+1);
array_units(e1)=0;
end
end
end
end
ptsa=cell(num_data_rows,(num_vars+1));
nptsa=cell(num_data_rows,(num_vars+1));
mn_rt1a=zeros(num_data_rows,1);
mn_rt2a=zeros(num_data_rows,1);
stdrta=zeros(num_data_rows,1);
ci_inta=zeros(num_data_rows,1);
median_indexa=zeros(num_data_rows,1);
median_val=zeros(num_data_rows,1);
min_rta=zeros(num_data_rows,1);
max_rta=zeros(num_data_rows,1);
out_mn_rt1a=zeros(num_data_rows,1);
out_mn_rt2a=zeros(num_data_rows,1);
out_stdrta=zeros(num_data_rows,1);
out_ci_inta=zeros(num_data_rows,1);
out_median_indexa=zeros(num_data_rows,1);
out_median_val=zeros(num_data_rows,1);
out_min_rta=zeros(num_data_rows,1);
out_max_rta=zeros(num_data_rows,1);
% Initialize the maximum number of outliers in array rta data row.
max_num_out=0;
for e1=1:num_data_rows;
if iscell(rta);
buf1=rta{e1, :};
if iscell(buf1);
rt=buf1{1};
else
rt=buf1;
end
else
rt=rta(e1, :);
end
num_cols=length(rt);
if num_cols > 0
if isequal(abs_rta, 1)
rtb=abs(rt);
else
rtb=rt;
end
rtb=squeeze(rtb);
rtb=rtb(:);
% Calculate the robust estimate of the mean.
%
% Statistical language for this type of mean is
% "calculates the Least Median of Squares (LMS)
% location parameter of the columns of a matrix
% X. If X is a vector, it returns the LMS
% location parameter of its components. If X
% is a scalar, it returns X."
%if isequal(flag1(1), 1)
% [gm]=LMSloc(10.^(rtb./20));
% gm=20.*log10(gm);
%else
% [gm]=LMSloc(rtb);
%end
[ gm ] = rmean(rtb, flag1(1));
% calculate standard deviation
%if isequal(flag1(1), 1)
% stdrt=20.*log10(std(10.^(rts./20)));
%else
stdrt=std(rtb);
%end
if length(rtb) > 3 && isequal(sod, 0)
if ~isempty(rtb)
[res, raw] = fastlts(rtb);
npts=find(res.flag==0);
else
npts=[];
end
pts=setdiff( 1:num_cols, npts); % find the indices of the outliers
%pts=intersect( find(rtb > gm - num_std*stdrt), find(rtb < gm + num_std*stdrt)); % find all values within num_std std of geometric mean
%npts=setdiff( 1:num_cols, pts); % find the indices of the outliers
else
pts=1:num_cols;
npts=[];
end
if isempty(pts)
pts=1:num_cols;
npts=[];
end
% Determine the maximum number of outliers in any rta data row
max_num_out=max([max_num_out, length(npts)]);
% indices of set of non-outliers (i.e. values kept)
ptsa{e1, 1}=pts;
% indices of set of outliers (i.e. values discarded from
% descriptive statistics)
nptsa{e1, 1}=npts;
% select the set of non-outliers data for calculating descriptive statistics
if isequal(abs_rta, 1)
rts=abs(rt(pts));
else
rts=rt(pts);
end
% Calculate the arithmetic mean.
if isequal(flag1(1), 1)
[mn_rt1]=mean(10.^(rts./20));
mn_rt1=20.*log10(mn_rt1);
else
[mn_rt1]=mean(rts);
end
% Calculate the robust estimate of the mean
%if isequal(flag1(1), 1)
% [mn_rt2]=LMSloc(10.^(rts./20));
% mn_rt2=20.*log10(mn_rt2);
%else
% [mn_rt2]=LMSloc(rts);
%end
[ mn_rt2 ] = rmean(rts, flag1(1));
% calculate standard deviation
%if isequal(flag1(1), 1)
% stdrt=20.*log10(std(10.^(rts./20)));
%else
stdrt=std(rts);
%end
% calculate 95% confidence interval of
% the standard error of the
% t-distribution with a two-sided test
[ci_int]=t_confidence_interval(rts, 0.95);
% Calculate the median
if isequal(flag1(1), 1)
[medianrt]=median(10.^(rts./20));
medianrt=20.*log10(medianrt);
else
medianrt=median(rts);
end
% Calculate the median index
% (data point closest ot the median value)
[mbuf ix]=min(abs(rts-medianrt));
% Calculate the minimum
minrt=min(rts);
% Calculate the maximum
maxrt=max(rts);
mn_rt1a(e1,1)= m_round(mn_rt1, round_kind(1), round_digits(1));
mn_rt2a(e1,1)= m_round(mn_rt2, round_kind(1), round_digits(1));
stdrta(e1,1)= m_round(stdrt, 1, 3);
ci_inta(e1,1)= m_round(ci_int, 1, 3);
median_indexa(e1,1)=m_round(ix, 0, 0);
median_val(e1,1)= m_round(medianrt, round_kind(1), round_digits(1));
min_rta(e1,1)= m_round(minrt, round_kind(1), round_digits(1));
max_rta(e1,1)= m_round(maxrt, round_kind(1), round_digits(1));
if ~isequal(sod, 1) && ~isempty(npts)
% select the set of outliers data for calculating descriptive statistics
if isequal(abs_rta, 1)
rts=abs(rt(npts));
else
rts=rt(npts);
end
% Calculate the arithmetic mean.
if isequal(flag1(1), 1)
[mn_rt1]=mean(10.^(rts./20));
mn_rt1=20.*log10(mn_rt1);
else
[mn_rt1]=mean(rts);
end
% Calculate the robust estimate of the mean
%if isequal(flag1(1), 1)
% [mn_rt2]=LMSloc(10.^(rts./20));
% mn_rt2=20.*log10(mn_rt2);
%else
% [mn_rt2]=LMSloc(rts);
%end
[ mn_rt2 ] = rmean(rts, flag1(1));
% calculate standard deviation
%if isequal(flag1(1), 1)
% stdrt=20.*log10(std(10.^(rts./20)));
%else
stdrt=std(rts);
%end
% calculate 95% confidence interval of
% the standard error of the
% t-distribution with a two-sided test
[ci_int]=t_confidence_interval(rts, 0.95);
% Calculate the median
if isequal(flag1(1), 1)
[medianrt]=median(10.^(rts./20));
medianrt=20.*log10(medianrt);
else
medianrt=median(rts);
end
% Calculate the median index
% (data point closest ot the median value)
[mbuf ix]=min(abs(rts-medianrt));
% Convert to Median Index of the original data array
ix=npts(ix);
% Calculate the minimum
minrt=min(rts);
% Calculate the maximum
maxrt=max(rts);
out_mn_rt1a(e1,1)= m_round(mn_rt1, round_kind(1), round_digits(1));
out_mn_rt2a(e1,1)= m_round(mn_rt2, round_kind(1), round_digits(1));
out_stdrta(e1,1)= m_round(stdrt, 1, 3);
out_ci_inta(e1,1)= m_round(ci_int, 1, 3);
out_median_indexa(e1,1)=m_round(ix, 0, 0);
out_median_val(e1,1)= m_round(medianrt, round_kind(1), round_digits(1));
out_min_rta(e1,1)= m_round(minrt, round_kind(1), round_digits(1));
out_max_rta(e1,1)= m_round(maxrt, round_kind(1), round_digits(1));
else
out_mn_rt1a(e1,1)=0;
out_mn_rt2a(e1,1)=0;
out_stdrta(e1,1)=0;
out_ci_inta(e1,1)=0;
out_median_indexa(e1,1)=0;
out_median_val(e1,1)=0;
out_min_rta(e1,1)=0;
out_max_rta(e1,1)=0;
end
else
% if the data array is empty
ptsa{e1,1}=[];
nptsa{e1,1}=[];
mn_rt1a(e1,1)=0;
mn_rt2a(e1,1)=0;
stdrta(e1,1)=0;
ci_inta(e1,1)=0;
median_indexa(e1,1)=0;
median_val(e1,1)=0;
min_rta(e1,1)=0;
max_rta(e1,1)=0;
out_mn_rt1a(e1,1)=0;
out_mn_rt2a(e1,1)=0;
out_stdrta(e1,1)=0;
out_ci_inta(e1,1)=0;
out_median_indexa(e1,1)=0;
out_median_val(e1,1)=0;
out_min_rta(e1,1)=0;
out_max_rta(e1,1)=0;
end
end
% Save data to the output variable rt_stats
rt_stats=[mn_rt1a, mn_rt2a, stdrta, ci_inta, median_indexa, median_val, min_rta, max_rta];
% Calculate the number of descriptive statistics
num_stats=size(rt_stats, 2);
% % Save data to the output variable rt_outlier_stat
rt_outlier_stats=[out_mn_rt1a, out_mn_rt2a, out_stdrta, out_ci_inta, out_median_indexa, out_median_val, out_min_rta, out_max_rta];
% % Initialize the output variables other_stats and other_outlier_stats
other_stats=zeros(num_vars, num_data_rows, num_stats);
other_outlier_stats=zeros(num_vars, num_data_rows, num_stats);
for e2=1:num_vars;
rta=varargin{e2};
[num_data_rows, buf]=size(rta);
for e1=1:num_data_rows;
if iscell(rta);
buf1=rta{e1, :};
if iscell(buf1);
rt=buf1{1};
else
rt=buf1;
end
else
rt=rta(e1, :);
end
num_cols=length(rt);
if num_cols > 0
if ~isequal(dep_var, 1)
% Calculate the robust estimate of the mean.
%
% Statistical language for this type of mean is
% "calculates the Least Median of Squares (LMS)
% location parameter of the columns of a matrix
% X. If X is a vector, it returns the LMS
% location parameter of its components. If X
% is a scalar, it returns X."
% select the set of outliers data for calculating descriptive statistics
if isequal( abs_other(e2), 1)
rto=abs(rt);
else
rto=rt;
end
rto=squeeze(rto);
rto=rto(:);
%if isequal(flag1(1+e2), 1)
% [gm]=LMSloc(10.^(rto./20));
% gm=20.*log10(gm);
%else
% [gm]=LMSloc(rto);
%end
[ gm ] = rmean(rto, flag1(1+e2));
% calculate standard deviation
%if isequal(flag1(1+e2), 1)
% stdrt=20.*log10(std(10.^(rts./20)));
%else
stdrt=std(rt);
%end
if length(rt) > 3 && isequal(sod, 0)
if ~isempty(rto)
[res, raw] = fastlts(rto);
npts=find(res.flag==0);
else
npts=[];
end
pts=setdiff( 1:num_cols, npts); % find the indices of the outliers
%pts=intersect( find(rto > gm - num_std*stdrt), find(rto < gm + num_std*stdrt)); % find all values within num_std std of geometric mean
%npts=setdiff( 1:num_cols, pts); % find the indices of the outliers
else
pts=1:num_cols;
npts=[];
end
if isempty(pts)
pts=1:num_cols;
npts=[];
end
% Determine the maximum number of outliers in any rta data row
max_num_out=max([max_num_out, length(npts)]);
% indices of set of non-outliers (i.e. values kept)
ptsa{e1, e2+1}=pts;
% indices of set of outliers (i.e. values discarded from
% descriptive statistics)
nptsa{e1, e2+1}=npts;
else
[mp1 np1]=size(ptsa);
if mp1 < e1
pts=ptsa{1, 1};
ptsa{e1, e2+1}=ptsa{1, 1};
else
pts=ptsa{e1, 1};
ptsa{e1, e2+1}=ptsa{e1, 1};
end
end
% select the set of non-outliers data for calculating
% descriptive statistics
if isequal( abs_other(e2), 1)
rts=abs(rt(pts));
else
rts=rt(pts);
end
% Calculate the arithmetic mean.
if isequal(flag1(1+e2), 1)
[mn_rt1]=mean(10.^(rts./20));
mn_rt1=20.*log10(mn_rt1);
else
[mn_rt1]=mean(rts);
end
% Calculate the robust estimate of the mean
if ~isempty(rts)
% if isequal(flag1(1+e2), 1)
% [mn_rt2]=LMSloc(10.^(rts./20));
% mn_rt2=20.*log10(mn_rt2);
% else
% [mn_rt2]=LMSloc(rts);
% end
[ mn_rt2 ] = rmean(rts, flag1(1+e2));
else
mn_rt2=[];
end
% calculate standard deviation
%if isequal(flag1(1+e2), 1)
% stdrt=20.*log10(std(10.^(rts./20)));
%else
stdrt=std(rts);
%end
% calculate 95% confidence interval of
% the standard error of the
% t-distribution with a two-sided test
[ci_int]=t_confidence_interval(rts, 0.95);
% Calculate the median
if isequal(flag1(1+e2), 1)
[medianrt]=median(10.^(rts./20));
medianrt=20.*log10(medianrt);
else
[medianrt]=median(rts);
end
% Calculate the median index
% (data point closest ot the median value)
[mbuf ix]=min(abs(rts-medianrt));
% Calculate the minimum
minrt=min(rts);
% Calculate the maximum
maxrt=max(rts);
other_stats(e2, e1, 1)=m_round(mn_rt1, round_kind(e2+1), round_digits(e2+1));
other_stats(e2, e1, 2)=m_round(mn_rt2, round_kind(e2+1), round_digits(e2+1));
other_stats(e2, e1, 3)=m_round(stdrt, 1, 3);
other_stats(e2, e1, 4)=m_round(ci_int, 1, 3);
other_stats(e2, e1, 5)=m_round(ix, 0, 0);
other_stats(e2, e1, 6)=m_round(medianrt, round_kind(e2+1), round_digits(e2+1));
other_stats(e2, e1, 7)=m_round(minrt, round_kind(e2+1), round_digits(e2+1));
other_stats(e2, e1, 8)=m_round(maxrt, round_kind(e2+1), round_digits(e2+1));
if ~isequal(sod, 1) && ~isempty(npts)
if isequal(dep_var, 1)
[mp1 np1]=size(nptsa);
if mp1 < e1
npts=nptsa{1, 1};
nptsa{e1, e2+1}=nptsa{1, 1};
else
npts=nptsa{e1, 1};
nptsa{e1, e2+1}=nptsa{e1, 1};
end
end
% select the set of outliers data for calculating descriptive statistics
if isequal( abs_other(e2), 1)
rts=abs(rt(npts));
else
rts=rt(npts);
end
% Calculate the arithmetic mean.
if isequal(flag1(1+e2), 1)
[mn_rt1]=mean(10.^(rts./20));
mn_rt1=20.*log10(mn_rt1);
else
[mn_rt1]=mean(rts);
end
% Calculate the robust estimate of the mean
%if isequal(flag1(1+e2), 1)
% [mn_rt2]=LMSloc(10.^(rts./20));
% mn_rt2=20.*log10(mn_rt2);
%else
% [mn_rt2]=LMSloc(rts);
%end
[ mn_rt2 ] = rmean(rts, flag1(1+e2));
% calculate standard deviation
%if isequal(flag1(1+e2), 1)
% stdrt=20.*log10(std(10.^(rts./20)));
%else
stdrt=std(rts);
%end
% calculate 95% confidence interval of
% the standard error of the
% t-distribution with a two-sided test
[ci_int]=t_confidence_interval(rts, 0.95);
% Calculate the median
if isequal(flag1(1+e2), 1)
[medianrt]=median(10.^(rts./20));
medianrt=20.*log10(medianrt);
else
[medianrt]=median(rts);
end
% Calculate the median index
% (data point closest ot the median value)
[mbuf ix]=min(abs(rts-medianrt));
% Convert to Median Index of the original data array
ix=npts(ix);
% Calculate the minimum
minrt=min(rts);
% Calculate the maximum
maxrt=max(rts);
other_outlier_stats(e2, e1, 1)=m_round(mn_rt1, round_kind(e2+1), round_digits(e2+1));
other_outlier_stats(e2, e1, 2)=m_round(mn_rt2, round_kind(e2+1), round_digits(e2+1));
other_outlier_stats(e2, e1, 3)=m_round(stdrt, 1, 3);
other_outlier_stats(e2, e1, 4)=m_round(ci_int, 1, 3);
other_outlier_stats(e2, e1, 5)=m_round(ix, 0, 0);
other_outlier_stats(e2, e1, 6)=m_round(medianrt, round_kind(e2+1), round_digits(e2+1));
other_outlier_stats(e2, e1, 7)=m_round(minrt, round_kind(e2+1), round_digits(e2+1));
other_outlier_stats(e2, e1, 8)=m_round(maxrt, round_kind(e2+1), round_digits(e2+1));
else
other_outlier_stats(e2, e1, 1:8)=0;
end
else
% if the data is empty
other_stats(e2, e1, 1:8)=0;
other_outlier_stats(e2, e1, 1:8)=0;
end
end
end
% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% Write the descriptive statistics to the tab delimited outfile
%
% % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Write Data Vertically
if isequal(save_file, 1)
% check if outfile is a filename or a fid
if isa(outfile, 'double') && outfile > -1
flag2=1;
fid=outfile;
else
flag2=0;
[filename_base, ext]=file_extension(outfile);
fid=fopen([filename_base '.txt'], 'w');
end
for e2=1:(1+num_vars);
if isequal(e2, 1)
rta=rta2;
[num_data_rows, buf]=size(rta);
mn_rt1a=rt_stats(:, 1);
mn_rt2a=rt_stats(:, 2);
stdrta=rt_stats(:, 3);
ci_inta=rt_stats(:, 4);
median_indexa=rt_stats(:, 5);
median_val=rt_stats(:, 6);
min_rta=rt_stats(:, 7);
max_rta=rt_stats(:, 8);
out_mn_rt1a=rt_outlier_stats(:, 1);
out_mn_rt2a=rt_outlier_stats(:, 2);
out_stdrta=rt_outlier_stats(:, 3);
out_ci_inta=rt_outlier_stats(:, 4);
out_median_indexa=rt_outlier_stats(:, 5);
out_median_val=rt_outlier_stats(:, 6);
out_min_rta=rt_outlier_stats(:, 7);
out_max_rta=rt_outlier_stats(:, 8);
else
rta=varargin{e2-1};
[num_data_rows, buf]=size(rta);
for e1=1:num_data_rows;
mn_rt1a(e1, 1)=other_stats(e2-1, e1, 1);
mn_rt2a(e1, 1)=other_stats(e2-1, e1, 2);
stdrta(e1, 1)=other_stats(e2-1, e1, 3);
ci_inta(e1, 1)=other_stats(e2-1, e1, 4);
median_indexa(e1, 1)=other_stats(e2-1, e1, 5);
median_val(e1, 1)=other_stats(e2-1, e1, 6);
min_rta(e1, 1)=other_stats(e2-1, e1, 7);
max_rta(e1, 1)=other_stats(e2-1, e1, 8);
end
for e1=1:num_data_rows;
out_mn_rt1a(e1, 1)=other_outlier_stats(e2-1, e1, 1);
out_mn_rt2a(e1, 1)=other_outlier_stats(e2-1, e1, 2);
out_stdrta(e1, 1)=other_outlier_stats(e2-1, e1, 3);
out_ci_inta(e1, 1)=other_outlier_stats(e2-1, e1, 4);
out_median_indexa(e1, 1)=other_outlier_stats(e2-1, e1, 5);
out_median_val(e1, 1)=other_outlier_stats(e2-1, e1, 6);
out_min_rta(e1, 1)=other_outlier_stats(e2-1, e1, 7);
out_max_rta(e1, 1)=other_outlier_stats(e2-1, e1, 8);
end
end
%Print the row header for each data set;
if isequal(e2, 1)
fprintf(fid, 'Data and Descriptive Statistics of Main Data Set to determine Outliers\r\n\r\n');
fprintf(fid, 'Array and Row Names\tData\t');
tabs1='';
for e1=1:(max_num_cols);
tabs1=[tabs1, '\t'];
end
fprintf(fid, tabs1);
if ~isequal(sod, 1) && logical(max_num_out > 0)
fprintf(fid, 'Descriptive Statistics of Data with Outliers Removed\t');
tabs1='';
for e1=1:(num_stats);
tabs1=[tabs1, '\t'];
end
fprintf(fid, tabs1);
fprintf(fid,'Analysis of Outliers');
tabs1='';
for e1=1:(max_num_out+1);
tabs1=[tabs1, '\t'];
end
fprintf(fid, tabs1);
fprintf(fid, 'Descriptive Statistics of Outliers');
else
fprintf(fid, 'Descriptive Statistics of Data ');
end
fprintf(fid, '\r\n\r\n\t');
elseif isequal(e2, 2)
fprintf(fid, 'Other Data and Statistics\t');
else
fprintf(fid, '\t');
end
if isequal(e2, 1)
fprintf(fid, '%s\t', col_name);
tabs1='';
for e1=1:(max_num_cols-1);
tabs1=[tabs1, '\t'];
end
else
tabs1='';
for e1=1:(max_num_cols);
tabs1=[tabs1, '\t'];
end
end
fprintf(fid, tabs1);
if num_cols > 2
fprintf(fid, '\tArithmetic Mean\tRobust Mean\tStandard Deviation\t95+-%%Confidence Interval\tMedian Index\tMedian\tMinimum\tMaximum');
% If there are outliers then print all of the descriptive
% statistics of the outliers.
if max_num_out > 0
fprintf(fid,'\t\tOutliers');
tabs1='';
for e3=1:(max_num_out);
tabs1=[tabs1, '\t'];
end
fprintf(fid, tabs1);
fprintf(fid, '\tArithmetic Mean\tRobust Mean\tStandard Deviation\t95+-%%Confidence Interval\tMedian Index\tMedian\tMinimum\tMaximum\t\r\n');
else
fprintf(fid, '\r\n');
end
else
fprintf(fid, '\tArithmetic Mean\tRobust Mean\t\r\n');
end
fprintf(fid, '%s\t', [array_names{e2}, ' ', array_units{e2} ]);
nums=1:(max_num_cols);
fprintf(fid, '%i\t', nums);
if e2 > length(array_units)
e3=length(array_units);
else
e3=e2;
end
if max_num_cols > 2
% Print the units of the descriptive statistics
% they have the same units as the array; however, the median
% index has the units of an index of the median of the data array.
fprintf(fid, '\t');
for e1=1:4;
fprintf(fid, [array_units{e3}, '\t']);
end
fprintf(fid, '%s\t', 'Index');
for e1=6:num_stats;
fprintf(fid, [array_units{e3}, '\t']);
end
if max_num_out > 0
fprintf(fid, '\t%s\t', 'Indices');
tabs1='';
for e1=1:(max_num_out);
tabs1=[tabs1, '\t'];
end
fprintf(fid, tabs1);
% Print the units of the descriptive statistics
% they have the same unitsas the array; however, the median
% index has the units of an index.
for e1=1:4;
fprintf(fid, [array_units{e3}, '\t']);
end
fprintf(fid, '%s\t', 'Index');
for e1=6:num_stats;
fprintf(fid, [array_units{e3}, '\t']);
end
end
fprintf(fid, '\r\n');
else
fprintf(fid, ['\t' array_units{e3}, '\t', array_units{e3}, '\t\r\n']);
end
[num_data_rows, buf]=size(rta);
for e1=1:num_data_rows;
if iscell(rta);
buf1=rta{e1, :};
if iscell(buf1);
rt=buf1{1};
else
rt=buf1;
end
else
rt=rta(e1, :);
end
num_cols=length(rt);
[mp1 np1]=size(ptsa);
if mp1 < e1
pts=ptsa{1,e2};
npts=nptsa{1,e2};
else
pts=ptsa{e1,e2};
npts=nptsa{e1, e2};
end
mn_rt1=mn_rt1a(e1);
mn_rt2=mn_rt2a(e1);
std_rt=stdrta(e1);
ci_int=ci_inta(e1);
ix=median_indexa(e1);
medianrt=median_val(e1);
minrt=min_rta(e1);
maxrt=max_rta(e1);
out_mn_rt1=out_mn_rt1a(e1);
out_mn_rt2=out_mn_rt2a(e1);
out_std_rt=out_stdrta(e1);
out_ci_int=out_ci_inta(e1);
out_ix=out_median_indexa(e1);
out_medianrt=out_median_val(e1);
out_minrt=out_min_rta(e1);
out_maxrt=out_max_rta(e1);
if ~isempty(row_unit)
if iscell(row_unit)
if ischar(row_unit{e1})
row_unit1=row_unit{e1};
else
% If its not a string assume its a number
row_unit1=num2str(row_unit{e1});
end
elseif ischar(row_unit)
row_unit1=row_unit;
else
row_unit1=num2str(row_unit(e1));
end
else
row_unit1='';
end
if isempty(row_names)
fprintf(fid, '%i\t', e1);
else
if length(row_names) >= e1
if iscell(row_names)
if ischar(row_names{e1})
if isempty(row_names{e1})
fprintf(fid, '%s\t', [num2str(e1), ' ', row_unit1]);
else
fprintf(fid, '%s\t', [row_names{e1}, ' ', row_unit1]);
end
else
% If its not a string assume its a number
if isempty(num2str(row_names{e1}))
fprintf(fid, '%s\t', [num2str(e1), ' ', row_unit1]);
else
fprintf(fid, '%s\t', [num2str(row_names{e1}), ' ', row_unit1]);
end
end
else
if isempty(num2str(row_names(e1)))
fprintf(fid, '%s\t', [num2str(e1), ' ', row_unit1]);
else
if ischar(row_names(e1))
fprintf(fid, '%s\t', [row_names ' ', row_unit1]);
else
fprintf(fid, '%s\t', [num2str(row_names(e1)), ' ', row_unit1]);
end
end
end
else
fprintf(fid, '%i\t', e1);
end
end
% Print the Data
if ~(any(isempty(rt)) || any(isempty(round_kind)) || any(isempty(round_digits)))
[A2, A_str]=m_round(rt, round_kind(e2), round_digits(e2));
else
A_str={};
A2=[];
end
for e3=1:length(A_str);
fprintf(fid, '%s\t', A_str{e3});
end
if max_num_cols > num_cols
for e3=1:(max_num_cols-num_cols);
fprintf(fid, '\t' );
end
end
if num_cols > 2
% Print the Descriptive Statistics of all the data
[A2, A_str]=m_round([mn_rt1 mn_rt2 std_rt, ci_int ix medianrt minrt maxrt], [round_kind(e2)*[1 1] [1 1 0] round_kind(e2)*[1 1 1]], [round_digits(e2)*[1 1] [3 3 0] round_digits(e2)*[1 1 1]]);
fprintf(fid, '\t' );
for e3=1:length(A_str);
fprintf(fid, '%s\t', A_str{e3});
end
fprintf(fid, '\t' );
for e3=1:length(npts);
fprintf(fid, '%d\t', npts(e3) );
end
if max_num_out > 0
fprintf(fid, '\t' );
for e3=1:(max_num_out-length(npts));
fprintf(fid, '\t' );
end
% Print the Descriptive Statistics of the outlier data
[A2, A_str]=m_round([out_mn_rt1 out_mn_rt2 out_std_rt, out_ci_int out_ix out_medianrt out_minrt out_maxrt], [round_kind(e2)*[1 1] [1 1 0] round_kind(e2)*[1 1 1]], [round_digits(e2)*[1 1] [3 3 0] round_digits(e2)*[1 1 1]]);
for e3=1:length(A_str);
fprintf(fid, '%s\t', A_str{e3});
end
end
else
% Print the Descriptive Statistics of all the data
[A2, A_str]=m_round([mn_rt1, mn_rt2], round_kind(e2), round_digits(e2));
fprintf(fid, '\t' );
for e3=1:length(A_str);
fprintf(fid, '%s\t', A_str{e3});
end
end
fprintf(fid, '\r\n');
end
fprintf(fid, '\r\n');
end
if isequal(flag2, 0)
fclose(fid);
fclose('all');
end
end
if isequal(sod, 1) || logical(max_num_out < 1) || logical(max_num_cols < 2)
rt_outlier_stats=[];
other_outlier_stats=[];
end
% Write Data Horizontally
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