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copied!<p>It sounds like you might do well to just use the cost function (i.e. the one named <code>cost</code>) defined further down in the "Examples" section of <code>?cv.glm</code>. Quoting from that section:</p> <pre><code> # [...] Since the response is a binary variable an # appropriate cost function is cost <- function(r, pi = 0) mean(abs(r-pi) > 0.5) </code></pre> <p>This does essentially what you were trying to do with your example. Replacing your "no" and "yes" with <code>0</code> and <code>1</code>, lets say you have two vectors, <code>predict</code> and <code>response</code>. Then <code>cost()</code> is nicely designed to take them and return the mean classification rate:</p> <pre><code>## Simulate some reasonable data set.seed(1) predict <- seq(0.1, 0.9, by=0.1) response <- rbinom(n=length(predict), prob=predict, size=1) response # [1] 0 0 0 1 0 0 0 1 1 ## Demonstrate the function 'cost()' in action cost(response, predict) # [1] 0.3333333 ## Which is right, as 3/9 elements (4, 6, & 7) are misclassified ## (assuming you use 0.5 as the cutoff for your predictions). </code></pre> <p>I'm guessing the trickiest bit of this will be just getting your mind fully wrapped around the idea of passing a function in as an argument. (At least that was for me, for the longest time, the hardest part of using the <strong>boot</strong> package, which requires that move in a fair number of places.)</p> <hr> <p><strong>Added on 2016-03-22:</strong></p> <p>The function <code>cost()</code>, given above is in my opinion unnecessarily obfuscated; the following alternative does exactly the same thing but in a more expressive way:</p> <pre><code>cost <- function(r, pi = 0) { mean((pi < 0.5) & r==1 | (pi > 0.5) & r==0) } </code></pre>

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