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#$Author: sinnwell $
#$Date: 2008/02/11 22:54:22 $
#$Header: /people/biostat3/sinnwell/Haplo/Make/RCS/haplo.em.q,v 1.17 2008/02/11 22:54:22 sinnwell Exp $
#$Locker: $
#$Log: haplo.em.q,v $
#Revision 1.17 2008/02/11 22:54:22 sinnwell
#include subjects removed by PIN steps (low-LD, rare haps) in rows.rem and issue a warning to reduce min.posterior
#
#Revision 1.16 2007/10/16 19:07:12 sinnwell
#remove intMax check and go with max.haps.limit of 2e6 from control
#intMax was the highest index for integers, but too much memory to allocate in C
#
#Revision 1.15 2007/04/03 21:08:36 sinnwell
#add PACKAGE in checkIntMax .C call
#
#Revision 1.14 2007/02/27 20:16:21 schaid
# control max.haps.limit with checkIntMax (see haplo_em_pin)
#
#Revision 1.13 2004/07/09 14:36:22 sinnwell
#warning for n.loci < 2
#
#Revision 1.12 2004/03/18 23:31:28 sinnwell
#keep haplotype matrix from data.frame, char vecs from factors
#
#Revision 1.11 2004/03/01 20:52:37 sinnwell
#change T to TRUE for matrix()
#
#Revision 1.10 2004/02/02 23:00:01 sinnwell
#insert ghost runif(1) line to init .Random.seed for R bug
#
#Revision 1.9 2003/09/19 21:38:45 schaid
#fixed returned class to be R compatible
#
#Revision 1.8 2003/08/26 22:08:31 sinnwell
#add GPL License
#
#Revision 1.7 2003/08/26 21:04:49 schaid
#Major revision of haplo.em, by adding new functions and new C code to use progressive insertion of loci.
#
# License:
#
# Copyright 2003 Mayo Foundation for Medical Education and Research.
#
# This program is free software; you can redistribute it and/or modify it under the terms of
# the GNU General Public License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT ANY
# WARRANTY; without even the implied warranty of MERCHANTABILITY or
# FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for
# more details.
#
# You should have received a copy of the GNU General Public License along with this
# program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330,
# Boston, MA 02111-1307 USA
#
# For other licensing arrangements, please contact Daniel J. Schaid.
#
# Daniel J. Schaid, Ph.D.
# Division of Biostatistics
# Harwick Building Room 775
# Mayo Clinic
# 200 First St., SW
# Rochester, MN 55905
#
# phone: 507-284-0639
# fax: 507-284-9542
# email: schaid@mayo.edu
#
haplo.em <- function(geno, locus.label=NA, miss.val=c(0,NA), weight=NULL,
control = haplo.em.control() ){
n.loci <- ncol(geno)/2
n.subject <- nrow(geno)
subj.id <- 1:n.subject
if(n.loci<2) stop("Must have at least 2 loci for haplotype estimation!")
# set up weight
if(any(is.null(weight))){
weight <- rep(1,n.subject)
}
if(any(weight<0)){
stop("negative weights not allowed")
}
if(length(weight)!=n.subject){
stop("Length of weight != number of subjects (nrow of geno)")
}
# Create locus label if not included
if(all(is.na(locus.label))) locus.label<- paste("loc-",1:n.loci,sep="")
if(length(locus.label)!=n.loci){
stop("length of locus.label != n.loci")
}
# recode geno to integer values, accounting for missing values
temp.geno <- loci(geno,locus.names=locus.label,miss.val=miss.val)
# Compute the max number of pairs of haplotypes over all subjects
max.pairs <- geno.count.pairs(temp.geno)
max.haps <- 2*sum(max.pairs)
## This system-max for integer values is used in haplo.em.control
## for setting max.haps.limit
#intMax <- .C("checkIntMax",
# intMax = as.integer(0),
# PACKAGE="haplo.stats")$intMax
if(max.haps > control$max.haps.limit) max.haps <- control$max.haps.limit
# check whether to delete some rows - now defunct, but use
# dummy to not break code that uses this in returned list
rows.rem <- numeric(0)
geno.vec <- as.vector(temp.geno)
geno.vec <- ifelse(is.na(geno.vec),0,geno.vec)
allele.labels <- attr(temp.geno, "unique.alleles")
if(length(allele.labels)!=n.loci)
stop("Number of loci in alleles list != n.loci")
n.alleles <- numeric(n.loci)
a.freq <- vector("list",n.loci)
for(i in 1:n.loci){
n.alleles[i] <- length(allele.labels[[i]])
j <- (i-1)*2 + 1
p <- table(temp.geno[,c(j, (j+1))], exclude=NA)
p <- p/sum(p)
a.freq[[i]] <- list(p=p)
}
if(is.null(control$loci.insert.order)) {
control$loci.insert.order <- 1:n.loci
}
# need zero-offset for loci-insert-order when
# pass to C
loci.insert.order <- (control$loci.insert.order - 1)
if(length(loci.insert.order) != n.loci){
stop("length of loci.insert.order != n.loci")
}
if(sum( abs(sort(loci.insert.order) - (0:(n.loci-1)))) > 0){
stop("All loci are not accounted for in loci.insert.order")
}
if(control$insert.batch.size > n.loci){
control$insert.batch.size <- n.loci
}
if(!is.null(control$iseed)) {
set.seed(control$iseed) } else
{ runif(1)
control$iseed <- .Random.seed
}
# The seeds for the ranAS183 random number generator used in the C function
# hwe_sim must be between 1 and 30000, but bigger is better (we think), so we
# add 10000
seed.array <- runif(3)
iseed1 = 10000 + 20000*seed.array[1]
iseed2 = 10000 + 20000*seed.array[2]
iseed3 = 10000 + 20000*seed.array[3]
fit <- haplo.em.fitter(
n.loci,
n.subject,
weight,
geno.vec,
n.alleles,
max.haps,
max.iter=control$max.iter,
loci.insert.order,
min.posterior=control$min.posterior,
tol=control$tol,
insert.batch.size=control$insert.batch.size,
random.start=control$random.start,
iseed1=iseed1,
iseed2=iseed2,
iseed3=iseed3,
verbose=control$verbose)
# if n.try > 1 try, remaining tries are random starts for posteriors
if(control$n.try > 1){
for(i in 2:control$n.try){
seed.array <- runif(3)
iseed1 = 10000 + 20000*seed.array[1]
iseed2 = 10000 + 20000*seed.array[2]
iseed3 = 10000 + 20000*seed.array[3]
fit.new <- haplo.em.fitter(
n.loci,
n.subject,
weight,
geno.vec,
n.alleles,
max.haps,
max.iter=control$max.iter,
loci.insert.order,
min.posterior=control$min.posterior,
tol=control$tol,
insert.batch.size=control$insert.batch.size,
random.start=1,
iseed1=iseed1,
iseed2=iseed2,
iseed3=iseed3,
verbose=control$verbose)
if(fit.new$tmp1$lnlike > fit$tmp1$lnlike)
{ fit <- fit.new
}
}
}
tmp1 <- fit$tmp1
tmp2 <- fit$tmp2
u.hap <- matrix(tmp2$u.hap,nrow=tmp2$n.u.hap,byrow=TRUE)
# code alleles for haplotpes with original labels
# use I() to keep char vectors to factors in making a data.frame
haplotype <- data.frame(I(allele.labels[[1]][u.hap[,1]]))
for(j in 2:n.loci){
haplotype <- cbind(haplotype, I(allele.labels[[j]][u.hap[,j]]))
}
# haplotype <- data.frame(haplotype)
names(haplotype) <- locus.label
# convert from 0-offset in C to 1-offset in S, and recode hap codes
# to 1,2,..., n_uhap
hap1code <- tmp2$hap1code + 1
hap2code <- tmp2$hap2code + 1
uhapcode <- tmp2$u.hap.code + 1
n1 <- length(uhapcode)
n2 <- length(hap1code)
tmp <- as.numeric(factor(c(uhapcode, hap1code, hap2code)))
uhapcode <- tmp[1:n1]
hap1code <- tmp[(n1+1):(n1+n2)]
hap2code <- tmp[(n1+n2+1):(n1+2*n2)]
uhap.df <- data.frame(uhapcode, tmp2$hap.prob, u.hap)
names(uhap.df) <- c("hap.code","hap.prob",locus.label)
indx.subj = tmp2$indx.subj + 1
# in rare cases of very low LD, a subject gets removed by the trimming
# of haplotypes add warning and offer options in this case
if(length(unique(tmp2$indx.subj)) < n.subject) {
unique.subj <- unique(indx.subj)
rows.rem <- c(rows.rem, which(is.na(match(1:n.subject, unique.subj))))
warning("Subject(s) ", paste(rows.rem,sep=','), " removed in trimming steps.\n Try decreasing min.posterior control parameter to reduce trimming.\n")
}
subj.used.id <- subj.id[indx.subj]
# compute lnlike if no LD. This is a rough approximation which will
# be accurate only if all haplotypes are considered in the list
# of enumerated haplotypes. If there is no trimming
# (min.posterior = 0), and it is possible to enumerate all
# possible pairs of haplotypes, then hap.prob.noLD will sum
# to 1. But, with trimming, this may not occur, so we
# rescale to force them to sum to 1. This may not lead to
# an accurate test for no LD by the likelihood ratio statistic.
hap.prob.noLD <- a.freq[[1]]$p[u.hap[,1]]
df.noLD <- length(a.freq[[1]]$p) - 1
for(j in 2:n.loci){
hap.prob.noLD <- hap.prob.noLD * a.freq[[j]]$p[u.hap[,j]]
df.noLD <- df.noLD + length(a.freq[[j]]$p) - 1
}
hap.prob.noLD <- hap.prob.noLD/sum(hap.prob.noLD)
prior.noLD <- hap.prob.noLD[hap1code]*hap.prob.noLD[hap2code]
prior.noLD <- ifelse(hap1code!=hap2code, 2*prior.noLD, prior.noLD)
ppheno.noLD <- tapply(prior.noLD, indx.subj, sum)
lnlike.noLD <- sum(log(ppheno.noLD))
lr = 2*(tmp1$lnlike - lnlike.noLD)
df.LD <- sum(tmp2$hap.prob > 0.0000001) - 1
df.lr <- df.LD - df.noLD
obj <- list(
lnlike=tmp1$lnlike,
lr = lr,
df.lr = df.lr,
hap.prob = tmp2$hap.prob,
hap.prob.noLD = hap.prob.noLD,
converge = tmp1$converge,
locus.label = locus.label,
indx.subj = indx.subj,
subj.id = subj.used.id,
post = tmp2$post,
hap1code = hap1code,
hap2code = hap2code,
haplotype = haplotype,
nreps = table(indx.subj),
rows.rem = rows.rem,
max.pairs=max.pairs,
control=control)
if(exists("is.R") && is.function(is.R) && is.R()) {
class(obj) <- "haplo.em"
} else {
oldClass(obj) <- "haplo.em"
}
return(obj)
}
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