# APPEstimation-package: R function to calculate model performance measure adjusted... In APPEstimation: Adjusted Prediction Model Performance Estimation

## Description

This package provides the function to estimate model performance measures (L_1, L_2, C-statistics). The difference in the distribution of predictors between two datasets (training and validation) is adjusted by a density ratio estimate.

## Details

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## Author(s)

Eisuke Inoue, Hajime Uno

Maintainer: Eisuke Inoue <eisuke.inoue@marianna-u.ac.jp>

## References

Sugiyama, M., Suzuki, T. & Kanamori, T. Density Ratio Estimation in Machine Learning. Cambridge University Press 2012. ISBN:9781139035613.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```set.seed(100) # generating learning data n0 = 100 Z = cbind(rbeta(n0, 5, 5), rbeta(n0, 5, 5)) Y = apply(Z, 1, function (xx) { rbinom(1, 1, (1/(1+exp(-(sum(c(-2,2,2) * c(1,xx)))))))}) dat = data.frame(Y=Y, Za=Z[,1], Zb=Z[,2]) # the model to be evaluated mdl = glm(Y~., binomial, data=dat) # validation dataset, with different centers on predictors n1 = 100 Z1 = cbind(rbeta(n1, 6, 4), rbeta(n1, 6, 4)) Y1 = apply(Z1, 1, function (xx) { rbinom(1, 1, (1/(1+exp(-(sum(c(-2,2,2) * c(1,xx)))))))}) dat1 = data.frame(Y=Y1, Za=Z1[,1], Zb=Z1[,2]) # calculation of L1 and L2 for this model appe.glm(mdl, dat, dat1, reps=0) ```

APPEstimation documentation built on May 2, 2019, 6:34 a.m.