| Type: | Package |
| Title: | Multiple-Instance Logistic Regression with LASSO Penalty |
| Version: | 0.4.1 |
| Date: | 2025-09-18 |
| Description: | 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. |
| Author: | Ping-Yang Chen [aut, cre], ChingChuan Chen [aut], Chun-Hao Yang [aut], Sheng-Mao Chang [aut] |
| URL: | https://github.com/PingYangChen/milr |
| BugReports: | https://github.com/PingYangChen/milr/issues |
| Maintainer: | Ping-Yang Chen <pychen.ping@gmail.com> |
| Depends: | R (≥ 3.2.3) |
| Imports: | utils, pipeR (≥ 0.5), numDeriv, glmnet, Rcpp (≥ 0.12.0),RcppParallel |
| LinkingTo: | Rcpp, RcppArmadillo, RcppParallel |
| Suggests: | testthat, knitr, Hmisc, rmarkdown, data.table, ggplot2, plyr |
| SystemRequirements: | GNU make |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2025-09-19 02:32:28 UTC; user |
| Repository: | CRAN |
| Date/Publication: | 2025-09-19 02:50:02 UTC |
The milr package: multiple-instance logistic regression with lasso penalty
Description
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 multinomial, and, we can only observe the subject-level outcomes. For example, in manufactory 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.
Author(s)
Maintainer: Ping-Yang Chenpychen.ping@gmail.com
Authors:
ChingChuan Chencelestial0230@stat.sinica.edu.tw
Chun-Hao Yangchunhaoyang@ufl.edu
Sheng-Mao Changsmchang@mail.ncku.edu.tw
References
Chen, R.-B., Cheng, K.-H., Chang, S.-M., Jeng, S.-L., Chen, P.-Y., Yang, C.-H., and Hsia, C.-C. (2016). Multiple-Instance Logistic Regression with LASSO Penalty. arXiv:1607.03615 [stat.ML].
See Also
Useful links:
DGP: data generation
Description
Generating the multiple-instance data set.
Usage
DGP(n, m, beta)Arguments
n | an integer. The number of bags. |
m | an integer or vector of length |
beta | a vector. The true regression coefficients. |
Value
a list including (1) bag-level labels,Z, (2) the design matrix,X, and (3) bag ID of each instance,ID.
Examples
data1 <- DGP(50, 3, runif(10, -5, 5))data2 <- DGP(50, sample(3:5, 50, TRUE), runif(10, -5, 5))Fitted Response of milr Fits
Description
Fitted Response of milr Fits
Usage
## S3 method for class 'milr'fitted(object, type = "bag", ...)Arguments
object | A fitted obejct of class inheriting from |
type | The type of fitted response required. Default is |
... | further arguments passed to or from other methods. |
Fitted Response of softmax Fits
Description
Fitted Response of softmax Fits
Usage
## S3 method for class 'softmax'fitted(object, type = "bag", ...)Arguments
object | A fitted obejct of class inheriting from |
type | The type of fitted response required. Default is |
... | further arguments passed to or from other methods. |
logit link function
Description
calculate the values of logit link
Usage
logit(X, beta)Arguments
X | A matrix, the design matrix. |
beta | A vector, the coefficients. |
Value
An vector of the values of logit link.
Maximum likelihood estimation of multiple-instance logistic regression with LASSO penalty
Description
Please refer tomilr-package.
Usage
milr( y, x, bag, lambda = 0, numLambda = 20L, lambdaCriterion = "BIC", nfold = 10L, maxit = 500L)Arguments
y | a vector. Bag-level binary labels. |
x | the design matrix. The number of rows of |
bag | a vector, bag id. |
lambda | the tuning parameter for LASSO-penalty. If |
numLambda | An integer, the maximum length of LASSO-penalty. in atuo-tunning mode ( |
lambdaCriterion | a string, the used optimality criterion for tuning the |
nfold | an integer, the number of fold for cross-validation to choose the optimal |
maxit | an integer, the maximum iteration for the EM algorithm. The default is 500. |
Value
An object with S3 class "milr".
- lambda
a vector of candidate lambda values.
- cv
a vector of predictive deviance via
nfold-fold cross validationwhenlambdaCriterion = "deviance".- deviance
a vector of deviance of candidate model for each candidate lambda value.
- BIC
a vector of BIC of candidate model for each candidate lambda value.
- best_index
an integer, indicates the index of the best model among candidate lambda values.
- best_model
a list of the information for the best model including deviance (not cv deviance), BIC, chosen lambda, coefficients, fitted values, log-likelihood and variances of coefficients.
Examples
set.seed(100)beta <- runif(5, -5, 5)trainData <- DGP(40, 3, beta)testData <- DGP(5, 3, beta)# default (not use LASSO)milr_result <- milr(trainData$Z, trainData$X, trainData$ID)coef(milr_result) # coefficientsfitted(milr_result) # fitted bag labelsfitted(milr_result, type = "instance") # fitted instance labelssummary(milr_result) # summary milrpredict(milr_result, testData$X, testData$ID) # predicted bag labelspredict(milr_result, testData$X, testData$ID, type = "instance") # predicted instance labels# use BIC to choose penalty (not run)#milr_result <- milr(trainData$Z, trainData$X, trainData$ID,# exp(seq(log(0.01), log(50), length = 30)))#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# use auto-tuning (not run)#milr_result <- milr(trainData$Z, trainData$X, trainData$ID, lambda = -1, numLambda = 20)#coef(milr_result) # coefficients#fitted(milr_result) # fitted bag labels#fitted(milr_result, type = "instance") # fitted instance labels#summary(milr_result) # summary milr#predict(milr_result, testData$X, testData$ID) # predicted bag labels#predict(milr_result, testData$X, testData$ID, type = "instance") # predicted instance labels# use cv in auto-tuning (not run)#milr_result <- milr(trainData$Z, trainData$X, trainData$ID, # lambda = -1, numLambda = 20, lambdaCriterion = "deviance")#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 labelsPredict Method for milr Fits
Description
Predict Method for milr Fits
Usage
## S3 method for class 'milr'predict(object, newdata = NULL, bag_newdata = NULL, type = "bag", ...)Arguments
object | A fitted obejct of class inheriting from |
newdata | Default is |
bag_newdata | Default is |
type | The type of prediction required. Default is |
... | further arguments passed to or from other methods. |
Predict Method for softmax Fits
Description
Predict Method for softmax Fits
Usage
## S3 method for class 'softmax'predict(object, newdata = NULL, bag_newdata = NULL, type = "bag", ...)Arguments
object | A fitted obejct of class inheriting from |
newdata | Default is |
bag_newdata | Default is |
type | The type of prediction required. Default is |
... | further arguments passed to or from other methods. |
Multiple-instance logistic regression via softmax function
Description
This function calculates the alternative maximum likelihood estimation for multiple-instance logistic regressionthrough a softmax function (Xu and Frank, 2004; Ray and Craven, 2005).
Usage
softmax(y, x, bag, alpha = 0, ...)Arguments
y | a vector. Bag-level binary labels. |
x | the design matrix. The number of rows of |
bag | a vector, bag id. |
alpha | A non-negative realnumber, the softmax parameter. |
... | arguments to be passed to the |
Value
a list including coefficients and fitted values.
References
S. Ray, and M. Craven. (2005) Supervised versus multiple instance learning: An empirical comparsion. in Proceedings of the 22nd International Conference on Machine Learnings, ACM, 697–704.
X. Xu, and E. Frank. (2004) Logistic regression and boosting for labeled bags of instances. in Advances in Knowledge Discovery and Data Mining, Springer, 272–281.
Examples
set.seed(100)beta <- runif(10, -5, 5)trainData <- DGP(40, 3, beta)testData <- DGP(5, 3, beta)# Fit softmax-MILR model S(0)softmax_result <- softmax(trainData$Z, trainData$X, trainData$ID, alpha = 0)coef(softmax_result) # coefficientsfitted(softmax_result) # fitted bag labelsfitted(softmax_result, type = "instance") # fitted instance labelspredict(softmax_result, testData$X, testData$ID) # predicted bag labelspredict(softmax_result, testData$X, testData$ID, type = "instance") # predicted instance labels# Fit softmax-MILR model S(3) (not run)# softmax_result <- softmax(trainData$Z, trainData$X, trainData$ID, alpha = 3)