Package: MTLR 0.2.1

Humza Haider

MTLR: Survival Prediction with Multi-Task Logistic Regression

An implementation of Multi-Task Logistic Regression (MTLR) for R. This package is based on the method proposed by Yu et al. (2011) which utilized MTLR for generating individual survival curves by learning feature weights which vary across time. This model was further extended to account for left and interval censored data.

Authors:Humza Haider

MTLR_0.2.1.tar.gz
MTLR_0.2.1.tar.gz(r-4.5-noble)MTLR_0.2.1.tar.gz(r-4.4-noble)
MTLR_0.2.1.tgz(r-4.4-emscripten)MTLR_0.2.1.tgz(r-4.3-emscripten)
MTLR.pdf |MTLR.html✨
MTLR/json (API)
NEWS

# Install 'MTLR' in R:
install.packages('MTLR', repos = 'https://cloud.r-project.org')

Bug tracker:https://github.com/haiderstats/mtlr/issues1 issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

openblascpp

3.48 score 1 packages 197 downloads 2 mentions 4 exports 5 dependencies

Last updated 6 years agofrom:bf6078aa87. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 25 2025
R-4.5-linux-x86_64OKMar 25 2025
R-4.4-linux-x86_64OKMar 25 2025

Exports:create_foldsmtlrmtlr_cvplotcurves

Dependencies:latticeMatrixRcppRcppArmadillosurvival

Introduction to the MTLR Workflow

Humza Haider (hshaider@ualberta.ca)

Rendered fromworkflow.Rmdusingknitr::rmarkdownon Mar 25 2025.

Last update: 2018-12-18
Started: 2018-12-18

Citation

To cite package ‘MTLR’ in publications use:

Haider H (2019). MTLR: Survival Prediction with Multi-Task Logistic Regression. R package version 0.2.1, https://CRAN.R-project.org/package=MTLR.

ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.

Corresponding BibTeX entry: 

  @Manual{,
    title = {MTLR: Survival Prediction with Multi-Task Logistic
      Regression},
    author = {Humza Haider},
    year = {2019},
    note = {R package version 0.2.1},
    url = {https://CRAN.R-project.org/package=MTLR},
  }

Readme and manuals

MTLR

The goal of MTLR is to provide an R implementation for Multi-Task Logistic Regression. In addition to supplying the model provided by Yu et al. we have extended the model for left censoring, interval censoring, and a mixture of censoring types. Functionality includes training an MTLR model, predicting survival curves for new observations, and plotting these survival curves and feature weights estimated by MTLR.

Installation

You can install the version from CRAN or the development version from GitHub:

# CRAN:
install.packages("MTLR")

# GitHub:
# install.packages("devtools")
devtools::install_github("haiderstats/MTLR")

Example

Given a survival dataset containing event time and event status indicator (censored/uncensored) we can produce an MTLR model. For example, consider the lung dataset from the survival package:

# Load survival for the lung dataset and the Surv() function.
library(survival)
#Here we will use 9 intervals (10 time points) just for plotting purposes. 
#The default is sqrt the number of observations.
mod <- mtlr(Surv(time,status)~., data = lung, nintervals = 9)
print(mod)
#> 
#> Call:  mtlr(formula = Surv(time, status) ~ ., data = lung, nintervals = 9) 
#> 
#> Time points:
#>  [1]  62.3 145.4 179.3 210.4 241.4 284.5 308.2 383.5 476.3 642.8
#> 
#> 
#> Weights:
#>           Bias      inst      age      sex ph.ecog ph.karno pat.karno meal.cal  wt.loss
#> 62.27   0.1147 -0.017981  0.04891 -0.01249 0.00461 -0.00648   0.01352 -0.02617 -0.01325
#> 145.36  0.1362 -0.021147  0.03275 -0.00473 0.02312 -0.02227  -0.01753 -0.01105 -0.02974
#> 179.27  0.2119 -0.008203  0.02260 -0.02564 0.02394 -0.02046  -0.03161 -0.02310 -0.02218
#> 210.36  0.0398  0.000359  0.00816 -0.03638 0.04704 -0.02230  -0.04129 -0.01410 -0.03367
#> 241.36 -0.0996  0.009570 -0.01581 -0.04405 0.06690 -0.04139  -0.05453 -0.00808 -0.01288
#> 284.55 -0.2299  0.004869 -0.00476 -0.05180 0.04824 -0.02107  -0.03135  0.00237 -0.02552
#> 308.18 -0.3012 -0.007743 -0.00467 -0.05426 0.04908 -0.02280  -0.03264 -0.01608  0.02130
#> 383.45 -0.0289 -0.019985 -0.01030 -0.03263 0.02868 -0.00680  -0.02321 -0.01458 -0.00235
#> 476.27 -0.1285 -0.010232  0.00106 -0.02226 0.01715  0.01699  -0.01555 -0.02112 -0.01587
#> 642.82 -0.3513 -0.014975  0.02291 -0.02548 0.01847  0.00556  -0.00856  0.00373 -0.00552
#Plot feature weights:
plot(mod)

#Get survival curves for the lung dataset:
curves <- predict(mod)
#Plot the first 20 survival curves:
plotcurves(curves, 1:20)