Llarrma

LLARRMA R-package main page

This webpage describes the LLARRMA in its original version. An extended and updated version of LLARRMA has been incorporated into the newer package FReMA.

Description

LASSO local automatic regularization resample model averaging (LLARRMA), combines LASSO shrinkage with resample model averaging and multiple imputation, estimating for each SNP the probability that it would be included in a multi-SNP model in alternative subsamples of the data.

For constructive comments and suggestions email the authors
William Valdar and Jeremy Sabourin.

Contents

  1. Updates
  2. Some current limitations
  3. Requirements and installation
  4. Input file
  5. To do list
  6. References

Updates

  • 2012-12-14 Web fix for download link.
  • 2012-06-8 LLARRMA v1.01 uploaded – fixed issue when using binomial phenotypes not coded as {0,1}
  • 2012-05-13 LLARRMA v1.0 uploaded

Some current limitations

LLARRMA is limited to linear and logistic regression models, and currently does not feature the ability include unpenalized covariates. If you have know covariates, you may first regress out their effects and run LLARRMA on the residuals. We do not recommend added covariates as a marker predictor and allow it to be penalized as their scale may effect the local lambda selection in a manner that makes the marker RMIPs non-informative.

LLARRMA currently requires package “glmnet” for fitting lasso based regression paths, which is not monitored by anyone on the LLARRMA project. If changes to this package are made, issues may arise in LLARRMA. If such an issue does arise, please email William Valdar and we will do our best to fix the issue within our code to account for their changes.

Requirements and Installation

Installing R package LLARRMA

The R package R/llarrma currently requires R packages “glmnet”, “matrix”, and “lattice”,
as well as the R software.

Installation is supported through R CMD INSTALL terminal interface outside of the RGui.
Please attempt to install “R/llarrma” using the directions bellow,
using Unix/Mac Terminal or Windows Command Prompt through R CMD INSTALL method
before contacting Valdarlab with
issues regarding installation problems for unsupported methods.

Terminal installation directions follow

  1. Download the latest version of LLARRMA from here: llarrma_1.01.tar.gz.
  2. Unix (and all users with full R-install capabilities RTools/RFortran installed for Windows/Mac machines)
    users Open a UNIX shell and go to the directory containing the tar.gz file
  3. Type R CMD INSTALL --clean llarrma_XXX.tar.gz
    for XXX current package version
  • Installation through RApp Gui is unsupported.

Input files

More details for the .ped file format to be added soon. see package documentation for more details currently.

Command line options

R/llarrma cannot be run from the command line.

To do list

Suggestions to William Valdar and Jeremy Sabourin. Here is the current and incomplete list (in no particular order):

  • unpenalized covariates within llarrma.

References

Valdar W*, Sabourin J*, Nobel A, Holmes C (2012) Reprioritizing genetic associations in hit regions using LASSO-based resample model averaging. Genetic Epidemiology 36(5):451-462 PMID:22549815