Matlab Output into latex table (Matlab to Latex Table)

This post comments on turning Matlab output into raw latex for inclusion in a latex table, for using \begin{tabular} \end{tabular}.

My preferred way to do this is to use latex.m to convert a Matlab matrix into latex format that can then be posted into tabular.

This is a common problem in simulation studies, where you use Matlab to summarize the results of a simulation study into a table.

I. Caveats regarding my solution:
1. There are probably fancier ways to do this now. I'm using Matlab2latex technology from about 2002. You can check on the central file exchange
2. Also, there might be a way to do this in base-Matlab. I got some file off of Central file exchange. I will note that sometimes a basic solution is very helpful because there are no extra bells/whistles and it is not hard to figure out the options of simple user supplied functions.

II. Other Posted Solutions:
1. http://pundit.pratt.duke.edu/wiki/MATLAB:LaTeX_Table_Writer
2. You can see more advanced ideas in the CFX
http://www.mathworks.com/matlabcentral/fileexchange/index?utf8=%E2%9C%93&term=latex+table
3. matrix2latex seems pretty good too.

III. Dependencies (what file exchange .m files am I using)

IV. My solution

1. use latex.m - This file will output a MATLAB numeric matrix in a format suitable to paste into a LaTeX tabular environment. 
http://www.mathworks.com/matlabcentral/fileexchange/2832-latex
2. internal comment. see gmail FBlog with code. Also, see work from Screening paper.

Subscript, underscore in Latex

In the equation model, either will work fine. But for inline mathematics you notice a difference. Probalby the operator name isn't needed here, if \max is already defined.
$\max_i$
vs.
$\underset{i}{\operatorname{max}}$

Comment Command for Joint Editing

Idea is when we collaborate on a paper it is nice to have a method of leaving comments.

I. Solution write your own commands for each author to make a comment function: author1comment, author2comment.

preamble (need color, anything else?)
\usepackage{color}

commands:
%-----------------------

\def\boxit#1{\vbox{\hrule\hbox{\vrule\kern6pt \vbox{\kern6pt#1\kern6pt}\kern6pt\vrule}\hrule}}
\def\bfred#1{{\color{red}\bf#1}}
\def\bfblue#1{{\color{blue}\bf#1}}
\def\hlred#1{{\color{red}#1}}
\def\hlblue#1{{\color{blue}#1}}


\newcommand{\BHcomment}[1]{\vskip 2mm\boxit{\vskip 2mm{\color{red}\bf#1} {\color{black}\bf -- BH\vskip 2mm}}\vskip 2mm}
\newcommand{\DCcomment}[1]{\vskip 2mm\boxit{\vskip 2mm{\color{blue}\bf#1} {\color{black}\bf -- DC\vskip 2mm}}\vskip 2mm}
\newcommand{\RScomment}[1]{\vskip 2mm\boxit{\vskip 2mm{\color{green}\bf#1} {\color{black}\bf -- RS\vskip 2mm}}\vskip 2mm}


%-----------------------




usage in the body
\BHcomment{Bret writes his comment here. He emphasizes his point with a double exclamation point}

II. There are other solutions:
1. I think there is a track changes package in latex...but I also think I hate this package
2. Good old fashion, Jim gets green, Bob gets red, and Nancy gets blue
pre-amble \usepackage{color}
in text
\textcolor{red}{Here is a comment from Bob, He is *not* happy with this sentence}

Matlab Simulation - General Discrete Distribution (multivarite)

1. one option is built in randsample with weighted option, you sample with certain weights.

There are CFX options
2. Discrete (multinomial) distribution sampler

http://www.mathworks.com/matlabcentral/fileexchange/25481-discrete-multinomial-distribution-sampler

3. randp
http://www.mathworks.com/matlabcentral/fileexchange/8891

Matlab Simulation: Weighted Without Replacement Sampling

1. matlab's randsample doesn't handle this


Y = RANDSAMPLE(...,true,W) returns a weighted sample, using positive
    weights W, taken with replacement.  W is often a vector of probabilities.
    This function does not support weighted sampling without replacement.

2. Someone on CFX wrote a file for this
http://www.mathworks.com/matlabcentral/fileexchange/27263-modified-randsample
Returns V, a weigthed sample of K elements taken among X without replacement
X a vector of numerics
K amount of element to sample from x
W a vector of positive weights w, whose length is length(x)

3. I think this will take care of multivariate hypergeometric sampling. That is sampling without replacement when there are multiple types, because the different types each have a different probability of being selectted.

string compare in Matlab, strcmp -

http://www.mathworks.com/help/techdoc/ref/strcmp.html
1. Basic Idea
2. full example code



1. Basic Idea
Standard input:
method is a cell
method{1} is the string for the method
method{2} is variable input arguments


method{1} = 'gauss';

 strcmp(method{1}, 'gauss')

2. Full example code

function [y,output] = simResp(method, X, int, beta)
%simResp = simulate response data for regressin models.
%This allows you to simulate regression data from general models.
%NOTES:
%1. It is good to think about the data for observation i
% Here the covariates X_i are the ith row of X (hence 1 x p)
% y_i = X_i beta + ep_i, ep_i \sim N(0,v). Hence:
% y_i \sim N(\mu_i, v), where \mu_i = X_i beta
%2. When you think about it in this form, you see the connection to GLMs
%3. It is best to write a separate function for model and simply have them called inside here;
% it makes error check much easier. e.g. for logistic regression and ordinal logistic regression,
% I'm calling outside functions.
%INPUTS
% *method - 2 x 1 cell. method{1} specifies the regression model to simulate; method{2} will
% contain any additional needed inputs. See methods after Outputs for a description.
% *X - n x p matrix. Design matrix  n replicates and p varibles (does NOT
% include the intercept).
% *beta - p x 1 matrix. covariate vector
% vector.
% *v - positive scalar. The VARIANCE (take square root for standard deviation);
% of the epsilon.
% *int - scalar. the intercept (if you don't want it, set int = 0)
%OUTPUTS
% *y = n x 1 vector. The response vector from the regression (all methods will have this)
% *output - cell. It is an any extra outputs that we might need for certain methods. See the List of methods
% which is given after list of outputs.
%METHODS:
%1. Linear Regression (Gaussian error).
% *method{1} = 'gauss'
% *method{2} = v. positive scalar. The VARIANCE (take square root for standard deviation);
% of the epsilon (in general for linear regression you need to specify the variance of the of errors.
% *output = {}; empty, there is not additional output.
%2. Logistic regression (binary, this with the logistic link)
% *SEE: the function y = simLogReg(int, beta, X)
% *method{1} = 'LR' = logistic regression
% *method{2} = {} (there are no needed extra inputs)
% *output = {}; empty, there is not additional output.
%3. Ordinal logistic regression (right now it only allows for the proportional odds model)
% *SEE: the function [ordata, latdata, newcutoff] =  simOL(Zvector, cutoff)
% *KEY: set cutoff = -1 to generate the cutoff inside the function.
% *method{1} = 'OLR' = Ordinal Logistic Regression
% *method{2}
% *output

if strcmp(method{1}, 'gauss')
    %1. linear regression model
    %remember you need to take square root of v for standard dev.
    y = int + X*beta + normrnd(0,sqrt(method{2}), size(X,1),1);
    output = {};
elseif strcmp(method{1}, 'LR')
    %2. logistic regression
    y = simLogReg(int, beta, X);
    output = {};
elseif strcmp(method{1}, 'OLR')
    %3. ordinal logistic regression
   
    %a) first we need to generate the Zvector in the standard way
    %remember we don't use the intercept here, bc for ordinal the
    %intercept is decided by the cutoff.
    %Zvector = X*beta
       
    %b) now call the function
    [y, latdata, newcutoff] =  simOL(X*beta, cutoff)
    output{1} = latdata;
    output{2} = newcutoff;
else

    error('You have entered an illegal method name; method{1} is not supported by the function')

end

Simulation Set the Seed - Clock the Seed

To clock the seed (remember rand and randn use different seeds, right?)
randn('state', sum(100*clock))
rand('state', sum(100*clock))

But for rand, should you use twister?
http://amath.colorado.edu/computing/Matlab/OldTechDocs/ref/rand.html

rand('state',s)	Resets the state to s.
rand('state',0)	Resets the generator to its initial state.
rand('state',j)	For integer j, resets the generator to its j-th state.
rand('state',sum(100*clock))	Resets it to a different state each time.

Also, see Loren's post on this
http://blogs.mathworks.com/loren/2008/11/13/new-ways-with-random-numbers-part-ii/