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<p class="MsoNormal">Hi Everyone,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">I’m trying to perform the following task more efficiently in Rcpp, and am running into some trouble…<o:p></o:p></p>
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<p class="MsoNormal">Here’s a simplified example of the sort of R code I’m trying to optimize, for Y a binary random variable, X a categorical variable with 3 classes:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">For(I in 1:100000)<o:p></o:p></p>
<p class="MsoNormal">{<o:p></o:p></p>
<p class="MsoNormal"> glm(X~Y, family=”binomial”);<o:p></o:p></p>
<p class="MsoNormal">}<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">This seems like an excellent candidate for Rcpp--I simply wish to evaluate the ‘for’ loop in c++ to reduce the computational overhead.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">What I would like to <b>avoid</b> is having to re-write code for explicitly evaluating a generalized linear model in c++. Any quick-and-dirty solution is acceptable. Two approaches that I have tried and been unsuccessful with are:<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">-Attempting to expose the C glm methods from the R stats package<o:p></o:p></p>
<p class="MsoNormal">-Working with the RcppModels package [having trouble with this due to my inability to find documentation, not that there isn’t some out there]<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Any thoughts, references, or corrections to my above approaches would be appreciated.<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Thanks,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Jason<o:p></o:p></p>
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