Some more cleanup, removed redundant check.

README.md

@@ -14,17 +14,16 @@ The resulting estimates of slope and intercept are relatively insensitive to out
 The implementation in [TheilSen.m](TheilSen.m) is exact but naïve:
 It generates the set of all pairs of the _n_ input samples, resulting in an overall complexity of _O(n²)_ in speed and space.
 The resulting slope and offset are the median slope and offset of the lines defined by all data point pairs.  
-
 (Note that alternative implementations of the algorithm have lower complexity, and are thus much faster for large amounts of input samples.)
 
 ### No toolbox required
 
 This code is based on [Theil-Sen Robust Linear Regression](https://mathworks.com/matlabcentral/fileexchange/48294-theil-sen-robust-linear-regression), version 1.2.0.0, by Zachary Danziger.
 A key modification is to use `median(X, 'omitnan')` instead of `nanmedian(X)` to avoid dependency on the (commercially licensed) [Statistics Toolbox](https://mathworks.com/products/statistics.html).  
-See the [changelog](#changelog) below for further modifications.
-
 (Note that there are several other implementations on Mathworks's [File Exchange](https://mathworks.com/matlabcentral/fileexchange).
-Unfortunately most were found to depend on the Statistics Toolbox, [except one](https://mathworks.com/matlabcentral/fileexchange/43135-regression-utilities), which was judged to be slower and less versatile.)
+Unfortunately, most were found to depend on the Statistics Toolbox, [except one](https://mathworks.com/matlabcentral/fileexchange/43135-regression-utilities), which was judged to be slower and less versatile.)
+
+See the [changelog](#changelog) below for further modifications.
 
 ## Installation
 
@@ -36,9 +35,10 @@ Please refer to the comments in the header lines of [TheilSen.m](TheilSen.m).
 
 ### Example
 
-The script [example.m](example.m) simulates data based on known "true" values with minor, additive Gaussian noise.
+The script [example.m](example.m) simulates data based on known, "true" values with minor, additive Gaussian noise.
 The data are then corrupted with a small percentage of outliers.
-It then fits and compares the least squares with the Theil-Sen estimator.
+
+The data are fit with the Theil-Sen estimator and least squares, for comparison.
 Note how a few "unlucky" outliers can bias the least squares estimate (LS), but have little effect on the Theil-Sen estimator (TS).
 
 <img src="example.svg" alt="plot from example.m" width=500px />

TheilSen.m

 function coef = TheilSen(X, y)
-% THEILSEN performs Theil-Sen robust, simple linear regression(s) of X on y.
+% THEILSEN performs Theil-Sen robust simple linear regression(s) of X on y.
 % 
-% For convenience, the input X may contain more than one predictor variable.
-% But note that two or more predictor variables in X are treated as independent
-% simple regressions; do not confuse the output with multiple regression.
+% For convenience, the input X may contain more than one predictor variable;
+% but note that two or more predictor variables in X are treated as independent
+% simple regressions: Do not confuse the output with multiple regression.
 %
-% THEILSEN treats NaNs in X or y as missing values, and ignores them.
+% THEILSEN treats NaNs in X or y as missing values and ignores them.
 %
 % INPUT
-%   X: One or more columns vectors containing explanatory/predictor variables.
-%      Rows contain the observed predictor variables.
+%   X: One or more column vectors containing explanatory/predictor variables.
+%      Rows represent observations (assumed to be i.i.d.).
 %   y: A column vector containing the observations of the response variable.
 %
 % OUTPUT
 %   coef: Estimated regression coefficients for each predictor column in X, with
 %         respect to the response variable y. Each column in coef corresponds
-%         to one predictor in X, i.e. it has as many columns as X does.
+%         to one predictor in X, i.e. it will have as many columns as X.
 %         The first row, i.e. coef(1, :), contains the estimated offset(s).
 %         The second row, i.e. coef(2, :), contains the estimated slope(s).
 %         (This output format is chosen to avoid confusion, e.g. with previous
@@ -39,7 +39,7 @@ function coef = TheilSen(X, y)
 sizeX = size(X);
 sizeY = size(y);
 
-if length(sizeY) ~= 2 || sizeY(1) < 2 || sizeY(2) ~= 1 || ~isnumeric(X)
+if length(sizeY) ~= 2 || sizeY(1) < 2 || sizeY(2) ~= 1 || ~isnumeric(Y)
     error('Input y must be a column array of at least 2 observed responses.')
 end
 
@@ -51,16 +51,13 @@ if sizeX(1) ~= sizeY(1)
     error('The number of rows (observations) of X and y must match.')
 end
 
-Num_Obs = sizeX(1);  % rows in X and y are observations
+Num_Obs = sizeX(1);  % rows in X (and y) are observations
 Num_Pred = sizeX(2);  % columns in X are (independent) predictor variables
 
-if Num_Obs < 2
-    error('Expecting a data matrix Obs x Dim with at least 2 observations.')
-end
-
-%%% For the curious, here more readable code for 1 predictor column in X.
-%%% However, the special case is absorbed in the general version for
-%%% any number of columns in X, for the sake of code simplicity.
+%%% For the curious, here more readable code for 1 column in X.
+%%% This special case is absorbed in the general version below, for
+%%% any number of columns in X, to avoid code duplication.
+%
 % % calculate slope for all pairs of data points
 % C = nan(Num_Obs, Num_Obs);
 % for i = 1:Num_Obs-1