Bioconductor version: 2.7
A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now.
Author: Nusrat Rabbee <nrabbee at post.harvard.edu>, Gary Wong <wongg62 at berkeley.edu>
Maintainer: Nusrat Rabbee <nrabbee at post.harvard.edu>
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R") biocLite("RLMM")
To cite this package in a publication, start R and enter:
citation("RLMM")
R Script | RLMM Doc | |
Reference Manual |
biocViews | Microarray, OneChannel, SNP, GeneticVariability |
Depends | R (>= 2.1.0) |
Imports | graphics, grDevices, MASS, stats, utils |
Suggests | |
System Requirements | Internal files Xba.CQV, Xba.regions (or other regions file) |
License | LGPL (>= 2) |
URL | http://www.stat.berkeley.edu/users/nrabbee/RLMM |
Depends On Me | |
Imports Me | |
Suggests Me | |
Version | 1.12.0 |
Since | Bioconductor 1.8 (R-2.3) |
Package Source | RLMM_1.12.0.tar.gz |
Windows Binary | RLMM_1.12.0.zip (32- & 64-bit) |
MacOS 10.5 (Leopard) binary | RLMM_1.12.0.tgz |
Package Downloads Report | Download Stats |
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