r - calibration of the posterior probabilities -


currently work on calibration of probability. use calibration approach, called rescaling algorithm - source http://lem.cnrs.fr/portals/2/actus/dp_201106.pdf (page 7).

the algorithm wrote is:

rescaling_fun = function(x, y, z) {      p_korg  = z # yhat_test_prob$bad      p_k_c1  = sum(as.numeric(y) - 1)/length(y) # testset$bad     p_kt_c1 = sum(as.numeric(x) - 1)/length(x) # trainset$bad     p_k_c0  = sum(abs(as.numeric(y) - 2))/length(y)     p_kt_c0 = sum(abs(as.numeric(x) - 2))/length(x)      p_new <- ((p_k_c1/p_kt_c1) * p_korg)/((p_k_c0/p_k_c0) * (1 - p_korg) + (p_k_c0/p_k_c1) * (p_korg))    return(p_new) }

the input values are:

1. x - train_set$bad (actuals of `train set`) 2. y - test_set$bad (actuals of `test set`) 3. z - yhat_test_prob$bad (prediction on `test set`)

the problem - result values not within range of 0 , 1. please solve problem?

your formulas obtain probs (p_k_c1 ...) need modified. example, according paper, y binary variable (0, 1) , formula sum(y - 1)/length(y) negative - converts y values -1 or 0, followed adding them. consider should (sum(y)-1)/length(y). below example.

set.seed(1237) y <- sample(0:1, 10, replace = t) y [1] 0 1 0 0 0 1 1 0 1 1 # must negative sum(y - 1) - y 0 or 1 sum(as.numeric(y) - 1)/length(y) [1] -0.5 # modification  (sum(as.numeric(y)) - 1)/length(y) [1] 0.4

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