Nothing
R/effectplot.R
In qtl: Tools for Analyzing QTL Experiments
Defines functions
effectplot.calmeanse
######################################################################
#
# effectplot.R
#
# copyright (c) 2002-2019, Hao Wu and Karl W. Broman
# Last modified Dec, 2019
# first written Jul, 2002
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License,
# version 3, as published by the Free Software Foundation.
#
# 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, version 3, for more details.
#
# A copy of the GNU General Public License, version 3, is available
# at http://www.r-project.org/Licenses/GPL-3
#
# Modified by Hao Wu Feb 2005 for the following:
# 1. function will take marker, pseudomarker or phenotype as input;
# 2. separate functions to extract marker genodata given marker names
# and calculate means and ses;
#
# Part of the R/qtl package
# Contains: effectplot, effectplot.getmark, effectplot.calmeanse
#
######################################################################
effectplot <-
function (cross, pheno.col = 1, mname1, mark1, geno1, mname2,
mark2, geno2, main, ylim, xlab, ylab, col, add.legend = TRUE,
legend.lab, draw=TRUE, var.flag=c("pooled","group"))
{
if(!inherits(cross, "cross"))
stop("The first input variable must be an object of class cross")
if(LikePheVector(pheno.col, nind(cross), nphe(cross))) {
cross$pheno <- cbind(pheno.col, cross$pheno)
pheno.col <- 1
}
# if mname2 is given but not mname1, switch it around
if((missing(mname1) && missing(mark1) && missing(geno1)) &&
any(!missing(mname2) || missing(mark2) || missing(geno2))) {
if(!missing(mname2)) { mname1 <- mname2; mname2 <- NULL }
if(!missing(mark2)) { mark1 <- mark2; mark2 <- NULL }
if(!missing(geno2)) { geno1 <- geno2; geno2 <- NULL }
}
if(missing(mark2)) mark2 <- NULL
if(missing(mname2)) mname2 <- NULL
if(length(pheno.col) > 1) {
pheno.col <- pheno.col[1]
warning("effectplot can take just one phenotype; only the first will be used")
}
if(is.character(pheno.col)) {
num <- find.pheno(cross, pheno.col)
if(is.na(num))
stop("Couldn't identify phenotype \"", pheno.col, "\"")
pheno.col <- num
}
if(pheno.col < 1 | pheno.col > nphe(cross))
stop("pheno.col values should be between 1 and the no. phenotypes")
if(!is.numeric(cross$pheno[,pheno.col]))
stop("phenotype \"", colnames(cross$pheno)[pheno.col], "\" is not numeric.")
var.flag <- match.arg(var.flag)
# local variables
n.ind <- nind(cross)
pheno <- cross$pheno[, pheno.col]
type <- crosstype(cross)
chr_type1 <- chr_type2 <- "A"
gennames1 <- gennames2 <- NULL
# If imputations are not available, create them
if(!("draws" %in% names(cross$geno[[1]]))) {
warning(" -Running sim.geno.")
cross <- sim.geno(cross, n.draws=16)
}
####################################################
# get genotype data for markers given marker name
# if marker genodata were given, this will be skipped
####################################################
# used for alternative print name for pseudomarkers
pm.pattern <- "^c.*\\.loc.*$" # pseudomarker names will be like "c1.loc10"
dig <- 1
step <- attr(cross$geno[[1]]$draws, "step")
if(!is.null(step)) {
if(step > 0) dig <- max(dig, -floor(log10(step)))
}
else {
stepw <- attr(cross$geno[[1]]$draws, "stepwidth")
if(!is.null(stepw) && stepw > 0) dig <- max(dig, -floor(log10(stepw)))
}
printname1 <- printname2 <- NULL
# Get marker 1 genotype data
if(missing(mark1)) { # no data given
if(missing(mname1)) # no marker data or marker name, have to stop
stop("Either mname1 or mark1 must be specified.")
# get marker data according to marker name
tmp <- effectplot.getmark(cross, mname1)
tmptmp <- attr(tmp, "mname")
if(!is.null(tmptmp)) mname1 <- tmptmp
mark1 <- tmp$mark
gennames1 <- tmp$genname
# perhaps alternative print name
if(length(grep(pm.pattern, mname1))>0) {
tmp <- find.pseudomarkerpos(cross, mname1, "draws")
printname1 <- paste(tmp[1,1], charround(tmp[1,2],dig), sep="@")
}
}
else {
# make mark1 a matrix if it's not
if(!is.matrix(mark1))
mark1 <- matrix(mark1, ncol=1)
if(dim(mark1)[1] != n.ind)
stop("Marker 1 data has the wrong dimension")
if(missing(mname1))
mname1 <- "Marker 1"
}
if(is.null(printname1)) printname1 <- mname1
# Deal with marker 2
if(!is.null(mname2) || !is.null(mark2)) {
if(is.null(mark2)) {
# get marker data according to marker name
tmp <- effectplot.getmark(cross, mname2)
tmptmp <- attr(tmp, "mname")
if(!is.null(tmptmp)) mname2 <- tmptmp
mark2 <- tmp$mark
gennames2 <- tmp$genname
# perhaps alternative print name
if(length(grep(pm.pattern, mname2))>0) {
tmp <- find.pseudomarkerpos(cross, mname2, "draws")
printname2 <- paste(tmp[1,1], charround(tmp[1,2],dig), sep="@")
}
}
else { # mark2 data is given
# make mark2 a matrix if it's not
if(!is.matrix(mark2))
mark2 <- matrix(mark2, ncol=1)
if(dim(mark2)[1] != n.ind)
stop("Marker 2 data has the wrong dimension")
if(is.null(mname2))
mname2 <- "Marker 2"
}
if(is.null(printname2)) printname2 <- mname2
}
else {
mark2 <- NULL
geno2 <- NULL
}
### till now, mark1 and mark2 are genotype data in matrix
########################################################
# deal with the data - if one of them is a pseudomarker,
# make the other one the same number of draws
########################################################
# determine number of draws - this part of codes works even if mark2 is NULL
ndraws1 <- dim(mark1)[2]
if(is.null(mark2))
ndraws2 <- 1
else
ndraws2 <- dim(mark2)[2]
# make them the same number of draws
if( (ndraws1>1) && (ndraws2>1) ) {
# two pseudomarkers, they must have the same number of draws
if(ndraws1 != ndraws2)
stop("Input two pseudomarkers have different number of draws.")
else
ndraws <- ndraws1
}
else if( (ndraws1>1) && (ndraws2==1) ) {
# one pm and one typed marker
if(!is.null(mark2))
mark2 <- matrix(rep(mark2,ndraws1), ncol=ndraws1)
ndraws <- ndraws1
}
else if( (ndraws1==1) && (ndraws2>1) ) {
# one pm and one typed marker
mark1 <- matrix(rep(mark1,ndraws2), ncol=ndraws2)
ndraws <- ndraws2
}
else # they are all real markers
ndraws <- 1
# drop data for individuals with missing phenotypes or genotypes
keepind <- !is.na(pheno)
if(!is.null(mark1))
keepind <- keepind & apply(mark1, 1, function(a) all(!is.na(a)))
if(!is.null(mark2))
keepind <- keepind & apply(mark2, 1, function(a) all(!is.na(a)))
mark1 <- mark1[keepind,]
mark2 <- mark2[keepind,]
pheno <- pheno[keepind]
########################################################
# 1. get level names - this part will be executed when
# user only input mark without mname and geno
# 2. adjust marker data if the input is not numeric
########################################################
tmpf <- factor(mark1)
if(!missing(geno1)) { # geno1 is given
# check if it has the correct length
if(length(geno1) < length(levels(tmpf)))
stop("geno1 is too short.")
}
else {
# geno1 is not given
if(!is.null(gennames1)) # get it from genname1
geno1 <- gennames1
else if(is.factor(mark1)) { # or if it's factor, get it from level
geno1 <- levels(mark1)
mark1 <- as.numeric(mark1)
}
else if(!is.numeric(mark1)) {
# if it's neither factor nor numeric, it must be a string vector
# such like c("F","M","F")...
geno1 <- levels(tmpf)
}
else { # otherwise, generate a standard one
geno1 <- getgenonames(type, "A", cross.attr=attributes(cross))
if(length(levels(tmpf)) > length(geno1))
geno1 <- c(geno1, rep("?", length(levels(tmpf)) -
length(geno1)))
}
}
# adjust marker data - if the input is not numeric, convert them into numeric
if(!is.numeric(mark1))
mark1 <- matrix(as.numeric(tmpf, levels=sort(levels(tmpf))), ncol=ndraws)
# Now work on mark2
if(!is.null(mark2)) {
tmpf <- factor(mark2)
if(!missing(geno2)) { # geno2 is given
# check if it has the correct length
if(length(geno2) < length(levels(tmpf)))
stop("geno2 is too short.")
}
else {
# geno2 is not given
if(!is.null(gennames2)) # get it from genname2
geno2 <- gennames2
else if(is.factor(mark2)) { # or if it's factor, get it from level
geno2 <- levels(mark2)
mark2 <- as.numeric(mark2)
}
else if(!is.numeric(mark2)) {
# if it's neither factor nor numberic, it must be a string vector
# such like c("F","M","F")...
geno2 <- levels(tmpf)
}
else { # otherwise, generate a standard one
geno2 <- getgenonames(type, "A", cross.attr=attributes(cross))
if(length(levels(tmpf)) > length(geno2))
geno2 <- c(geno2, rep("?", length(levels(tmpf)) -
length(geno2)))
}
}
# adjust marker data - if the input is not numeric, convert them into numeric
if(!is.numeric(mark2))
mark2 <- matrix(as.numeric(tmpf, levels=sort(levels(tmpf))), ncol=ndraws)
}
# number of genotypes
ngen1 <- length(geno1)
if(!is.null(mark2))
ngen2 <- length(geno2)
# calculate means and ses
# and make output object
# the output will be a data frame. For two-marker case,
# the rows corresponding to the first marker and the columns
# corresponding to the second marker
result <- effectplot.calmeanse(pheno, mark1, mark2, geno1, geno2, ndraws, var.flag)
means <- result$Means
ses <- result$SEs
# assign column and row names
if(is.null(mark2)) {
if(length(means) != length(geno1)) {
warning("Number of genotypes is different than length(geno1).")
if(length(means) < length(geno1))
geno1 <- geno1[1:length(means)]
else geno1 <- c(geno1, rep("?", length(means) - length(geno1)))
ngen1 <- length(geno1)
}
names(result$Means) <- paste(printname1, geno1, sep = ".")
names(result$SEs) <- paste(printname1, geno1, sep = ".")
}
else {
if(nrow(means) != length(geno1)) {
warning("Number of genotypes in marker 1 is different than length(geno1).")
if(nrow(means) < length(geno1))
geno1 <- geno1[1:nrow(means)]
else geno1 <- c(geno1, rep("?", nrow(means) - length(geno1)))
ngen1 <- length(geno1)
}
if(ncol(means) != length(geno2)) {
warning("Number of genotypes in marker 2 is different than length(geno2).")
if(ncol(means) < length(geno2))
geno2 <- geno2[1:ncol(means)]
else geno2 <- c(geno2, rep("?", ncol(means) - length(geno2)))
ngen2 <- length(geno2)
}
rownames(result$Means) <- paste(printname1, geno1, sep = ".")
colnames(result$Means) <- paste(printname2, geno2, sep = ".")
rownames(result$SEs) <- paste(printname1, geno1, sep = ".")
colnames(result$SEs) <- paste(printname2, geno2, sep = ".")
}
# calculate lo's and hi's for plot
lo <- means - ses
hi <- means + ses
######### Draw the figure if requested ############
if(draw) {
# graphics parameters
old.xpd <- par("xpd")
old.las <- par("las")
par(xpd = FALSE, las = 1)
on.exit(par(xpd = old.xpd, las = old.las))
# colors (for case of two markers)
if(missing(col)) {
if(ngen1 <= 5) {
if(ngen1 == 1) int.color <- "black"
else if(ngen1 == 2) int.color <- c("red", "blue")
else int.color <- c("black", "red", "blue", "orange", "green")[1:ngen1]
}
else
int.color <- c("black", rainbow(ngen1-1, start=0, end=2/3))
}
else int.color <- col
# plot title
if(missing(main)) {
if(is.null(mark2))
main <- paste("Effect plot for", printname1)
else main <- paste("Interaction plot for", printname1, "and",
printname2)
}
# y axis limits
if(missing(ylim)) {
ylimits <- range(c(lo, means, hi), na.rm = TRUE)
ylimits[2] <- ylimits[2] + diff(ylimits) * 0.1
}
else ylimits <- ylim
# x axis limits
if(is.null(mark2)) { # one marker
u <- seq(along=geno1)
d <- diff(u[1:2])
xlimits <- c(min(u) - d/4, max(u) + d/4)
}
else { # two markers
u <- seq(along=geno2)
d <- diff(u[1:2])
xlimits <- c(min(u) - d/4, max(u) + d/4)
}
## fix of x limits
d <- 1
xlimits <- c(1 - d/4, length(u) + d/4)
if(is.null(mark2)) { # single marker
if(missing(xlab)) xlab <- printname1
if(missing(ylab)) ylab <- names(cross$pheno)[pheno.col]
if(missing(col)) col <- "black"
# plot the means
plot(1:ngen1, means, main = main, xlab = xlab, ylab = ylab,
pch = 1, col = col[1], ylim = ylimits, xaxt = "n",
type = "b", xlim = xlimits)
# confidence limits
for(i in 1:ngen1) {
if(!is.na(lo[i]) && !is.na(hi[i]))
lines(c(i, i), c(lo[i], hi[i]), pch = 3, col = col[1],
type = "b", lty = 3)
}
# X-axis ticks
a <- par("usr")
ystart <- a[3]
yend <- ystart - diff(a[3:4]) * 0.02
ytext <- ystart - diff(a[3:4]) * 0.05
# for(i in 1:ngen1) {
# lines(x = c(i, i), y = c(ystart, yend), xpd = TRUE)
# text(i, ytext, geno1[i], xpd = TRUE)
# }
axis(side=1, at=1:ngen1, labels=geno1)
}
else { # two markers
if(missing(xlab)) xlab <- printname2
if(missing(ylab)) ylab <- names(cross$pheno)[pheno.col]
# plot the first genotype of marker 1
plot(1:ngen2, means[1, ], main = main, xlab = xlab,
ylab = ylab, pch = 1, col = int.color[1],
ylim = ylimits, xaxt = "n", type = "b", xlim = xlimits)
# confidence limits
for(i in 1:ngen2) {
if(!is.na(lo[1, i]) && !is.na(hi[1, i]))
lines(c(i, i), c(lo[1, i], hi[1, i]), pch = 3,
col = int.color[1], type = "b", lty = 3)
}
for(j in 2:ngen1) { # for the rest of genotypes for Marker 1
lines(1:ngen2, means[j, ], col = int.color[j], pch = 1,
type = "b")
# confidence limits
for(i in 1:ngen2) {
if(!is.na(lo[j, i]) && !is.na(hi[j, i]))
lines(c(i, i), c(lo[j, i], hi[j, i]), pch = 3,
col = int.color[j], type = "b", lty = 3)
}
}
# draw X-axis ticks
a <- par("usr")
ystart <- a[3]
yend <- ystart - diff(a[3:4]) * 0.02
ytext <- ystart - diff(a[3:4]) * 0.05
# for(i in 1:ngen2) {
# lines(x = c(i, i), y = c(ystart, yend), xpd = TRUE)
# text(i, ytext, geno2[i], xpd = TRUE)
# }
axis(side=1, at=1:ngen2, labels=geno2)
# add legend
if(add.legend) {
col <- int.color[1:ngen1]
# legend position
x.leg <- a[1]*0.25+a[2]*0.75
y.leg <- a[4] - diff(a[3:4]) * 0.05
y.leg2 <- a[4] - diff(a[3:4]) * 0.03
legend(x.leg, y.leg, geno1, lty = 1, pch = 1, col = col,
cex = 1, xjust = 0.5)
if(missing(legend.lab)) legend.lab <- printname1
text(x.leg, y.leg2, legend.lab)
}
}
}
return(invisible(result))
}
##############################################
# function to get genotype data for a marker
# given marker name
##############################################
effectplot.getmark <-
function (cross, mname)
{
# cross type
type <- crosstype(cross)
# return variables
mark <- NULL
gennames <- NULL
# for pseudomarkers refered to as "1@10.5":
# - check that it is not a phenotype or marker name
# - otherwise convert to the usual name via find.pseudomarker
pmalt.pattern <- "@-*[0-9]+" # alternate way to refer to a pseudomarker ("1@10.5")
if(length(grep(pmalt.pattern, mname))>0 &&
!(mname %in% names(cross$pheno) || mname %in% colnames(pull.geno(cross)))) {
ss <- unlist(strsplit(mname, "@"))
if(!(ss[1] %in% names(cross$geno)))
stop("Don't understand the marker name ", mname)
mname <- find.pseudomarker(cross, ss[1], as.numeric(ss[2]), "draws")
}
# determine marker type - it could be a marker, a pseudomarker or a phenotype
mar.type <- "none"
# regular expression pattern for a pseudomarker names
pm.pattern <- "^c.*\\.loc.*$" # pseudomarker names will be like "c1.loc10"
if(mname %in% names(cross$pheno)) { # this is a phenotype
mar.type <- "pheno"
idx.pos <- which(mname==names(cross$pheno))
}
else if(length(grep(pm.pattern, mname)) > 0) { # like "c1.loc10", this is a pseudomarker
# note that the column names for draws is like "loc10",
# so I need to take the part after "." in mname
tmp <- unlist(strsplit(mname, "loc"))
chr <- substr(tmp[1],2,nchar(tmp[1])-1) # this will be like 1 or "X"
if( !(chr %in% names(cross$geno)) )
stop("Couldn't find marker ", mname)
mar.type <- "pm"
chr_type <- chrtype(cross$geno[[chr]])
pm.name <- paste("loc", tmp[2],sep="") # this will be like loc10
idx.pos <- which(pm.name==colnames(cross$geno[[chr]]$draws))
if(length(idx.pos) == 0)
stop("Couldn't find marker ", mname)
else if(length(idx.pos)>1) # take the first one for multiple markers with the same name
idx.pos <- idx.pos[1]
}
else { # this is a real marker name but it could be a observed or imputed
for(i in 1:length(cross$geno)) {
if(mname %in% colnames(cross$geno[[i]]$draws)) { # this is a pseudomarker
mar.type <- "pm"
chr <- i
chr_type <- chrtype(cross$geno[[chr]])
idx.pos <- which(mname == colnames(cross$geno[[i]]$draws))
break
}
else if(mname %in% colnames(cross$geno[[i]]$data)) { # this is a typed marker
mar.type <- "marker"
chr <- i
chr_type <- chrtype(cross$geno[[i]])
idx.pos <- which(mname == colnames(cross$geno[[i]]$data))
break
}
}
}
# if didn't find this marker
if(mar.type == "none")
stop("Marker ", mname, " not found")
# get data from typed marker, pseudomarker or phenotype
if(mar.type == "pheno") { # this is a phenotype
mark <- cross$pheno[,idx.pos]
# the phenotype need to be categorical
if(length(unique(mark)) > 5) { # I'm using arbitrary number here
stop("The input phenotype ", mname, " is not a categorical trait")
}
gennames <- sort(unique(mark))
}
else if(mar.type=="marker") { # this is a real marker
mark <- cross$geno[[chr]]$data[, idx.pos]
# if X chr and backcross or intercross, get sex/dir data + revise data
if(chr_type == "X" && (type %in% c("bc","f2","bcsft"))) {
sexpgm <- getsex(cross)
mark <- as.numeric(reviseXdata(type, "full", sexpgm,
geno = as.matrix(mark),
cross.attr=attributes(cross)))
gennames <- getgenonames(type, chr_type, "full", sexpgm, attributes(cross))
}
}
else if(mar.type=="pm") { # this is a pseudomarker
# get the imputed genotype data for this marker
mark <- cross$geno[[chr]]$draws[,idx.pos,,drop=FALSE]
# if X chr and backcross or intercross, get sex/dir data + revise data
if(chr_type == "X" && (type %in% c("bc","f2","bcsft"))) {
sexpgm <- getsex(cross)
mark <- reviseXdata(type, "full", sexpgm, draws=mark,
cross.attr=attributes(cross))[,1,]
gennames <- getgenonames(type, chr_type, "full", sexpgm, attributes(cross))
}
else mark <- mark[,1,]
}
else # none of the above
stop("Couldn't find marker ", mname)
# make mark a matrix if it's not one
if(!is.matrix(mark))
mark <- matrix(mark, ncol=1)
# return
ret <- list(mark=mark, gennames=gennames)
attr(ret, "mname") <- mname
ret
}
##############################################
# function to calculate the means and ses
# if ndraws is 1, it's easy
# if ndraws > 1 (has pseudomarker),
# loop thru the draws
##############################################
effectplot.calmeanse <-
function(pheno, mark1, mark2, geno1, geno2, ndraws, var.flag=c("pooled","group"))
{
# local variables
nind <- length(pheno)
# method to calculate variances for estimated QTL effects
var.flag <- match.arg(var.flag)
result <- NULL
nind <- sum(!is.na(pheno)) # number of individuals
if(is.null(mark2)) { # if mark2 is missing
if(ndraws > 1) { # more than one draws
mark1.level <- seq(along=geno1) # level for mark1
# init
means.all <- matrix(NA, nrow=ndraws, ncol=length(mark1.level))
colnames(means.all) <- mark1.level
vars.all <- matrix(NA, nrow=ndraws, ncol=length(mark1.level))
colnames(vars.all) <- mark1.level
weight <- rep(0, ndraws) # weight for draws
# loop thru draws
for(i in 1:ndraws) {
mark1.tmp <- mark1[,i] # data for current draw
# fit a regression - this is used to calculate the weights
mark1.factor <- factor(mark1.tmp, mark1.level)
lm.tmp <- lm(pheno~mark1.factor-1)
rss <- sum(lm.tmp$residuals^2)
# compute the weight
weight[i] <- (-nind/2)*log(rss)
# group means
means.tmp <- tapply(pheno, mark1.tmp, mean, na.rm = TRUE)
# calculate group means and variances
if(var.flag=="group") { # use variance in each group
vars.tmp <- tapply(pheno, mark1.tmp, function(a) var(a,na.rm = TRUE)/length(a))
}
else { # use pooled variance
vars.tmp <- tapply(mark1.tmp, mark1.tmp, function(a) rss/nind/length(a))
}
# note that there could be missing categories in draws
means.all[i, names(means.tmp)] <- means.tmp
vars.all[i, names(vars.tmp)] <- vars.tmp
}
# average across draws - for vars, it should be
# mean of variance plus variance of means
weight <- exp(weight-max(weight))
means <- apply(means.all, 2, function(a) weighted.mean(a,weight,na.rm=TRUE))
meanvar <- apply(vars.all, 2, function(a) weighted.mean(a,weight,na.rm=TRUE)) # mean of vars
varmean <- apply(means.all, 2, function(a) weighted.mean((a-mean(a,na.rm=TRUE))^2,weight,na.rm=TRUE)) # var of means
measured <- apply(means.all, 2, function(a) sum(!is.na(a)))
means[measured==0] <- meanvar[measured==0] <- varmean[measured==0] <- NA
# standard error
ses <- sqrt(meanvar+varmean)
}
else { # ndraws is 1
u <- sort(unique(mark1))
if(any(!(u %in% seq(along=geno1)))) {
newmark1 <- mark1
for(i in seq(along=u))
newmark1[mark1==u[i]] <- i
mark1 <- newmark1
}
means <- tapply(pheno, mark1, mean, na.rm = TRUE)
if(var.flag == "group") { # use group variance
ses <- tapply(pheno, mark1, function(a) sd(a, na.rm = TRUE)/sqrt(sum(!is.na(a))))
}
else { # use pooled variance
mark1.factor <- factor(mark1, seq(along=geno1))
lm.tmp <- lm(pheno~mark1.factor-1)
rss <- sum(lm.tmp$residuals^2)
ses <- tapply(mark1, mark1, function(a) sqrt(rss/nind/length(a)))
}
}
}
else { # with mark2
if(ndraws > 1) {
mark1.level <- seq(along=geno1) # level for mark1
mark2.level <- seq(along=geno2) # level for mark2
u <- sort(unique(as.numeric(mark1)))
if(any(!(u %in% seq(along=geno1)))) {
newmark1 <- mark1
for(i in seq(along=u))
for(j in 1:ncol(mark2))
newmark1[mark1[,j]==u[i],j] <- i
mark1 <- newmark1
}
u <- sort(unique(as.numeric(mark2)))
if(any(!(u %in% seq(along=geno2)))) {
newmark2 <- mark2
for(i in seq(along=u))
for(j in 1:ncol(mark2))
newmark2[mark2[,j]==u[i],j] <- i
mark2 <- newmark2
}
# init
means.all <- array(NA, c(length(mark1.level), length(mark2.level), ndraws))
dimnames(means.all) <- list(mark1.level, mark2.level, NULL)
vars.all <- array(NA, c(length(mark1.level), length(mark2.level), ndraws))
dimnames(vars.all) <- list(mark1.level, mark2.level, NULL)
weight <- rep(0, ndraws) # weight for draws
# loop thru draws
for(i in 1:ndraws) {
mark1.tmp <- mark1[,i] # data for current draw
mark2.tmp <- mark2[,i]
# fit a regression - this is used to calculate the weights
mark1.factor <- factor(mark1.tmp, mark1.level)
mark2.factor <- factor(mark2.tmp, mark2.level)
lm.tmp <- lm(pheno~mark1.factor+mark2.factor+1)
rss <- sum(lm.tmp$residuals^2)
# compute the weight
weight[i] <- (-nind/2)*log(rss)
# group means
means.tmp <- tapply(pheno, list(mark1.tmp, mark2.tmp), mean, na.rm = TRUE)
# calculate group means and variances
if(var.flag=="group") { # use variance in each group
vars.tmp <- tapply(pheno, list(mark1.tmp,mark2.tmp),
function(a) var(a,na.rm = TRUE)/length(a))
}
else { # use pooled variance
vars.tmp <- tapply(mark1.tmp, list(mark1.tmp,mark2.tmp),
function(a) rss/nind/length(a))
}
# note that there could be missing categories in draws
means.all[dimnames(means.tmp)[[1]], dimnames(means.tmp)[[2]],i] <- means.tmp
vars.all[dimnames(vars.tmp)[[1]], dimnames(vars.tmp)[[2]], i] <- vars.tmp
}
# average across draws - for vars, it should be
# mean of variance plus variance of means
weight <- exp(weight-max(weight))
means <- apply(means.all, c(1,2), function(a) weighted.mean(a,weight,na.rm=TRUE))
meanvar <- apply(vars.all, c(1,2), function(a) weighted.mean(a,weight,na.rm=TRUE))
varmean <- apply(means.all, c(1,2), function(a) weighted.mean((a-mean(a,na.rm=TRUE))^2,weight,na.rm=TRUE)) # var of means
measured <- apply(means.all, c(1,2), function(a) sum(!is.na(a)))
means[measured==0] <- meanvar[measured==0] <- varmean[measured==0] <- NA
# standard error
ses <- sqrt(meanvar+varmean)
}
else { # ndraws is 1
u <- sort(unique(mark1))
if(any(!(u %in% seq(along=geno1)))) {
newmark1 <- mark1
for(i in seq(along=u))
newmark1[mark1==u[i]] <- i
mark1 <- newmark1
}
u <- sort(unique(mark2))
if(any(!(u %in% seq(along=geno2)))) {
newmark2 <- mark2
for(i in seq(along=u))
newmark2[mark2==u[i]] <- i
mark2 <- newmark2
}
mark1 <- factor(mark1, seq(along=geno1))
mark2 <- factor(mark2, seq(along=geno2))
means <- tapply(pheno, list(mark1, mark2), mean, na.rm = TRUE)
if(var.flag=="group") { # use group variance
ses <- tapply(pheno, list(mark1, mark2), function(a) sd(a, na.rm = TRUE)/sqrt(sum(!is.na(a))))
}
else {# use pooled variance
lm.tmp <- lm(pheno~mark1+mark2-1)
rss <- sum(lm.tmp$residuals^2)
ses <- tapply(mark1, list(mark1, mark2), function(a) sqrt(rss/nind/length(a)))
}
}
}
# result
result$Means <- means
result$SEs <- ses
result
}
# end of effectplot.R
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Defines functions effectplot.calmeanse
######################################################################
#
# effectplot.R
#
# copyright (c) 2002-2019, Hao Wu and Karl W. Broman
# Last modified Dec, 2019
# first written Jul, 2002
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License,
# version 3, as published by the Free Software Foundation.
#
# 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, version 3, for more details.
#
# A copy of the GNU General Public License, version 3, is available
# at http://www.r-project.org/Licenses/GPL-3
#
# Modified by Hao Wu Feb 2005 for the following:
# 1. function will take marker, pseudomarker or phenotype as input;
# 2. separate functions to extract marker genodata given marker names
# and calculate means and ses;
#
# Part of the R/qtl package
# Contains: effectplot, effectplot.getmark, effectplot.calmeanse
#
######################################################################
effectplot <-
function (cross, pheno.col = 1, mname1, mark1, geno1, mname2,
mark2, geno2, main, ylim, xlab, ylab, col, add.legend = TRUE,
legend.lab, draw=TRUE, var.flag=c("pooled","group"))
{
if(!inherits(cross, "cross"))
stop("The first input variable must be an object of class cross")
if(LikePheVector(pheno.col, nind(cross), nphe(cross))) {
cross$pheno <- cbind(pheno.col, cross$pheno)
pheno.col <- 1
}
# if mname2 is given but not mname1, switch it around
if((missing(mname1) && missing(mark1) && missing(geno1)) &&
any(!missing(mname2) || missing(mark2) || missing(geno2))) {
if(!missing(mname2)) { mname1 <- mname2; mname2 <- NULL }
if(!missing(mark2)) { mark1 <- mark2; mark2 <- NULL }
if(!missing(geno2)) { geno1 <- geno2; geno2 <- NULL }
}
if(missing(mark2)) mark2 <- NULL
if(missing(mname2)) mname2 <- NULL
if(length(pheno.col) > 1) {
pheno.col <- pheno.col[1]
warning("effectplot can take just one phenotype; only the first will be used")
}
if(is.character(pheno.col)) {
num <- find.pheno(cross, pheno.col)
if(is.na(num))
stop("Couldn't identify phenotype \"", pheno.col, "\"")
pheno.col <- num
}
if(pheno.col < 1 | pheno.col > nphe(cross))
stop("pheno.col values should be between 1 and the no. phenotypes")
if(!is.numeric(cross$pheno[,pheno.col]))
stop("phenotype \"", colnames(cross$pheno)[pheno.col], "\" is not numeric.")
var.flag <- match.arg(var.flag)
# local variables
n.ind <- nind(cross)
pheno <- cross$pheno[, pheno.col]
type <- crosstype(cross)
chr_type1 <- chr_type2 <- "A"
gennames1 <- gennames2 <- NULL
# If imputations are not available, create them
if(!("draws" %in% names(cross$geno[[1]]))) {
warning(" -Running sim.geno.")
cross <- sim.geno(cross, n.draws=16)
}
####################################################
# get genotype data for markers given marker name
# if marker genodata were given, this will be skipped
####################################################
# used for alternative print name for pseudomarkers
pm.pattern <- "^c.*\\.loc.*$" # pseudomarker names will be like "c1.loc10"
dig <- 1
step <- attr(cross$geno[[1]]$draws, "step")
if(!is.null(step)) {
if(step > 0) dig <- max(dig, -floor(log10(step)))
}
else {
stepw <- attr(cross$geno[[1]]$draws, "stepwidth")
if(!is.null(stepw) && stepw > 0) dig <- max(dig, -floor(log10(stepw)))
}
printname1 <- printname2 <- NULL
# Get marker 1 genotype data
if(missing(mark1)) { # no data given
if(missing(mname1)) # no marker data or marker name, have to stop
stop("Either mname1 or mark1 must be specified.")
# get marker data according to marker name
tmp <- effectplot.getmark(cross, mname1)
tmptmp <- attr(tmp, "mname")
if(!is.null(tmptmp)) mname1 <- tmptmp
mark1 <- tmp$mark
gennames1 <- tmp$genname
# perhaps alternative print name
if(length(grep(pm.pattern, mname1))>0) {
tmp <- find.pseudomarkerpos(cross, mname1, "draws")
printname1 <- paste(tmp[1,1], charround(tmp[1,2],dig), sep="@")
}
}
else {
# make mark1 a matrix if it's not
if(!is.matrix(mark1))
mark1 <- matrix(mark1, ncol=1)
if(dim(mark1)[1] != n.ind)
stop("Marker 1 data has the wrong dimension")
if(missing(mname1))
mname1 <- "Marker 1"
}
if(is.null(printname1)) printname1 <- mname1
# Deal with marker 2
if(!is.null(mname2) || !is.null(mark2)) {
if(is.null(mark2)) {
# get marker data according to marker name
tmp <- effectplot.getmark(cross, mname2)
tmptmp <- attr(tmp, "mname")
if(!is.null(tmptmp)) mname2 <- tmptmp
mark2 <- tmp$mark
gennames2 <- tmp$genname
# perhaps alternative print name
if(length(grep(pm.pattern, mname2))>0) {
tmp <- find.pseudomarkerpos(cross, mname2, "draws")
printname2 <- paste(tmp[1,1], charround(tmp[1,2],dig), sep="@")
}
}
else { # mark2 data is given
# make mark2 a matrix if it's not
if(!is.matrix(mark2))
mark2 <- matrix(mark2, ncol=1)
if(dim(mark2)[1] != n.ind)
stop("Marker 2 data has the wrong dimension")
if(is.null(mname2))
mname2 <- "Marker 2"
}
if(is.null(printname2)) printname2 <- mname2
}
else {
mark2 <- NULL
geno2 <- NULL
}
### till now, mark1 and mark2 are genotype data in matrix
########################################################
# deal with the data - if one of them is a pseudomarker,
# make the other one the same number of draws
########################################################
# determine number of draws - this part of codes works even if mark2 is NULL
ndraws1 <- dim(mark1)[2]
if(is.null(mark2))
ndraws2 <- 1
else
ndraws2 <- dim(mark2)[2]
# make them the same number of draws
if( (ndraws1>1) && (ndraws2>1) ) {
# two pseudomarkers, they must have the same number of draws
if(ndraws1 != ndraws2)
stop("Input two pseudomarkers have different number of draws.")
else
ndraws <- ndraws1
}
else if( (ndraws1>1) && (ndraws2==1) ) {
# one pm and one typed marker
if(!is.null(mark2))
mark2 <- matrix(rep(mark2,ndraws1), ncol=ndraws1)
ndraws <- ndraws1
}
else if( (ndraws1==1) && (ndraws2>1) ) {
# one pm and one typed marker
mark1 <- matrix(rep(mark1,ndraws2), ncol=ndraws2)
ndraws <- ndraws2
}
else # they are all real markers
ndraws <- 1
# drop data for individuals with missing phenotypes or genotypes
keepind <- !is.na(pheno)
if(!is.null(mark1))
keepind <- keepind & apply(mark1, 1, function(a) all(!is.na(a)))
if(!is.null(mark2))
keepind <- keepind & apply(mark2, 1, function(a) all(!is.na(a)))
mark1 <- mark1[keepind,]
mark2 <- mark2[keepind,]
pheno <- pheno[keepind]
########################################################
# 1. get level names - this part will be executed when
# user only input mark without mname and geno
# 2. adjust marker data if the input is not numeric
########################################################
tmpf <- factor(mark1)
if(!missing(geno1)) { # geno1 is given
# check if it has the correct length
if(length(geno1) < length(levels(tmpf)))
stop("geno1 is too short.")
}
else {
# geno1 is not given
if(!is.null(gennames1)) # get it from genname1
geno1 <- gennames1
else if(is.factor(mark1)) { # or if it's factor, get it from level
geno1 <- levels(mark1)
mark1 <- as.numeric(mark1)
}
else if(!is.numeric(mark1)) {
# if it's neither factor nor numeric, it must be a string vector
# such like c("F","M","F")...
geno1 <- levels(tmpf)
}
else { # otherwise, generate a standard one
geno1 <- getgenonames(type, "A", cross.attr=attributes(cross))
if(length(levels(tmpf)) > length(geno1))
geno1 <- c(geno1, rep("?", length(levels(tmpf)) -
length(geno1)))
}
}
# adjust marker data - if the input is not numeric, convert them into numeric
if(!is.numeric(mark1))
mark1 <- matrix(as.numeric(tmpf, levels=sort(levels(tmpf))), ncol=ndraws)
# Now work on mark2
if(!is.null(mark2)) {
tmpf <- factor(mark2)
if(!missing(geno2)) { # geno2 is given
# check if it has the correct length
if(length(geno2) < length(levels(tmpf)))
stop("geno2 is too short.")
}
else {
# geno2 is not given
if(!is.null(gennames2)) # get it from genname2
geno2 <- gennames2
else if(is.factor(mark2)) { # or if it's factor, get it from level
geno2 <- levels(mark2)
mark2 <- as.numeric(mark2)
}
else if(!is.numeric(mark2)) {
# if it's neither factor nor numberic, it must be a string vector
# such like c("F","M","F")...
geno2 <- levels(tmpf)
}
else { # otherwise, generate a standard one
geno2 <- getgenonames(type, "A", cross.attr=attributes(cross))
if(length(levels(tmpf)) > length(geno2))
geno2 <- c(geno2, rep("?", length(levels(tmpf)) -
length(geno2)))
}
}
# adjust marker data - if the input is not numeric, convert them into numeric
if(!is.numeric(mark2))
mark2 <- matrix(as.numeric(tmpf, levels=sort(levels(tmpf))), ncol=ndraws)
}
# number of genotypes
ngen1 <- length(geno1)
if(!is.null(mark2))
ngen2 <- length(geno2)
# calculate means and ses
# and make output object
# the output will be a data frame. For two-marker case,
# the rows corresponding to the first marker and the columns
# corresponding to the second marker
result <- effectplot.calmeanse(pheno, mark1, mark2, geno1, geno2, ndraws, var.flag)
means <- result$Means
ses <- result$SEs
# assign column and row names
if(is.null(mark2)) {
if(length(means) != length(geno1)) {
warning("Number of genotypes is different than length(geno1).")
if(length(means) < length(geno1))
geno1 <- geno1[1:length(means)]
else geno1 <- c(geno1, rep("?", length(means) - length(geno1)))
ngen1 <- length(geno1)
}
names(result$Means) <- paste(printname1, geno1, sep = ".")
names(result$SEs) <- paste(printname1, geno1, sep = ".")
}
else {
if(nrow(means) != length(geno1)) {
warning("Number of genotypes in marker 1 is different than length(geno1).")
if(nrow(means) < length(geno1))
geno1 <- geno1[1:nrow(means)]
else geno1 <- c(geno1, rep("?", nrow(means) - length(geno1)))
ngen1 <- length(geno1)
}
if(ncol(means) != length(geno2)) {
warning("Number of genotypes in marker 2 is different than length(geno2).")
if(ncol(means) < length(geno2))
geno2 <- geno2[1:ncol(means)]
else geno2 <- c(geno2, rep("?", ncol(means) - length(geno2)))
ngen2 <- length(geno2)
}
rownames(result$Means) <- paste(printname1, geno1, sep = ".")
colnames(result$Means) <- paste(printname2, geno2, sep = ".")
rownames(result$SEs) <- paste(printname1, geno1, sep = ".")
colnames(result$SEs) <- paste(printname2, geno2, sep = ".")
}
# calculate lo's and hi's for plot
lo <- means - ses
hi <- means + ses
######### Draw the figure if requested ############
if(draw) {
# graphics parameters
old.xpd <- par("xpd")
old.las <- par("las")
par(xpd = FALSE, las = 1)
on.exit(par(xpd = old.xpd, las = old.las))
# colors (for case of two markers)
if(missing(col)) {
if(ngen1 <= 5) {
if(ngen1 == 1) int.color <- "black"
else if(ngen1 == 2) int.color <- c("red", "blue")
else int.color <- c("black", "red", "blue", "orange", "green")[1:ngen1]
}
else
int.color <- c("black", rainbow(ngen1-1, start=0, end=2/3))
}
else int.color <- col
# plot title
if(missing(main)) {
if(is.null(mark2))
main <- paste("Effect plot for", printname1)
else main <- paste("Interaction plot for", printname1, "and",
printname2)
}
# y axis limits
if(missing(ylim)) {
ylimits <- range(c(lo, means, hi), na.rm = TRUE)
ylimits[2] <- ylimits[2] + diff(ylimits) * 0.1
}
else ylimits <- ylim
# x axis limits
if(is.null(mark2)) { # one marker
u <- seq(along=geno1)
d <- diff(u[1:2])
xlimits <- c(min(u) - d/4, max(u) + d/4)
}
else { # two markers
u <- seq(along=geno2)
d <- diff(u[1:2])
xlimits <- c(min(u) - d/4, max(u) + d/4)
}
## fix of x limits
d <- 1
xlimits <- c(1 - d/4, length(u) + d/4)
if(is.null(mark2)) { # single marker
if(missing(xlab)) xlab <- printname1
if(missing(ylab)) ylab <- names(cross$pheno)[pheno.col]
if(missing(col)) col <- "black"
# plot the means
plot(1:ngen1, means, main = main, xlab = xlab, ylab = ylab,
pch = 1, col = col[1], ylim = ylimits, xaxt = "n",
type = "b", xlim = xlimits)
# confidence limits
for(i in 1:ngen1) {
if(!is.na(lo[i]) && !is.na(hi[i]))
lines(c(i, i), c(lo[i], hi[i]), pch = 3, col = col[1],
type = "b", lty = 3)
}
# X-axis ticks
a <- par("usr")
ystart <- a[3]
yend <- ystart - diff(a[3:4]) * 0.02
ytext <- ystart - diff(a[3:4]) * 0.05
# for(i in 1:ngen1) {
# lines(x = c(i, i), y = c(ystart, yend), xpd = TRUE)
# text(i, ytext, geno1[i], xpd = TRUE)
# }
axis(side=1, at=1:ngen1, labels=geno1)
}
else { # two markers
if(missing(xlab)) xlab <- printname2
if(missing(ylab)) ylab <- names(cross$pheno)[pheno.col]
# plot the first genotype of marker 1
plot(1:ngen2, means[1, ], main = main, xlab = xlab,
ylab = ylab, pch = 1, col = int.color[1],
ylim = ylimits, xaxt = "n", type = "b", xlim = xlimits)
# confidence limits
for(i in 1:ngen2) {
if(!is.na(lo[1, i]) && !is.na(hi[1, i]))
lines(c(i, i), c(lo[1, i], hi[1, i]), pch = 3,
col = int.color[1], type = "b", lty = 3)
}
for(j in 2:ngen1) { # for the rest of genotypes for Marker 1
lines(1:ngen2, means[j, ], col = int.color[j], pch = 1,
type = "b")
# confidence limits
for(i in 1:ngen2) {
if(!is.na(lo[j, i]) && !is.na(hi[j, i]))
lines(c(i, i), c(lo[j, i], hi[j, i]), pch = 3,
col = int.color[j], type = "b", lty = 3)
}
}
# draw X-axis ticks
a <- par("usr")
ystart <- a[3]
yend <- ystart - diff(a[3:4]) * 0.02
ytext <- ystart - diff(a[3:4]) * 0.05
# for(i in 1:ngen2) {
# lines(x = c(i, i), y = c(ystart, yend), xpd = TRUE)
# text(i, ytext, geno2[i], xpd = TRUE)
# }
axis(side=1, at=1:ngen2, labels=geno2)
# add legend
if(add.legend) {
col <- int.color[1:ngen1]
# legend position
x.leg <- a[1]*0.25+a[2]*0.75
y.leg <- a[4] - diff(a[3:4]) * 0.05
y.leg2 <- a[4] - diff(a[3:4]) * 0.03
legend(x.leg, y.leg, geno1, lty = 1, pch = 1, col = col,
cex = 1, xjust = 0.5)
if(missing(legend.lab)) legend.lab <- printname1
text(x.leg, y.leg2, legend.lab)
}
}
}
return(invisible(result))
}
##############################################
# function to get genotype data for a marker
# given marker name
##############################################
effectplot.getmark <-
function (cross, mname)
{
# cross type
type <- crosstype(cross)
# return variables
mark <- NULL
gennames <- NULL
# for pseudomarkers refered to as "1@10.5":
# - check that it is not a phenotype or marker name
# - otherwise convert to the usual name via find.pseudomarker
pmalt.pattern <- "@-*[0-9]+" # alternate way to refer to a pseudomarker ("1@10.5")
if(length(grep(pmalt.pattern, mname))>0 &&
!(mname %in% names(cross$pheno) || mname %in% colnames(pull.geno(cross)))) {
ss <- unlist(strsplit(mname, "@"))
if(!(ss[1] %in% names(cross$geno)))
stop("Don't understand the marker name ", mname)
mname <- find.pseudomarker(cross, ss[1], as.numeric(ss[2]), "draws")
}
# determine marker type - it could be a marker, a pseudomarker or a phenotype
mar.type <- "none"
# regular expression pattern for a pseudomarker names
pm.pattern <- "^c.*\\.loc.*$" # pseudomarker names will be like "c1.loc10"
if(mname %in% names(cross$pheno)) { # this is a phenotype
mar.type <- "pheno"
idx.pos <- which(mname==names(cross$pheno))
}
else if(length(grep(pm.pattern, mname)) > 0) { # like "c1.loc10", this is a pseudomarker
# note that the column names for draws is like "loc10",
# so I need to take the part after "." in mname
tmp <- unlist(strsplit(mname, "loc"))
chr <- substr(tmp[1],2,nchar(tmp[1])-1) # this will be like 1 or "X"
if( !(chr %in% names(cross$geno)) )
stop("Couldn't find marker ", mname)
mar.type <- "pm"
chr_type <- chrtype(cross$geno[[chr]])
pm.name <- paste("loc", tmp[2],sep="") # this will be like loc10
idx.pos <- which(pm.name==colnames(cross$geno[[chr]]$draws))
if(length(idx.pos) == 0)
stop("Couldn't find marker ", mname)
else if(length(idx.pos)>1) # take the first one for multiple markers with the same name
idx.pos <- idx.pos[1]
}
else { # this is a real marker name but it could be a observed or imputed
for(i in 1:length(cross$geno)) {
if(mname %in% colnames(cross$geno[[i]]$draws)) { # this is a pseudomarker
mar.type <- "pm"
chr <- i
chr_type <- chrtype(cross$geno[[chr]])
idx.pos <- which(mname == colnames(cross$geno[[i]]$draws))
break
}
else if(mname %in% colnames(cross$geno[[i]]$data)) { # this is a typed marker
mar.type <- "marker"
chr <- i
chr_type <- chrtype(cross$geno[[i]])
idx.pos <- which(mname == colnames(cross$geno[[i]]$data))
break
}
}
}
# if didn't find this marker
if(mar.type == "none")
stop("Marker ", mname, " not found")
# get data from typed marker, pseudomarker or phenotype
if(mar.type == "pheno") { # this is a phenotype
mark <- cross$pheno[,idx.pos]
# the phenotype need to be categorical
if(length(unique(mark)) > 5) { # I'm using arbitrary number here
stop("The input phenotype ", mname, " is not a categorical trait")
}
gennames <- sort(unique(mark))
}
else if(mar.type=="marker") { # this is a real marker
mark <- cross$geno[[chr]]$data[, idx.pos]
# if X chr and backcross or intercross, get sex/dir data + revise data
if(chr_type == "X" && (type %in% c("bc","f2","bcsft"))) {
sexpgm <- getsex(cross)
mark <- as.numeric(reviseXdata(type, "full", sexpgm,
geno = as.matrix(mark),
cross.attr=attributes(cross)))
gennames <- getgenonames(type, chr_type, "full", sexpgm, attributes(cross))
}
}
else if(mar.type=="pm") { # this is a pseudomarker
# get the imputed genotype data for this marker
mark <- cross$geno[[chr]]$draws[,idx.pos,,drop=FALSE]
# if X chr and backcross or intercross, get sex/dir data + revise data
if(chr_type == "X" && (type %in% c("bc","f2","bcsft"))) {
sexpgm <- getsex(cross)
mark <- reviseXdata(type, "full", sexpgm, draws=mark,
cross.attr=attributes(cross))[,1,]
gennames <- getgenonames(type, chr_type, "full", sexpgm, attributes(cross))
}
else mark <- mark[,1,]
}
else # none of the above
stop("Couldn't find marker ", mname)
# make mark a matrix if it's not one
if(!is.matrix(mark))
mark <- matrix(mark, ncol=1)
# return
ret <- list(mark=mark, gennames=gennames)
attr(ret, "mname") <- mname
ret
}
##############################################
# function to calculate the means and ses
# if ndraws is 1, it's easy
# if ndraws > 1 (has pseudomarker),
# loop thru the draws
##############################################
effectplot.calmeanse <-
function(pheno, mark1, mark2, geno1, geno2, ndraws, var.flag=c("pooled","group"))
{
# local variables
nind <- length(pheno)
# method to calculate variances for estimated QTL effects
var.flag <- match.arg(var.flag)
result <- NULL
nind <- sum(!is.na(pheno)) # number of individuals
if(is.null(mark2)) { # if mark2 is missing
if(ndraws > 1) { # more than one draws
mark1.level <- seq(along=geno1) # level for mark1
# init
means.all <- matrix(NA, nrow=ndraws, ncol=length(mark1.level))
colnames(means.all) <- mark1.level
vars.all <- matrix(NA, nrow=ndraws, ncol=length(mark1.level))
colnames(vars.all) <- mark1.level
weight <- rep(0, ndraws) # weight for draws
# loop thru draws
for(i in 1:ndraws) {
mark1.tmp <- mark1[,i] # data for current draw
# fit a regression - this is used to calculate the weights
mark1.factor <- factor(mark1.tmp, mark1.level)
lm.tmp <- lm(pheno~mark1.factor-1)
rss <- sum(lm.tmp$residuals^2)
# compute the weight
weight[i] <- (-nind/2)*log(rss)
# group means
means.tmp <- tapply(pheno, mark1.tmp, mean, na.rm = TRUE)
# calculate group means and variances
if(var.flag=="group") { # use variance in each group
vars.tmp <- tapply(pheno, mark1.tmp, function(a) var(a,na.rm = TRUE)/length(a))
}
else { # use pooled variance
vars.tmp <- tapply(mark1.tmp, mark1.tmp, function(a) rss/nind/length(a))
}
# note that there could be missing categories in draws
means.all[i, names(means.tmp)] <- means.tmp
vars.all[i, names(vars.tmp)] <- vars.tmp
}
# average across draws - for vars, it should be
# mean of variance plus variance of means
weight <- exp(weight-max(weight))
means <- apply(means.all, 2, function(a) weighted.mean(a,weight,na.rm=TRUE))
meanvar <- apply(vars.all, 2, function(a) weighted.mean(a,weight,na.rm=TRUE)) # mean of vars
varmean <- apply(means.all, 2, function(a) weighted.mean((a-mean(a,na.rm=TRUE))^2,weight,na.rm=TRUE)) # var of means
measured <- apply(means.all, 2, function(a) sum(!is.na(a)))
means[measured==0] <- meanvar[measured==0] <- varmean[measured==0] <- NA
# standard error
ses <- sqrt(meanvar+varmean)
}
else { # ndraws is 1
u <- sort(unique(mark1))
if(any(!(u %in% seq(along=geno1)))) {
newmark1 <- mark1
for(i in seq(along=u))
newmark1[mark1==u[i]] <- i
mark1 <- newmark1
}
means <- tapply(pheno, mark1, mean, na.rm = TRUE)
if(var.flag == "group") { # use group variance
ses <- tapply(pheno, mark1, function(a) sd(a, na.rm = TRUE)/sqrt(sum(!is.na(a))))
}
else { # use pooled variance
mark1.factor <- factor(mark1, seq(along=geno1))
lm.tmp <- lm(pheno~mark1.factor-1)
rss <- sum(lm.tmp$residuals^2)
ses <- tapply(mark1, mark1, function(a) sqrt(rss/nind/length(a)))
}
}
}
else { # with mark2
if(ndraws > 1) {
mark1.level <- seq(along=geno1) # level for mark1
mark2.level <- seq(along=geno2) # level for mark2
u <- sort(unique(as.numeric(mark1)))
if(any(!(u %in% seq(along=geno1)))) {
newmark1 <- mark1
for(i in seq(along=u))
for(j in 1:ncol(mark2))
newmark1[mark1[,j]==u[i],j] <- i
mark1 <- newmark1
}
u <- sort(unique(as.numeric(mark2)))
if(any(!(u %in% seq(along=geno2)))) {
newmark2 <- mark2
for(i in seq(along=u))
for(j in 1:ncol(mark2))
newmark2[mark2[,j]==u[i],j] <- i
mark2 <- newmark2
}
# init
means.all <- array(NA, c(length(mark1.level), length(mark2.level), ndraws))
dimnames(means.all) <- list(mark1.level, mark2.level, NULL)
vars.all <- array(NA, c(length(mark1.level), length(mark2.level), ndraws))
dimnames(vars.all) <- list(mark1.level, mark2.level, NULL)
weight <- rep(0, ndraws) # weight for draws
# loop thru draws
for(i in 1:ndraws) {
mark1.tmp <- mark1[,i] # data for current draw
mark2.tmp <- mark2[,i]
# fit a regression - this is used to calculate the weights
mark1.factor <- factor(mark1.tmp, mark1.level)
mark2.factor <- factor(mark2.tmp, mark2.level)
lm.tmp <- lm(pheno~mark1.factor+mark2.factor+1)
rss <- sum(lm.tmp$residuals^2)
# compute the weight
weight[i] <- (-nind/2)*log(rss)
# group means
means.tmp <- tapply(pheno, list(mark1.tmp, mark2.tmp), mean, na.rm = TRUE)
# calculate group means and variances
if(var.flag=="group") { # use variance in each group
vars.tmp <- tapply(pheno, list(mark1.tmp,mark2.tmp),
function(a) var(a,na.rm = TRUE)/length(a))
}
else { # use pooled variance
vars.tmp <- tapply(mark1.tmp, list(mark1.tmp,mark2.tmp),
function(a) rss/nind/length(a))
}
# note that there could be missing categories in draws
means.all[dimnames(means.tmp)[[1]], dimnames(means.tmp)[[2]],i] <- means.tmp
vars.all[dimnames(vars.tmp)[[1]], dimnames(vars.tmp)[[2]], i] <- vars.tmp
}
# average across draws - for vars, it should be
# mean of variance plus variance of means
weight <- exp(weight-max(weight))
means <- apply(means.all, c(1,2), function(a) weighted.mean(a,weight,na.rm=TRUE))
meanvar <- apply(vars.all, c(1,2), function(a) weighted.mean(a,weight,na.rm=TRUE))
varmean <- apply(means.all, c(1,2), function(a) weighted.mean((a-mean(a,na.rm=TRUE))^2,weight,na.rm=TRUE)) # var of means
measured <- apply(means.all, c(1,2), function(a) sum(!is.na(a)))
means[measured==0] <- meanvar[measured==0] <- varmean[measured==0] <- NA
# standard error
ses <- sqrt(meanvar+varmean)
}
else { # ndraws is 1
u <- sort(unique(mark1))
if(any(!(u %in% seq(along=geno1)))) {
newmark1 <- mark1
for(i in seq(along=u))
newmark1[mark1==u[i]] <- i
mark1 <- newmark1
}
u <- sort(unique(mark2))
if(any(!(u %in% seq(along=geno2)))) {
newmark2 <- mark2
for(i in seq(along=u))
newmark2[mark2==u[i]] <- i
mark2 <- newmark2
}
mark1 <- factor(mark1, seq(along=geno1))
mark2 <- factor(mark2, seq(along=geno2))
means <- tapply(pheno, list(mark1, mark2), mean, na.rm = TRUE)
if(var.flag=="group") { # use group variance
ses <- tapply(pheno, list(mark1, mark2), function(a) sd(a, na.rm = TRUE)/sqrt(sum(!is.na(a))))
}
else {# use pooled variance
lm.tmp <- lm(pheno~mark1+mark2-1)
rss <- sum(lm.tmp$residuals^2)
ses <- tapply(mark1, list(mark1, mark2), function(a) sqrt(rss/nind/length(a)))
}
}
}
# result
result$Means <- means
result$SEs <- ses
result
}
# end of effectplot.R
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