MATLAB: Generating contingency tables (with categorical data)

bootstrapcategorical datacontingency tables

Hi All,

I want to generate contingency tables, especially with categorical data. If possible, I want to generate samples with bootstrap, that is also with categorical data.

Can you help please? thanks..

Best Answer

If you have the Statistics Toolbox, then you can use the crosstab() function to generate contingency tables.

There are several ways to generate bootstrap samples. One handy function is randsample().

Related Solutions

MATLAB: Delete duplicated rows, (with saving some data)

This works:

A = {434846 'x'
434846 'y'
434850 'f'
434854 's'
434859 'A'
434859 'B'
434859 'C'
434862 'N'};
% 
A1 = cell2mat(A(:,1));
[Au,ia,ic] = unique(A1);
for k1 = 1:size(Au,1)
    A2{k1,:} = {Au(k1) A(k1==ic,2)};
end

EDIT — Print loop for ‘A2’:

for k1 = 1:size(A2,1)
    fprintf(1, ['\n%d\t' repmat('%s  ',1, length(A2{k1}{2}))],  A2{k1}{1}, char(A2{k1}{2}))
end
fprintf(1, '\n')

produces:

434846  xy  
434850  f  
434854  s  
434859  ABC  
434862  N

MATLAB: Cross Validated Classification Tree Contingency Table

It is not possible to access this data. However, coding is not hard. I presume you want to prune every tree to bestlevel returned by the cvLoss method. Here is how you can compute the overall confusion matrix (contingency table).

load ionosphere
cv = cvpartition(Y,'kfold',5);
Yhat = repmat(Y(1),numel(Y),1);
for k=1:5
    itrain = training(cv,k);
    itest = test(cv,k);
    tree = ClassificationTree.fit(X(itrain,:),Y(itrain));
    tree = prune(tree,'level',bestlevel);
    Yhat(itest) = predict(tree,X(itest,:));
end
confusionmat(Y,Yhat)

Best Answer

Related Solutions

MATLAB: Delete duplicated rows, (with saving some data)

MATLAB: Cross Validated Classification Tree Contingency Table

Related Question