Splits the data file named file.name in dir.file, into TRAIN and TEST files, based on the percentage train.percent - how many percent of the data should go into TRAIN file.
● Data Source:
CranContrib
● Keywords:
● Alias: pre8.split.train.test
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For each .ped file in dir.ped, categorizes genotype data into 3 levels, 1, 2, 3. Genos with two different Alleles are encoded as "2". Other genotypes are encoded as "1" or "3", where most frequent geno is "1". No missing values allowed, must be done after imputation. Geno values should use letters A, T, C, G if letter.encoding=TRUE.
● Data Source:
CranContrib
● Keywords:
● Alias: pre5.genos2numeric.batch
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For each pair of CASE and CONTROL files, combine them into one file. Last column of each output file will contain the disease status. The disease status is encoded as 1 for CASE and 0 for CONTROL.
● Data Source:
CranContrib
● Keywords:
● Alias: pre4.combine.case.control.batch
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Calls MACH1 program on file.ped and file.dat. MaCH1 can be run in 2 different ways: 1. with Hapmap, and 2. without Hapmap. NOTE: In this implementation, do NOT run "with Hapmap".
● Data Source:
CranContrib
● Keywords:
● Alias: pre3.call.mach
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The genMOSS package together with datafile preprocessing functions.
● Data Source:
CranContrib
● Keywords: htest, models
● Alias: genMOSSplus, genMOSSplus-package
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MOSS.GWAS
(Package: genMOSSplus) :
A function implementing the MOSS algorithm for the analysis of GWAS data.
The MOSS algorithm is a Bayesian variable selection procedure that can be used for the analsysis GWAS data. It identifies combinations of the best predictive SNPs associated with the response. It also performs a hierarchical log-linear model search to identify the most relevant associations among the resulting subsets of SNPs. The function has an option to use model averaging to construct a classifier for predicting the response and to assess its capability via k-fold cross validation. The prior used is the generalized hyper Dirichlet.
● Data Source:
CranContrib
● Keywords: htest, models
● Alias: MOSS.GWAS
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Function to help create the recommended subdirectory structure for the pre-processing. In dir.out a directory with name out.name will be created. Inside of this out.name directory will be a set of subdirectories, whose names will begin with prefix.dir, followed by a number, followed by short description of what the folder is designed to contain.
● Data Source:
CranContrib
● Keywords:
● Alias: pre0.dir.create
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Remove columns (genos) that have too many missing values. All genos that have more than perc.snp values missing in both case.ped AND control.ped files will be removed.
● Data Source:
CranContrib
● Keywords:
● Alias: pre2.remove.genos
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Same thing as pre5.genos2numeric, only leaves genotypes the way they are, without categorizing them into 3 levels. Removes all SNPs that have missing or bad values. Intended to be done after imputation, to ensure consistency. Geno values should use letters A, T, C, G if letter.encoding=TRUE.
● Data Source:
CranContrib
● Keywords:
● Alias: genos.clean
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Obtains the number of rows and columns in a matrix that is stored in a text file. The entries in the file should be either space or tab delimited. No missing values.
● Data Source:
CranContrib
● Keywords: misc
● Alias: get.data.dims
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providing 
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