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updates2022.R
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library(TreeTools)
library(phangorn)
library(progress)
library(RcppArmadillo)
library(rsvd)
library(impute)
library(pheatmap)
library(Rcpp)
#sourceCpp("cppFuncs.cpp"
computeWeights=function(treesObj, plot=T){
if(min(treesObj$paths, na.rm = T)<1e-6){
offset=1e-6
}
else{
offset=0
}
mml=apply(log(treesObj$paths+offset),2,mean, na.rm=T)
varl=apply(log(treesObj$paths+offset),2,var, na.rm=T)
#use spline fitting
l=smooth.spline(x = mml, y = varl, w = (exp(mml)), nknots = 6, all.knots = F, spar=0.25)
f=approxfun(l, rule=2)
if(plot){
plot(mml, varl, xlab="mean log", ylab="var log")
lines(l, lwd=2, col=2)
}
#weights are 1/var where the var is the predicted variance
matrix(1/(f(log(treesObj$paths))), nrow=nrow(treesObj$paths))
}
evalBias=function(x,y,n=20, plot=T){
x=as.vector(x)
y=as.vector(y)
ii=which(!is.na(x)&!is.na(y))
x=x[ii]
y=y[ii]
x=cut(rank(x), breaks = n)
res=boxplot(y~x, outline=F, plot=plot)
thisMed=res$stats[3,]
thisSpread=res$stats[4,]-res$stats[2,]
intStats=mean(thisMed^2)*100
medStats=summary(lm(thisMed~1+as.vector(1:20)))$coef[2,4]
spreadStats=summary(lm(thisSpread~1+as.vector(1:20)))$coef[2,4]
allstats=round(c(intStats,-log10(c(medStats, spreadStats))),2)
names(allstats)=c("medianInt", "medianSlope", "spreadSlope")
if(plot){
abline(h=0, col=2, lwd=2)
title(paste(paste(names(allstats), allstats), collapse=", "))
}
return(allstats)
}
evalResid=function( residOut, treesObj, maxUse=5000, weighted=F, plot=F){
if(weighted){
rr=getRMat(residOut, weights = treesObj$weights, use.rows = 1:maxUse)
}
else{
rr=getRMat(residOut, use.rows = 1:maxUse)
}
evalBias(treesObj$paths[1:maxUse,], rr[1:maxUse,], plot=plot)
}
copyMat=function(mat, names=T){
newmat=matrix(nrow=nrow(mat), ncol=ncol(mat))
if(names){
rownames(newmat)=rownames(mat)
colnames(newmat)=colnames(mat)
}
newmat
}
allPaths=function(tree, needIndex=T){
pres=PathLengths(tree)
allD=pres[,3]
nA=length(tree$tip.label)+tree$Nnode
if(needIndex){
matIndex=matrix(nrow=nA, ncol=nA)
for( j in 1:nrow(pres)){
matIndex[pres[j,2], pres[j,1]]=j
}
return(list(dist=allD, nodeId=pres[,c(2,1)], matIndex=matIndex))
}
else{
return(list(dist=allD, nodeId=pres[,c(2,1)]))
}
}
matchAllnodes=function(tree, masterTree){
index = KeptVerts(masterTree, TipLabels(masterTree) %in% tree$tip.label)
key = which(index)
map = cbind(seq_along(key), key)
map
}
edgeIndexRelativeMaster=function(tree, masterTree){
map=matchAllnodes(tree,masterTree)
newedge=tree$edge
newedge[,1]=map[newedge[,1],2]
newedge[,2]=map[newedge[,2],2]
newedge
}
allPathsMasterRelative=function(tree, masterTree, masterTreePaths=NULL){
if(! is.list(masterTreePaths)){
masterTreePaths=allPaths(masterTree)
}
treePaths=allPaths(tree, needIndex = F)
map=matchAllnodes(tree,masterTree)
#remap the nodes
treePaths$nodeId[,1]=map[treePaths$nodeId[,1],2 ]
treePaths$nodeId[,2]=map[treePaths$nodeId[,2],2 ]
#ii=masterTreePaths$matIndex[(treePaths$nodeId[,2]-1)*nrow(masterTreePaths$matIndex)+treePaths$nodeId[,1]]
ii=masterTreePaths$matIndex[cbind(treePaths$nodeId[,1],treePaths$nodeId[,2])]
vals=double(length(masterTreePaths$dist))
vals[]=NA
vals[ii]=treePaths$dist
vals
}
readTrees=function(file, max.read=NA, masterTree=NULL){
tmp=scan(file, sep="\t", what="character")
trees=vector(mode = "list", length = min(length(tmp)/2,max.read, na.rm = T))
treenames=character()
maxsp=0; # maximum number of species
for ( i in 1:min(length(tmp),max.read*2, na.rm = T)){
if (i %% 2==1){
treenames=c(treenames, tmp[i])
}
else{
trees[[i/2]]=unroot(read.tree(text=tmp[i]))
#check if it has more species
if(length(trees[[i/2]]$tip.label)>maxsp){
maxsp=length(trees[[i/2]]$tip.label)
allnames=trees[[i/2]]$tip.label
}
}
}
names(trees)=treenames
treesObj=vector(mode = "list")
treesObj$trees=trees
treesObj$numTrees=length(trees)
treesObj$maxSp=maxsp
message(paste("max is ", maxsp))
report=matrix(nrow=treesObj$numTrees, ncol=maxsp)
colnames(report)=allnames
rownames(report)=treenames
for ( i in 1:nrow(report)){
ii=match(allnames, trees[[i]]$tip.label)
report[i,]=1-is.na(ii)
}
treesObj$report=report
if(is.null(masterTree)){
ii=which(rowSums(report)==maxsp)
#Create a master tree with no edge lengths
master=trees[[ii[1]]]
master$edge.length[]=1
}
else{
master=masterTree
}
master=Preorder(master)
treesObj$masterTree=master
for ( i in 1:treesObj$numTrees){
treesObj$trees[[i]]=RenumberTips(treesObj$trees[[i]], master$tip.label)
treesObj$trees[[i]]=Preorder(treesObj$trees[[i]])
}
ap=allPaths(master)
treesObj$ap=ap
matAnc=(ap$matIndex>0)+1-1
matAnc[is.na(matAnc)]=0
paths=matrix(nrow=treesObj$numTrees, ncol=length(ap$dist))
pb <- progress_bar$new(format = "(:spin) [:bar] :percent [Elapsed time: :elapsedfull || Estimated time remaining: :eta]",
total = treesObj$numTrees,
complete = "=", # Completion bar character
incomplete = "-", # Incomplete bar character
current = ">", # Current bar character
clear = FALSE, # If TRUE, clears the bar when finish
width = 100) # Width of the progress bar
for( i in 1:treesObj$numTrees){
pb$tick()
paths[i,]=allPathsMasterRelative(treesObj$trees[[i]], master, ap)
}
# paths=paths+min(paths[paths>0], na.rm=T)
treesObj$paths=paths
treesObj$matAnc=matAnc
treesObj$matIndex=ap$matIndex
treesObj$lengths=unlist(lapply(treesObj$trees, function(x){sqrt(sum(x$edge.length^2))}))
ii=which(rowSums(report)==maxsp)
if(length(ii)>20){
message (paste0("estimating master tree branch lengths from ", length(ii), " genes"))
tmp=lapply( treesObj$trees[ii], function(x){x$edge.length})
allEdge=matrix(unlist(tmp), ncol=2*maxsp-3, byrow = T)
allEdge=scaleMat(allEdge)
allEdgeM=apply(allEdge,2,mean)
treesObj$masterTree$edge.length=allEdgeM
}
else{
message("Not enough genes with all species present: master tree has no edge.lengths")
}
message("Naming paths")
for(i in ncol(treesObj$paths)){
tip=which(ap$matIndex==i, arr.ind = T)[,1]
if(tip<=maxsp){
colnames(treesObj$paths)=masterTree$tip.label[tip]
}
}
message("Done!")
class(treesObj)=append(class(treesObj), "treesObj")
treesObj
}
scaleMat=function(mat){t(apply(mat,1,scaleDist))}
scaleDist=function(x, na.rm=T){
x/sqrt(sum(x^2))
}
transformMat=function(tree){
nA=length(tree$tip.label)+tree$Nnode
matIndex=matrix(nrow=nA, ncol=nA)
mat=matrix(nrow=nrow(tree$edge), ncol=0)
index=1
for ( i in 1:nA){
ia=getAncestors(tree,i)
if(length(ia)>0){
thisindex=double()
ia=c(i,ia)
for (k in 2:length(ia)){
j=ia[k]
thisindex=c(thisindex, which(tree$edge[,2]==ia[k-1]&tree$edge[,1]==ia[k]))
vals=rep(0, nrow(mat))
vals[thisindex]=1
mat=cbind(mat, vals)
}
}
}
mat
}
# internal
getAncestors=function(tree, nodeN){
if(is.character(nodeN)){
nodeN=which(tree$tip.label==nodeN)
}
im=which(tree$edge[,2]==nodeN)
ianc=tree$edge[im,1]
ancVec=double()
while(length(ianc)>0){
ancVec=c(ancVec,ianc)
im=which(tree$edge[,2]==ianc)
ianc=tree$edge[im,1]
}
ancVec
}
getLogPaths=function(treesObj){
set.seed(1); iis=sample(nrow(treesObj$paths),500)
tmpdat=treesObj$paths[iis,]
mval=min(tmpdat[tmpdat>0], na.rm = T)
log(treesObj$paths+mval)
}
#various transformation functions, using log or arc hyperbolic sine or square root
transformData=function(x, transform=c("log", "asinh")){
if(transform=="log"){
if(length(x)>10000){
set.seed(1)
iis=sample(length(x), 10000)
tmpdat=as.vector(x[iis])
mval=min(tmpdat[tmpdat>0], na.rm = T)
}
else{
mval=min(x[x>0], na.rm=T)
}
log(x+mval)
}
else{
asinh(x)
}
}
transformPaths=function(treesObj, transform="log", impute=T){
transform=match.arg(transform, c("sqrt", "log", "none"))
if(transform=="log"){
set.seed(1); iis=sample(nrow(treesObj$paths),500)
tmpdat=treesObj$paths[iis,]
mval=min(tmpdat[tmpdat>0], na.rm = T)
treesObj$paths= log(treesObj$paths+mval)
treesObj$logOffset=mval
treesObj$transform="log"
}
else if (transform=="sqrt"){
treesObj$paths= sqrt(treesObj$paths)
treesObj$transform="sqrt"
}
else if (transform=="none"){
treesObj$transform="none"
}
if(impute){
set.seed(1);kres=impute.knn(treesObj$paths, colmax = 95)
treesObj$pathsImputed=kres$data
}
treesObj$weights=computeWeightsAllVar(treesObj$paths, transform = "none")
treesObj
}
unlogPaths=function(treesObj){
if(is.null(treesObj$isLogged)){
message("paths are not log transformed")
}
else{
exp(treesOb$paths)-treesObj$logOffset
}
}
getTrimmedAverage=function(x, trim=0.05){
apply(x,2, mean, trim=trim, na.rm=T)
}
getAllResiduals=function(treesObj, nvMod=NULL, n.pcs=0,cutoff=NULL, useSpecies=NULL, min.sp=10, min.valid=20, doOnly=NULL, maxT=NULL, block.do=F, weights=NULL, do.loess=F, family="gaussian", span=0.7, interaction=F){
if (is.null(cutoff)) {
cutoff = quantile(treesObj$paths, 0.05, na.rm = T)
message(paste("cutoff is set to", cutoff))
}
if(is.null(treesObj$pathsImputed) && n.pcs>0){
message("PC normalization not supported without imputation\n
Run transformPaths with imputation or set n.pcs=0")
return()
}
if(block.do){
pStr=apply(treesObj$report, 1, paste, collapse="")
uClust=unique(pStr)
blockId=match(pStr, uClust)
}
tPaths= treesObj$paths
if(is.null(nvMod)){
if(n.pcs>0){
# dimensionality reduction
svdres=rsvd(treesObj$pathsImputed, k=n.pcs+1)
if(!interaction){
nvMod=model.matrix(~1+svdres$v[, 1:n.pcs])
}
else{
nvMod=model.matrix(~1+.^2, data = as.data.frame(svdres$v[, 1:n.pcs]))
}
}
else {
nvAve=apply(tPaths,2, mean, trim=0.05, na.rm=T)
nvMod=model.matrix(~1+nvAve)
}
}
if(any(is.na(nvMod))){
message("NA values in model: something is wrong")
stop()
}
if(ncol(nvMod)>5 && do.loess){
message("Model is too big for loess. Set n.pcs<=4 or n.pcs<=2 if interaction=T")
stop()
}
if (is.null(useSpecies)){
useSpecies=treesObj$masterTree$tip.label
mappedEdges=treesObj$mappedEdges
}
if(is.null(maxT)){
maxT=treesObj$numTrees
}
if(is.null(weights)){
weigths=treesObj$weights
}
#useSpecies has to exist in masterTree
useSpecies=intersect(treesObj$masterTree$tip.label, useSpecies)
#maximum number of present species
maxSpecies=rowSums(treesObj$report[,useSpecies])
#this will hold the predictions
preds=copyMat(tPaths)
preds[]=NA
if(is.null(doOnly)){
doOnly=1
}
else{
maxT=1
}
isDone=vector("logical", nrow(preds))
isDone[]=F
#this will hold the indecies used to construct the regression
#which is a subset of the indecies for predictions
modelIndexList=vector("list", nrow(preds))
pb <- progress_bar$new(format = "(:spin) [:bar] :percent [Elapsed time: :elapsedfull || Estimated time remaining: :eta]",
total = treesObj$numTrees,
complete = "=", # Completion bar character
incomplete = "-", # Incomplete bar character
current = ">", # Current bar character
clear = FALSE, # If TRUE, clears the bar when finish
width = 100) # Width of the progress bar
for (i in doOnly:(doOnly+maxT-1)){
pb$tick()
if(!isDone[i]){
#get the ith tree
tree1=treesObj$trees[[i]]
#get the common species, prune and unroot
thisUseSpecies=intersect(tree1$tip.label, useSpecies)
if(length(thisUseSpecies)<min.sp){
next
}
#find all the genes that that whose maximal species set is the same as tree1
if(block.do){
# thisMaxSpecies=rowSums(treesObj$report[,thisUseSpecies])
# iido=which(maxSpecies==thisMaxSpecies&thisMaxSpecies==length(thisUseSpecies))
iido=which(blockId==blockId[i])
}
else{
iido=i
}
ee=edgeIndexRelativeMaster(tree1, treesObj$masterTree)
#these are the indecies for the paths that span the tree
#these are used to build the regression
iiPaths= treesObj$matIndex[ee[, c(2,1)]]
#extract the tPaths and corresponding weights
allbranch=tPaths[iido,iiPaths,drop=F]
allbranchw=treesObj$weights[iido,iiPaths, drop=F]
iibad=which(tPaths[iido, iiPaths, drop=F]<cutoff)
allbranch[iibad]=NA
iidoAll=iido
sumCanUse=rowSums(!is.na(allbranch))
iigood=which(sumCanUse>=min.valid)
if(length(iigood)<1){
next
}
allbranch=allbranch[iigood, ,drop=F]
allbranchw=allbranchw[iigood, ,drop=F]
iido=iido[iigood]
#these are all the indecies that are not NA and thus can get predictions
iiallnotna=which(!is.na(treesObj$paths[iido[1],]))
for(j in 1:length(iido)){
modelIndexList[[iido[j]]]=iiPaths[which(!is.na(allbranch[j,]))]
}
if(!do.loess){
preds[iido,iiallnotna]= fastLmResidMatWeightedPredict(allbranch, nvMod[iiPaths,], allbranchw, nvMod[iiallnotna,])
}
else{ #this is loess
message("loess should not be used since it produces weird results and also cannot predict outside the input range")
stop()
for(id in 1:length(iido)){
lres=loess(allbranch[id,]~nvMod[iiPaths,-1], weights =allbranchw[id,], span=span, family=family)
preds[iido[id],iiallnotna]=predict(lres, nvMod[iiallnotna,-1])
}
}
#plotting function. change to T for plotting functionality
if(F){
plot(preds[iido[1],], tPaths[iido[1],])
abline(a=0,b=1, lwd=3, col=2)
}
isDone[iidoAll]=T
}
}
allresiduals=(tPaths-preds)
rownames(allresiduals)=names(treesObj$trees)
# allresiduals[treesObj$paths<cutoff]=0
return(list(allresiduals=allresiduals, index=modelIndexList, preds=preds, tpaths=tPaths))
}
#' Provides names for paths/RERs representing terminal branches for plotting
#' Originally an internal function but necessary for the vignette/walk-through
#' @param masterTree The master tree used for analysis
#' @return Names corresponding to the paths/RERs for terminal branches
#' @export
namePathsWSpecies=function(masterTree){
mat=transformMat(masterTree)
n=length(masterTree$tip.label)
#these are the tip edges in the master tree
iim=match(1:n, masterTree$edge[,2])
#each column in the mat is composed of at most one tip edge
tip.edge=apply(mat[iim,],2,function(x){if(max(x)>0){which(x==1)} else{NA}})
return(masterTree$tip[tip.edge])
}
flatIndex=function(twocol,n){
(twocol[,2]-1)*n+twocol[,1]
}
myscale=function(x ){
s = apply(x, 2, sd, na.rm=T)
m = apply(x, 2, mean, na.rm=T)
x = sweep(x, 2, m)
x = sweep(x, 2, s, "/")
x
}
getRMat=function(resOut, all=F, weights=NULL, scale=F, use.rows=NULL){
allres=copyMat(resOut$allresiduals)
if(is.null(use.rows)){
use.rows=1:nrow(allres)
}
resIn=resOut$allresiduals
if(!is.null(weights)){
resIn=resIn*sqrt(weights)
}
if(scale){
resIn=myscale(resIn)
}
if(!all){
for(i in use.rows){
ii=resOut$index[[i]]
allres[i,ii]=resIn[i,ii]
}
allres
}
else{
allres=resIn
}
allres
}
win=function(x,w){
xs=sort(x[!is.na(x)], decreasing = T)
xmax=xs[w]
xmin=xs[length(xs)-w+1]
x[x>xmax]=xmax
x[x<xmin]=xmin
x
}
OldgetAllCor=function(RERmat, charP, method="auto",min.sp=10, min.pos=2, winsorize=NULL,weights=NULL){
if (method=="auto"){
lu=length(unique(charP))
if(lu==2){
method="k"
message("Setting method to Kendall")
}
else if (lu<=5){
method="s"
message("Setting method to Spearman")
}
else{
method="p"
message("Setting method to Pearson")
if(is.null(winsorize)){
message("Setting winsorise=3")
}
}
}
corout=matrix(nrow=nrow(RERmat), ncol=3)
rownames(corout)=rownames(RERmat)
colnames(corout)=c("Rho", "N", "P")
if(!is.null(winsorize)){
charP=win(charP, winsorize)
}
for( i in 1:nrow(corout)){
if(((nb<-sum(ii<-(!is.na(charP)&!is.na(RERmat[i,]))))>=min.sp)&sum(charP[ii]!=0)>=min.pos){
if(is.null(weights)){
if (!is.null(winsorize)){
x=win(RERmat[i,], winsorize)
}
else{
x=RERmat[i,]
}
cres=cor.test(x, charP, method=method)
corout[i,1:3]=c(cres$estimate, nb, cres$p.value)
}
else{
cres=wtd.cor(rank(RERmat[i,ii]), rank(charP[ii]), weight = weights[rownames(RERmat)[i],ii], mean1 = T)
corout[i, 1:3]=c(cres[1], nb, cres[4])
}
}
else{
#show(i)
#show(c(nb, charP[ii]))
}
}
as.data.frame(corout)
}
mypr=function(vals, genes, foldChange=T){
ii=match(names(vals), genes)
fg=(!is.na(ii))+1-1
oo=order(-vals)
fgo=fg[oo]
prc=matrix(nrow=length(fgo), ncol=2)
rec=0
for (i in 1:length(fgo)){
if(fgo[i]==1){
rec=rec+1
}
prc[i,1]=rec
prc[i,2]=rec/i
}
prc[,2]=rev(cummax(rev(prc[,2])))
if(foldChange){
message("FoldChange")
bg=sum(fg)/length(vals)
prc[,2]=prc[,2]/bg
colnames(prc)=c("x","y")
}
else{
colnames(prc)=c("x","y")
}
as.data.frame(prc)
}
plotAllPRggplot=function(resAll, genes, subset=1:length(resAll), grp1, grp2=NULL, grp3=NULL, invert=F, sz=1.7){
grp1=as.character(grp1)
if(!is.null(grp2)){
grp2=as.character(grp2)
}
else{
grp2=as.factor(rep(1, length(grp1)))
}
if(!is.null(grp3)){
grp3=as.character(grp3)
}
df=list()
for(i in 1:length(resAll)){
if(i %in% subset){
show(i)
stats=getStat(resAll[[i]])
if(invert){
pr=mypr(-stats, genes)
}
else{
pr=mypr(stats, genes)
}
ii=which(pr$x>=10)
pr=pr[ii,]
df$x=c(df$x, pr$x)
df$y=c(df$y,pr$y)
df$grp1=c(df$grp1, rep(grp1[i], length(pr$x)))
df$grp2=c(df$grp2, rep(grp2[i], length(pr$x)))
df$grp3=c(df$grp3, rep(grp3[i], length(pr$x)))
df$id=c(df$id, rep(i, length(pr$x)))
}
}
df=as.data.frame(df)
# show(df[1:10,])
# show(table(df$grp1))
df$id=as.factor(df$id)
df$grp1=as.factor(df$grp1)
df$grp2=as.factor(df$grp2)
if(length(unique(df$grp2))>1){
p=ggplot(df, aes(x=x, y=y,color=grp1, linetype=grp2, group=id))+geom_line(size=sz)+scale_x_log10()+theme_bw()
}
else{
p=ggplot(df, aes(x=x, y=y,color=grp1, group=id))+geom_line(size=sz)+scale_x_log10()+theme_bw()
}
if(!is.null(grp3)){
p=p+facet_wrap(~grp3)
}
p+theme_bw(base_size = 20)+xlab("recall")+ylab("precision fold change")
}
naresidCPP=function(data, mod, weights=NULL){
# message("CPP")
if(is.null(weights)){
out=fastLmResidMat(data, mod)
}
else{
out=fastLmResidMatWeighted(data,mod, weights)
out=out*sqrt(weights)
}
rownames(out)=rownames(data)
colnames(out)=colnames(data)
out
}
# myRfastNorm=function(x){
# mat <- (x) - Rfast::rowmeans(x)
# mat <- mat/sqrt(Rfast::rowsums(mat^2))
# mat
# }
# mycor=function (x, y){
# if(nrow(x)>100&&nrow(y)>100){
# matx <- myRfastNorm(x)
# maty=myRfastNorm(y)
#
# Rfast::Tcrossprod(matx, maty)
# }
# else cor(t(x), t(y))
# }
computeWeightsAllVarOld=function (mat, nv = NULL, transform = "none", plot = T, predicted = T)
{
if (is.null(nv)) {
nv = apply(mat, 2, mean, na.rm = T, trim = 0.05)
}
transform = match.arg(transform, choices = c("none", "sqrt",
"log"))
if (transform == "log") {
offset = 0
if (min(mat, na.rm = T) < 1e-08) {
offset = min(mat[mat > 1e-08])
}
mat = log(mat + offset)
nv = log(nv + offset)
}
if (transform == "sqrt") {
mat = sqrt(mat)
nv = sqrt(nv)
}
matsub = mat
matr = naresidCPP(matsub, model.matrix(~1 + nv))
matpred = fastLmPredictedMat(matsub, model.matrix(~1 + nv))
mml = as.vector(matsub)
varlall=varl = as.vector(log(matr^2))
ii = which(!is.na(mml))
mml = mml[ii]
varl = varl[ii]
set.seed(123)
iis = sample(length(mml), min(5e+05, length(mml)))
mml = mml[iis]
varl = varl[iis]
l = lowess(mml, varl, f = 0.7, iter = 2)
f = approxfun(l, rule = 2)
if (plot) {
par(mfrow = c(1, 2), omi = c(1, 0, 0, 0))
nbreaks = 20
qq = quantile(mml, seq(0, nbreaks, 1)/nbreaks)
qqdiff = diff(qq)
breaks = qq[1:nbreaks] + qqdiff/2
rr = quantile(mml, c(1e-04, 0.99))
breaks = unique(round(breaks, 3))
nbreaks = length(breaks)
cutres <- cut(mml, breaks = breaks)
cutres_tt = table(cutres)
boxplot((varl) ~ cutres, xlab = "", ylab = "log var",
outline = F, log = "", las = 2)
title("Before")
xx = (qq[1:nbreaks] + breaks)/2
lines(1:length(xx), (f(qq[1:nbreaks])), lwd = 2, col = 2)
}
wr = 1/exp(f(mml))
if (!predicted) {
weights = (matrix(1/exp(f(mat)), nrow = nrow(mat)))
}
else {
weights = (matrix(1/exp(f(matpred)), nrow = nrow(mat)))
}
if (plot) {
matr = naresidCPP(matsub[1:2000,], model.matrix(~1 + nv), weights[1:2000,])
iisub=ii[iis]
iisub=iisub[iisub<=length(matr)]
varl = (as.vector(log(matr^2))[ii])[iis]
boxplot((varl) ~ cutres, ylab = "log var", outline = F,
log = "", main = "After", las = 2)
abline(h = 0, col = "blue3", lwd = 2)
mtext(side = 1, text = "bins", outer = T, line = 2)
}
save(matr, weights, matsub, ii, iis, nv, file="matrOld.Rdata")
weights
}
naresid=function(data, X, weights=NULL, covar=NULL, numiter=0, useZero=F){
if(is.vector(X)){
mod=model.matrix(~1+as.vector(X));
}
else{
mod=X
}
#show(mod)
resid=matrix(nrow=nrow(data), ncol=ncol(data))
if(useZero){
resid[]=0;
}
else{
resid[]=NA;
}
for ( i in 1:nrow(data)){
# show(i)
ii=which(!is.na(data[i,]))
if(length(ii)>2){
iiinv=which(is.na(data[i,]))
dat=data[i,ii,drop=F]
modtmp=mod[ii,]
if(!is.null(weights)){
W=diag(weights[i,ii])
}
else{
W=NULL
}
if (!is.null(covar)){
if(!is.null(W)){
W=W%*%covar
}
else{
W=covar
}
}
n=dim(dat)[2]
Id=diag(n)
if(!is.null(W)){
# message("here")
coeff=dat%*%W%*%modtmp %*% solve(t(modtmp) %*% W %*% modtmp)
#check there is no error with lm
# lmres=lm(t(dat)~0+modtmp, weights = diag(W))
# show(coeff)
# show(coefficients(lmres))
resid[i, ii] = dat -(coeff %*% t(modtmp))
resid[i, ii]=resid[i,ii]*sqrt(diag(W))
if(numiter>0){
for(iter in 1:numiter){
ii.use=isNotOutlier(resid[i,], quant)
Wq=diag(weights[i, ii.use])
modq=mod[ii.use,]
coeff=dat[i, ii.use]%*%Wq%*%modq %*% solve(t(modq) %*% Wq %*% modq)
resid[i, ] = dat[i,] -(coeff %*% t(mod))
resid[i, ]=resid[i,]*sqrt(diag(W))
}
}
}
else{
coeff=dat%*%modtmp %*% solve(t(modtmp) %*% modtmp)
#check there is no error with lm
# lmres=lm(t(dat)~0+modtmp)
# show(coeff)
# show(coefficients(lmres))
resid[i, ii] = dat -(coeff %*% t(modtmp))
}
}
else{
# message("Cannot compute residuals")
}
}
rownames(resid)=rownames(data)
colnames(resid)=colnames(data)
resid
}
plotAsBoxMeanVar=function(mat,matr){
mml = as.vector(mats)
varl = as.vector(log(matr^2))
ii = which(!is.na(mml))
mml = mml[ii]
varl = varl[ii]
set.seed(123)
iis = sample(length(mml), min(5e+05, length(mml)))
mml = mml[iis]
varl = varl[iis]
plotAsBox(mml, varl, 20)