hlaPublish {HIBAG} | R Documentation |
Finalize a HIBAG model by removing unused SNP predictors and adding appendix information (platform, training set, authors, warning, etc)
hlaPublish(mobj, platform=NULL, information=NULL, warning=NULL, rm.unused.snp=TRUE, anonymize=TRUE, verbose=TRUE)
mobj |
an object of |
platform |
the text of platform information |
information |
the other information, like authors |
warning |
any warning message |
rm.unused.snp |
if |
anonymize |
if |
verbose |
if TRUE, show information |
Returns a new object of hlaAttrBagObj
.
Xiuwen Zheng
hlaModelFromObj
, hlaModelToObj
# make a "hlaAlleleClass" object hla.id <- "A" hla <- hlaAllele(HLA_Type_Table$sample.id, H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")], H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")], locus=hla.id, assembly="hg19") # training genotypes region <- 250 # kb snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19") train.geno <- hlaGenoSubset(HapMap_CEU_Geno, snp.sel = match(snpid, HapMap_CEU_Geno$snp.id), samp.sel = match(hla$value$sample.id, HapMap_CEU_Geno$sample.id)) # # train a HIBAG model # set.seed(1000) # please use "nclassifier=100" when you use HIBAG for real data model <- hlaAttrBagging(hla, train.geno, nclassifier=2, verbose.detail=TRUE) summary(model) length(model$snp.id) mobj <- hlaPublish(model, platform = "Illumina 1M Duo", information = "Training set -- HapMap Phase II") model2 <- hlaModelFromObj(mobj) length(mobj$snp.id) mobj$appendix summary(mobj) p1 <- hlaPredict(model, train.geno) p2 <- hlaPredict(model2, train.geno) # check cbind(p1$value, p2$value)