APPEstimation-package {APPEstimation} | R Documentation |
R function to calculate model performance measure adjusted for predictor distributions.
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
Package: | APPEstimation |
Type: | Package |
Title: | Adjusted Prediction Model Performance Estimation |
Version: | 0.1.1 |
Depends: | densratio |
Date: | 2018-1-4 |
Author: | Eisuke Inoue, Hajime Uno |
Maintainer: | Eisuke Inoue <eisuke.inoue@marianna-u.ac.jp> |
Description: | Calculating predictive model performance measures adjusted for predictor distributions using density ratio method (Sugiyama et al., (2012, ISBN:9781139035613)). L1 and L2 error for continuous outcome and C-statistics for binomial outcome are computed. |
License: | GPL-2 |
Index of help topics:
APPEstimation-package R function to calculate model performance measure adjusted for predictor distributions. appe.glm C-statistics adjusted for predictor distributions appe.lm L_1 and L_2 errors adjusted for predictor distributions cvalest.bin Estimation of C-statistics densratio.appe A wrapper function
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
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)
[Package APPEstimation version 0.1.1 Index]