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POFeature selection using the penalizedLDA package
 

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  1. POFeature selection using the penalizedLDA package
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    <p>I am trying to use the penalizedLDA package to run a penalized linear discriminant analysis in order to select the "most meaningful" variables. I have searched here and on other sites for help in accessing the the output from the penalized model to no avail. </p> <p>My data comprises of 400 varaibles and 44 groups. Code I used and results I got thus far:</p> <pre><code>yy.m&lt;-as.matrix(yy) #Factors/groups xx.m&lt;-as.matrix(xx) #Variables cv.out&lt;-PenalizedLDA.cv(xx.m,yy.m,type="standard") ## aplly the penalty out &lt;- PenalizedLDA(xx.m,yy.m,lambda=cv.out$bestlambda,K=cv.out$bestK) </code></pre> <p>Too get the structure of the output from the anaylsis:</p> <pre><code>&gt; str(out) List of 10 $ discrim: num [1:401, 1:4] -0.0234 -0.0219 -0.0189 -0.0143 -0.0102 ... $ xproj : num [1:100, 1:4] -8.31 -14.68 -11.07 -13.46 -26.2 ... $ K : int 4 $ crits :List of 4 ..$ : num [1:4] 2827 2827 2827 2827 ..$ : num [1:4] 914 914 914 914 ..$ : num [1:4] 162 162 162 162 ..$ : num [1:4] 48.6 48.6 48.6 48.6 $ type : chr "standard" $ lambda : num 0 $ lambda2: NULL $ wcsd.x : Named num [1:401] 0.0379 0.0335 0.0292 0.0261 0.0217 ... ..- attr(*, "names")= chr [1:401] "R400" "R405" "R410" "R415" ... $ x : num [1:100, 1:401] 0.147 0.144 0.145 0.141 0.129 ... ..- attr(*, "dimnames")=List of 2 .. ..$ : NULL .. ..$ : chr [1:401] "R400" "R405" "R410" "R415" ... $ y : num [1:100, 1] 2 2 2 2 2 1 1 1 1 1 ... - attr(*, "class")= chr "penlda" </code></pre> <p>I am interested in obtaining a list or matrix of the top 20 variables for feature selection, more than likely based on the coefficients of the Linear discrimination. I realized I would have to sort the coefficients in descending order, and get the variable names matched to it. So the output I would expect is something like this imaginary example </p> <pre><code> V1 V2 R400 0.34 R1535 0.22... </code></pre> <p>Can anyone provide any pointers (not necessarily the R code). Thanks in advance.</p>
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