In PingYangChen/milr: Multiple-Instance Logistic Regression with LASSO Penalty

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

Status

 License       |  Linux/osx Build  |   Windows Build   |

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milr

The multiple-instance logistic regression with lasso penalty.

Installation

You can install:

install from CRAN

install.packages("milr")

the latest development version from github with

install.packages("devtools")
devtools::install_github("PingYangChen/milr")

If you encounter a bug, please file a reproducible example on github.

examples

set.seed(100)
beta <- runif(5, -5, 5)
trainData <- DGP(70, 3, beta)
testData <- DGP(30, 3, beta)
# default (not use LASSO)
milr_result <- milr(trainData$Z, trainData$X, trainData$ID)
coef(milr_result)      # coefficients
fitted(milr_result)                    # fitted bag labels
fitted(milr_result, type = "instance") # fitted instance labels
summary(milr_result)   # summary milr
predict(milr_result, testData$X, testData$ID)                    # predicted bag labels
predict(milr_result, testData$X, testData$ID, type = "instance") # predicted instance labels