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2013-05-31T06:45:39.483
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2013-05-31T06:45:39.483
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I will try to explain the cost function in simple words. Let's take <code>cv.glm(data, glmfit, cost, K)</code> arguments step by step: <ol> <li><code>data</code> The data consists of many observations. Think of it like series of numbers or even.</li> <li><code>glmfit</code> It is generalized linear model, which runs on the above series. But there is a catch it splits data into several parts equal to K. And runs <code>glmfit</code> on each of them separately (test set), taking the rest of them as training set. The output of <code>glmfit</code> is a series consisting of same number of elements as the split input passed.</li> <li><code>cost</code> Cost Function. It takes two arguments first the split input series(test set), and second the output of <code>glmfit</code> on the test input. The default is mean square error function. <img src="https://i.stack.imgur.com/VOQqy.png" alt="The MSE function">. It sums the square of difference between observed data point and predicted data point. Inside the function a loop runs over the test set (output and input should have same number of elements) calculates difference, squares it and adds to output variable.</li> <li><code>K</code> The number to which the input should be split. Default gives leave one out cross validation.</li> </ol> Judging from your cost function description. Your input(x) would be a set of numbers between 0 and 1 (0-0.5 = no and 0.5-1 = yes) and output(y) is 'yes' or 'no'. So error(e) between observation(x) and prediction(y) would be : <pre><code>cost<- function(x, y){ e=0 for (i in 1:length(x)){ if(x[i]>0.5) { if( y[i]=='yes') {e=0} else {e=x[i]-0.5} }else { if( y[i]=='no') {e=0} else {e=0.5-x[i]} } e=e*e #square error } e=e/i #mean square error return (e) } </code></pre> Sources : <a href="http://www.cs.cmu.edu/~schneide/tut5/node42.html" rel="nofollow noreferrer">http://www.cs.cmu.edu/~schneide/tut5/node42.html</a>
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1. POcost function in cv.glm of boot library in R
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