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POData transformation avoiding nested loops in R
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copied!<p>I have a contingency table data matrix with 6 columns and 37 rows. I need to apply a Chi squared transformation to give me Row profiles and Column profiles for a correspondence analysis.</p> <p>Unfortunately I've been told I will need to use nested loops to transform the data and carry out the CA (rather than doing it the more sensible ways in R). I was given the structure to use for my nested loop:</p> <pre><code>transformed.data=data0 for (row.index in 1:nrow(data)) { for (col.index in 1:ncol(data)) { transfomed.data[row.index,col.index]= "TRANSFORMATION"[row.index,col.index] } } </code></pre> <p>From what i understand by using the nested loop it will apply my <em>"TRANSFORMATION"</em> first to the rows and then to the columns.</p> <p>The transformation I want done on the data to get the row profiles is:</p> <blockquote> <p><strong>(</strong> X( <em>ij</em> ) / sum( X( <strong><em>i</em></strong> ) ) <strong>)</strong> / sqrt( sum( X( <strong><em>j</em></strong> ) ) ) </p> </blockquote> <p>While the transformation I want done on the data to get the column profiles is:</p> <blockquote> <p><strong>(</strong> X( <em>ij</em> ) / sum( X( <strong><em>j</em></strong> ) ) <strong>)</strong> / sqrt( sum( X( <strong><em>i</em></strong> ) ) ) </p> </blockquote> <p>What would I enter as my <em>"TRANSFORMATION"</em> in the last line of the nested loop to get it to output my desired transformation for profiles. Otherwise if I've miss understood the point of a nested loop here please describe what it would allow me to do.</p> <p>This is the code for a subset of my data:</p> <pre><code>matrix(c(15366,2079,411,366,23223,2667,699,819,31632,2724,717,1473,49938,3111,1062,11964) ,nrow=4,ncol=4,byrow=T) </code></pre> <p>So using this subset alone I would expect the row profile for the first row to be: </p> <pre><code>0.002432689 0.0003291397 6.506803e-05 5.794379e-05 </code></pre> <p>And the column profile for the first column to be:</p> <pre><code>0.0009473414, 0.0132572344, 0.0572742202, 0.0132863528 </code></pre>

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