Package: milr 0.3.1
milr: Multiple-Instance Logistic Regression with LASSO Penalty
The multiple instance data set consists of many independent subjects (called bags) and each subject is composed of several components (called instances). The outcomes of such data set are binary or categorical responses, and, we can only observe the subject-level outcomes. For example, in manufacturing processes, a subject is labeled as "defective" if at least one of its own components is defective, and otherwise, is labeled as "non-defective". The 'milr' package focuses on the predictive model for the multiple instance data set with binary outcomes and performs the maximum likelihood estimation with the Expectation-Maximization algorithm under the framework of logistic regression. Moreover, the LASSO penalty is attached to the likelihood function for simultaneous parameter estimation and variable selection.
Authors:
milr_0.3.1.tar.gz
milr_0.3.1.tar.gz(r-4.5-noble)milr_0.3.1.tar.gz(r-4.4-noble)
milr_0.3.1.tgz(r-4.4-emscripten)milr_0.3.1.tgz(r-4.3-emscripten)
milr.pdf |milr.html✨
milr/json (API)
# Install 'milr' in R: |
install.packages('milr', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/pingyangchen/milr/issues
Last updated 4 years agofrom:55e4ab3cfa. Checks:OK: 1 NOTE: 1. Indexed: no.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Dec 02 2024 |
R-4.5-linux-x86_64 | NOTE | Dec 02 2024 |
Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixnumDerivpipeRRcppRcppArmadilloRcppEigenRcppParallelshapesurvival
Readme and manuals
Help Manual
Help page | Topics |
---|---|
The milr package: multiple-instance logistic regression with lasso penalty | milr-package |
DGP: data generation | DGP |
Fitted Response of milr Fits | fitted.milr |
Fitted Response of softmax Fits | fitted.softmax |
logit link function | logit |
Maximum likelihood estimation of multiple-instance logistic regression with LASSO penalty | milr |
Predict Method for milr Fits | predict.milr |
Predict Method for softmax Fits | predict.softmax |
Multiple-instance logistic regression via softmax function | softmax |