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abcAsso.cpp
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abcAsso.cpp
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/*
thorfinn thorfinn@binf.ku.dk dec17 2012
has modified so no sites[].chromo etc are used
anders albrecht@binf.ku.dk made this.
logistic regression changed 612new/613
part of angsd
*/
#include <cmath>
#include <zlib.h>
#include <htslib/kstring.h>
#include "shared.h"
#include "analysisFunction.h"
#include "abcFreq.h"
#include "abcAsso.h"
void abcAsso::printArg(FILE *argFile){
fprintf(argFile,"-------------\n%s:\n",__FILE__);
fprintf(argFile,"\t-doAsso\t%d\n",doAsso);
fprintf(argFile,"\t1: Frequency Test (Known Major and Minor)\n");
fprintf(argFile,"\t2: Score Test\n");
fprintf(argFile," Frequency Test Options:\n");
fprintf(argFile,"\t-yBin\t\t%s\t(File containing disease status)\t\n\n",yfile);
fprintf(argFile," Score Test Options:\n");
fprintf(argFile,"\t-yBin\t\t%s\t(File containing disease status)\n",yfile);
fprintf(argFile,"\t-yQuant\t\t%s\t(File containing phenotypes)\n",yfile);
fprintf(argFile,"\t-minHigh\t%d\t(Require atleast minHigh number of high credible genotypes)\n",minHigh);
fprintf(argFile,"\t-minCount\t%d\t(Require this number of minor alleles, estimated from MAF)\n",minCount);
fprintf(argFile,"\t-cov\t\t%s\t(File containing additional covariates)\n",covfile);
fprintf(argFile,"\t-model\t%d\n",model);
fprintf(argFile,"\t1: Additive/Log-Additive (Default)\n");
fprintf(argFile,"\t2: Dominant\n");
fprintf(argFile,"\t3: Recessive\n\n");
fprintf(argFile,"Examples:\n\tPerform Frequency Test\n\t \'./angsd -yBin pheno.ybin -doAsso 1 -GL 1 -out out -doMajorMinor 1 -minLRT 24 -doMaf 2 -doSNP 1 -bam bam.filelist'\n");
fprintf(argFile,"\tPerform Score Test\n\t \'./angsd -yBin pheno.ybin -doAsso 2 -GL 1 -doPost 1 -out out -doMajorMinor 1 -minLRT 24 -doMaf 2 -doSNP 1 -bam bam.filelist'\n");
fprintf(argFile,"\n");
}
void abcAsso::getOptions(argStruct *arguments){
doAsso=angsd::getArg("-doAsso",doAsso,arguments);
if(doAsso==3){
fprintf(stderr,"\t-> -doAsso 3 is deprecated from version 0.615 \n");
exit(0);
}
if(doAsso==0)
return;
doMaf=angsd::getArg("-doMaf",doMaf,arguments);
adjust=angsd::getArg("-adjust",adjust,arguments);
model=angsd::getArg("-model",model,arguments);
minCov=angsd::getArg("-minCov",minCov,arguments);
dynCov=angsd::getArg("-dynCov",dynCov,arguments);
minHigh=angsd::getArg("-minHigh",minHigh,arguments);
doPrint=angsd::getArg("-doPrint",doPrint,arguments);
minCount=angsd::getArg("-minCount",minCount,arguments);
sitePerm=angsd::getArg("-sitePerm",sitePerm,arguments);
GL=angsd::getArg("-GL",GL,arguments);
covfile=angsd::getArg("-cov",covfile,arguments);
doPost=angsd::getArg("-doPost",doPost,arguments);
yfile=angsd::getArg("-yBin",yfile,arguments);
if(yfile!=NULL)
isBinary=1;
yfile=angsd::getArg("-yQuant",yfile,arguments);
if(doPrint)
fprintf(stderr,"finished [%s]\t[%s]\n",__FILE__,__FUNCTION__);
if(doPrint)
fprintf(stderr,"staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
if(doAsso && doMaf==0){
fprintf(stderr,"Error: you must estimate the maf (-doMaf) in order to perform association \n");
exit(0);
}
if(doAsso && yfile==NULL){
fprintf(stderr,"Error: you must provide a phenotype file (-yBin or -yQuant) to perform association \n");
exit(0);
}
if(doAsso && arguments->inputtype==INPUT_BEAGLE&&doAsso==1){
fprintf(stderr,"Error: Only doAsso=2 can be performed on posterior input\n");
exit(0);
}
if(doAsso && arguments->inputtype!=INPUT_BEAGLE&&(doAsso==2)&&doPost==0){
fprintf(stderr,"Error: For doAsso=2 you must estimate the posterior probabilites for the genotypes (-doPost 1) \n");
exit(0);
}
}
abcAsso::abcAsso(const char *outfiles,argStruct *arguments,int inputtype){
multiOutfile=NULL;
bufstr.s=NULL;bufstr.l=bufstr.m=0;
model=1;
doPrint=0;
doAsso=0;
GL=0;
doPost=0;
isBinary=0;
sitePerm=0;//not for users
covfile=NULL;
yfile=NULL;
minHigh=10;
minCount=10;
dynCov=0;//not for users
minCov=5;//not for users
adjust=1;//not for users
doMaf=0;
//from command line
if(arguments->argc==2){
if(!strcasecmp(arguments->argv[1],"-doAsso")){
printArg(stdout);
exit(0);
}else
return;
}
getOptions(arguments);
if(doAsso==0){
shouldRun[index] =0;
return;
}
printArg(arguments->argumentFile);
//read phenotype
if(isBinary)
ymat = angsd::getMatrix(yfile,1,100000);
else
ymat = angsd::getMatrix(yfile,0,100000);
//read covariates
if(covfile!=NULL)
covmat = angsd::getMatrix(covfile,0,100000);
else{
covmat.x=0;
covmat.y=0;
covmat.matrix=NULL;
}
if(covfile!=NULL&&(covmat.x!=ymat.x)){
fprintf(stderr,"The number of covariates (%d) does not match the number of phenotypes (%d)\n",covmat.x,ymat.x);
exit(0);
}
if(!isBinary&&doAsso==1){
fprintf(stderr,"Error: Only doAsso=2 can be performed on quantitative traits\n");
exit(0);
}
//make output files
multiOutfile = new BGZF*[ymat.y];
const char* postfix;
postfix=".lrt";
if(covfile!=NULL&&minCov>0){
int keepList[ymat.x];
for(int i=0 ; i < ymat.x;i++) {
keepList[i]=1;
for(int yi=0;yi<ymat.y;yi++) {
if(ymat.matrix[i][yi]==-999)
keepList[i]=0;
}
for(int ci=0;ci<covmat.y;ci++) {
if(covmat.matrix[i][ci]==-999)
keepList[i]=0;
}
}
int nCov=0;
int count[covmat.y];
for(int ci=0;ci<covmat.y;ci++) {
count[ci]=0;
for(int i=0 ; i < ymat.x;i++) {
if(keepList[i]==0)
continue;
if(covmat.matrix[i][ci]!=0){
count[ci]++;
}
}
if(count[ci]<minCov){
fprintf(stderr,"Error: Cov #%d only has %d non zero entries\n",ci,count[ci]);
}
else
nCov++;
}
if(!dynCov&&covmat.y!=nCov){
fprintf(stderr,"Error: Creating new covariant matrix with %d columns\n",nCov);
exit(0);
}
else if(covmat.y!=nCov){
// angsd::printMatrix(covmat,stderr);
fprintf(stderr,"Error: Creating new covariant matrix with %d columns\n",nCov);
angsd::Matrix<double> newmat;
newmat.x=covmat.x;
newmat.y=nCov;
newmat.matrix=new double*[covmat.x];
for(int xi=0;xi<covmat.x;xi++){
newmat.matrix[xi] = new double[nCov];
int tempCount=0;
for(int ci=0;ci<covmat.y;ci++){
if(count[ci]>minCov){
newmat.matrix[xi][tempCount]=covmat.matrix[xi][ci];
tempCount++;
}
}
}
angsd::deleteMatrix(covmat);
covmat=newmat;
}
}
//open outfiles
for(int i=0;i<ymat.y;i++){
char ary[5000];
snprintf(ary,5000,"%s%d.gz",postfix,i);
multiOutfile[i] = NULL;
multiOutfile[i] = aio::openFileBG(outfiles,ary);
}
//print header
bufstr.l=0;
if(doAsso==2)
ksprintf(&bufstr,"Chromosome\tPosition\tMajor\tMinor\tFrequency\tN\tLRT\thigh_WT/HE/HO\n");
else
ksprintf(&bufstr,"Chromosome\tPosition\tMajor\tMinor\tFrequency\tLRT\n");
for(int yi=0;yi<ymat.y;yi++)
aio::bgzf_write(multiOutfile[yi],bufstr.s,bufstr.l);
bufstr.l=0;
}
abcAsso::~abcAsso(){
if(doPrint)
fprintf(stderr,"staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
if(doAsso==0)
return;
for(int i=0;i<ymat.y;i++)
if(multiOutfile[i]!=NULL)
bgzf_close(multiOutfile[i]);
delete [] multiOutfile;
if(covfile!=NULL)
angsd::deleteMatrix(covmat);
angsd::deleteMatrix(ymat);
}
void abcAsso::clean(funkyPars *pars){
if(doAsso==0)
return;
assoStruct *assoc =(assoStruct*) pars->extras[index];
if(assoc->stat!=NULL)
for(int yi=0;yi<ymat.y;yi++)
delete[] assoc->stat[yi];
delete[] assoc->stat;
delete[] assoc->highWt;
delete[] assoc->highHe;
delete[] assoc->highHo;
if(assoc->keepInd!=NULL)
for( int yi =0;yi<ymat.y;yi++)
delete[] assoc->keepInd[yi];
delete[] assoc->keepInd;
delete assoc;
}
void abcAsso::print(funkyPars *pars){
if(doAsso==0)
return;
if(doPrint)
fprintf(stderr,"staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
printDoAsso(pars);
}
assoStruct *allocAssoStruct(){
assoStruct *assoc = new assoStruct;
assoc->stat=NULL;
assoc->keepInd=NULL;
assoc->highWt = NULL;
assoc->highHe = NULL;
assoc->highHo = NULL;
return assoc;
}
void abcAsso::run(funkyPars *pars){
if(doAsso==0)
return;
assoStruct *assoc = allocAssoStruct();
pars->extras[index] = assoc;
if(doAsso==1){
frequencyAsso(pars,assoc);
}
else if(doAsso==2){
assoc->highWt=new int[pars->numSites];
assoc->highHe=new int[pars->numSites];
assoc->highHo=new int[pars->numSites];
scoreAsso(pars,assoc);
}
}
void abcAsso::frequencyAsso(funkyPars *pars,assoStruct *assoc){
if(doPrint)
fprintf(stderr,"staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
if(pars->nInd!=ymat.x){
fprintf(stderr,"The number of sequenced individuals (%d) does not match the number of phenotypes (%d)\n",pars->nInd,ymat.x);
fflush(stderr);
exit(0);
}
if(ymat.y!=1){
fprintf(stderr,"Only one phenotype allowed for -doAsso 1\n");
fflush(stderr);
exit(0);
}
double **stat = new double*[ymat.y];
for(int yi=0;yi<ymat.y;yi++)
stat[yi] = new double[pars->numSites];
int y[pars->nInd];
for(int i=0;i<pars->nInd;i++)
y[i]=ymat.matrix[i][0];
double **like0;//genotype likelihood for controls
double **like1;//genotype likelihood for cases
double **likeAll;//genotype likelihood for cases and controls
int Ncases=0; //number of cases
int Ncontrols=0; //number of cases
int Nall=0; //number of cases and controls
int cases[pars->nInd];
int controls[pars->nInd];
int all[pars->nInd];
for(int i=0;i<pars->nInd;i++){
cases[i]=0;
controls[i]=0;
all[i]=0;
if((int)y[i]==1){
Ncases++;
cases[i]=1;
Nall++;
all[i]=1;
}
if((int)y[i]==0){
Ncontrols++;
controls[i]=1;
Nall++;
all[i]=1;
}
if(doPrint)
fprintf(stderr,"all, case, control: %d %d %d\n",all[i],cases[i],controls[i]);
}
if(doPrint)
fprintf(stderr,"count complete [%s]\t[%s]\n",__FILE__,__FUNCTION__);
if(doAsso==1){
like0=angsd::get3likesRMlow(pars,controls);
like1=angsd::get3likesRMlow(pars,cases);
likeAll=angsd::get3likesRMlow(pars,all);
}
if(doPrint)
fprintf(stderr,"like complete [%s]\t[%s]\n",__FILE__,__FUNCTION__);
for(int s=0;s<pars->numSites;s++){//loop overs sites
stat[0][s]=-999;
if(pars->keepSites[s]==0)
continue;
if(doAsso==1){
if(doPrint)
fprintf(stderr,"do freq [%s]\t[%s]\n",__FILE__,__FUNCTION__);
double score0=abcFreq::likeFixedMinor(abcFreq::emFrequency_ext(like0[s],Ncontrols,NULL,Ncontrols),like0[s],Ncontrols);
//likelihood for the cases
double score1=abcFreq::likeFixedMinor(abcFreq::emFrequency_ext(like1[s],Ncases,NULL,Ncases),like1[s],Ncases);
//likelihood for all individuals
double scoreNull=abcFreq::likeFixedMinor(abcFreq::emFrequency_ext(likeAll[s],Nall,NULL,Nall),likeAll[s],Nall);
//likelhood ratio statistics \sim chi^2
double LRT=-2*(score0+score1-scoreNull);
stat[0][s]=LRT;
}
}
assoc->stat=stat;
for(int s=0;s<pars->numSites;s++){
delete[] like0[s];
delete[] like1[s];
delete[] likeAll[s];
}
delete[] like0;
delete[] like1;
delete[] likeAll;
if(doPrint)
fprintf(stderr,"finish [%s]\t[%s]\n",__FILE__,__FUNCTION__);
}
void abcAsso::scoreAsso(funkyPars *pars,assoStruct *assoc){
if(doPrint)
fprintf(stderr,"staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
if(pars->nInd!=ymat.x){
fprintf(stderr,"The number of sequenced individuals (%d) does not match the number of phenotypes (%d)\n",pars->nInd,ymat.x);
exit(0);
}
int **keepInd = new int*[ymat.y];
double **stat = new double*[ymat.y];
for(int yi=0;yi<ymat.y;yi++){
stat[yi] = new double[pars->numSites];
keepInd[yi]= new int[pars->numSites];
}
for(int s=0;s<pars->numSites;s++){//loop overs sites
if(pars->keepSites[s]==0)
continue;
int *keepListAll = new int[pars->nInd];
for(int i=0 ; i<pars->nInd ;i++){
keepListAll[i]=1;
}
for(int yi=0;yi<ymat.y;yi++) { //loop over phenotypes
int *keepList = new int[pars->nInd];
keepInd[yi][s]=0;
for(int i=0 ; i<pars->nInd ;i++) {
keepList[i]=1;
if(keepListAll[i]==0||ymat.matrix[i][yi]==-999)
keepList[i]=0;
if(covfile!=NULL)
for(int ci=0;ci<covmat.y;ci++) {
if(covmat.matrix[i][ci]==-999)
keepList[i]=0;
}
if(keepList[i]==1)
keepInd[yi][s]++;
}
double *y = new double[pars->nInd];
for(int i=0 ; i<pars->nInd ;i++)
y[i]=ymat.matrix[i][yi];
freqStruct *freq = (freqStruct *) pars->extras[6];
stat[yi][s]=doAssociation(pars,pars->post[s],y,keepInd[yi][s],keepList,freq->freq[s],s,assoc);
//cleanup
delete [] y;
delete [] keepList;
} //phenotypes end
delete [] keepListAll;
} // sites end
assoc->stat=stat;
assoc->keepInd=keepInd;
}
int abcAsso::getFit(double *res,double *Y,double *covMatrix,int nInd,int nEnv){
/*
linear regression. Fits a linear model.
res is the predicted values (eta) = X%*%coef
Y is the responce
covMatrix is the design matrix (nInd x nEnv)
nInd is the number of individuals
nEnv is the number of predictors (including the intersept)
*/
if(doPrint)
fprintf(stderr,"staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
double Xt_y[nEnv];
double invXtX_Xt_y[nEnv];
for(int i=0;i<nEnv;i++)
Xt_y[i]=0;
for(int i=0;i<nEnv;i++)
invXtX_Xt_y[i]=0;
//get t(X)%*%y
for(int x=0;x<nEnv;x++)//col X
for(int i=0;i<nInd;i++)
Xt_y[x]+=covMatrix[x*nInd+i]*Y[i];
//get inv(t(X)%*%X)
double XtX[nEnv*nEnv];
for(int i=0;i<nEnv*nEnv;i++)
XtX[i]=0;
for(int x=0;x<nEnv;x++)//col X
for(int y=0;y<nEnv;y++)//row Xt
for(int i=0;i<nInd;i++)
XtX[x*nEnv+y]+=covMatrix[y*nInd+i]*covMatrix[x*nInd+i];
// double workspace[2*nEnv];
// angsd::matinv(XtX, nEnv, nEnv, workspace);
int singular=angsd::svd_inverse(XtX,nEnv,nEnv);
if(singular)
return 1 ;
//get (inv(t(X)%*%X))%*%(t(X)%*%y) //this is the coef!
for(int x=0;x<nEnv;x++)//col X
for(int y=0;y<nEnv;y++)//row Xt
invXtX_Xt_y[x]+=XtX[y*nEnv+x]*Xt_y[y];
//get X%*%(inv(t(X)%*%X))%*%(t(X)%*%y)
for(int j=0;j<nInd;j++)//row Xt
res[j]=0;
for(int j=0;j<nInd;j++)//row Xt
for(int x=0;x<nEnv;x++)
res[j]+=covMatrix[x*nInd+j]*invXtX_Xt_y[x];
return 0;
}
int abcAsso::getFitBin(double *res,double *Y,double *covMatrix,int nInd,int nEnv){
double tol = 1e-6;
/*
logistic regression. Fits a logistic regression model.
res is the estimated coefficients
Y is the responce
covMatrix is the design matrix (nInd x nEnv)
nInd is the number of individuals
nEnv is the number of predictors (including the intersept)
// R code
getFitBin<-function(y,X){
b<-rep(0,ncol(X))#estimates
for(i in 1:20){
eta <- as.vector(1/(1+exp(-(X%*%b))))
change<-solve(t(X*eta*(1-eta)) %*% X ) %*% t(X) %*% ( y - eta )
b<-b+ change
if(sum(abs(change))<1e-6)
break
}
b
}
///////
coef<-getFitBin(y,X)
yTilde <- sigm(X%*%coef)
*/
if(doPrint)
fprintf(stderr,"staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
double coef[nEnv];
double eta[nInd];
double Xt_y[nEnv];
double invXtX_Xt_y[nEnv];
double XtX[nEnv*nEnv];
for(int x=0;x<nEnv;x++)//col X
coef[x]=0;
for(int iter=0;iter<100;iter++){
//set to zero
for(int x=0;x<nEnv;x++){
Xt_y[x]=0;
invXtX_Xt_y[x]=0;
for(int y=0;y<nEnv;y++)
XtX[x*nEnv + y]=0;
}
for(int i=0;i<nInd;i++){
eta[i]=0;
}
//eta <- 1/(1+exp(-(X%*%b)))
for(int i=0;i<nInd;i++){
for(int x=0;x<nEnv;x++)//col X
eta[i]+=coef[x]*covMatrix[x*nInd+i];
eta[i] = 1.0/(1+exp(-eta[i]));
}
//get t(X) %*% ( y - eta )
for(int x=0;x<nEnv;x++)//col X
for(int i=0;i<nInd;i++)
Xt_y[x]+=covMatrix[x*nInd+i]*(Y[i]-eta[i]);
//get solve(t(X*eta*(1-eta)) %*% X )
for(int x=0;x<nEnv;x++)//col X
for(int y=0;y<nEnv;y++)//row Xt
for(int i=0;i<nInd;i++)
XtX[x*nEnv+y]+=covMatrix[y*nInd+i] * eta[i] * covMatrix[x*nInd+i];
// double workspace[2*nEnv];
//angsd::matinv(XtX, nEnv, nEnv, workspace);
int singular=angsd::svd_inverse(XtX,nEnv,nEnv);
if(singular)
return 1 ;
//S = svd_inverse(S,flag);
//get (inv(t(X)%*%X))%*%(t(X)%*%y)
for(int x=0;x<nEnv;x++)//col X
for(int y=0;y<nEnv;y++)//row Xt
invXtX_Xt_y[x]+=XtX[y*nEnv+x]*Xt_y[y];
double diff = 0;
for(int x=0;x<nEnv;x++)
diff += fabs(invXtX_Xt_y[x]);
for(int x=0;x<nEnv;x++)
coef[x]+=invXtX_Xt_y[x];
/*
fprintf(stdout,"iter=%d\n",iter);
for(int x=0;x<nEnv;x++)
fprintf(stdout,"%f\t",invXtX_Xt_y[x]);
fprintf(stdout,"diff=%f\n",diff);
for(int x=0;x<nEnv;x++)
fprintf(stdout,"%f\t",coef[x]);
fprintf(stdout,"\n");
*/
if(diff<tol)
break;
}
//yTilde <- X%in%coef
for(int j=0;j<nInd;j++)//row Xt
res[j]=0;
for(int j=0;j<nInd;j++)//row Xt
for(int x=0;x<nEnv;x++)
res[j]+=covMatrix[x*nInd+j]*coef[x];
for(int j=0;j<nInd;j++)
res[j]= angsd::sigm(res[j]);
// for(int x=0;x<nEnv;x++)
// fprintf(stdout,"%f\t",coef[x]);
//fprintf(stdout,"\n");
return 0;
}
double abcAsso::doAssociation(funkyPars *pars,double *postOrg,double *yOrg,int keepInd,int *keepList,double freq,int s,assoStruct *assoc){
if(doPrint)
fprintf(stderr,"Staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
double covMatrix[(covmat.y+1)*keepInd];
double y[keepInd];
double post[keepInd*3];
int count=0;
for(int i=0;i<pars->nInd;i++){
if(keepList[i]){
y[count]=yOrg[i];
for(int g=0;g<3;g++)
post[count*3+g]=postOrg[i*3+g];
count++;
}
}
if(count!=keepInd){
fprintf(stderr,"[%s] wrong number of non missing\n",__FUNCTION__);
fflush(stderr);
exit(0);
}
int nEnv;
if(adjust==1)
nEnv=(covmat.y+1);
else
nEnv=1;
int num;
for(int j=0;j<keepInd;j++)
covMatrix[j]=1;
if(covmat.matrix!=NULL){
num=0;
for(int j=0;j<covmat.x;j++){
if(keepList[j]==0)
continue;
for(int i=1;i<nEnv;i++)
covMatrix[i*keepInd+num]=covmat.matrix[j][i-1];
num++;
}
}
//fprintf(stderr,"number of inds %d\n",keepInd);
// permutation
if(sitePerm){
if((covmat.y+1)==1){
for(int i=0 ; i<keepInd ;i++){
int j = rand() % (keepInd);
angsd::swapDouble(y[j],y[i]);
}
}
else{
int col0=0;
for(int i=0 ; i<covmat.x ;i++) {
if(keepList[i]==0)
continue;
if(covmat.matrix[i][0]<0.5)
col0++;
}
for(int i=0 ; i<col0 ;i++) {
int j = rand() % (col0);
angsd::swapDouble(y[j],y[i]);
}
for(int i=0 ; i<keepInd-col0 ;i++) {
int j = rand() % (keepInd-col0);
angsd::swapDouble(y[j+col0],y[i+col0]);
}
if(col0<500||keepInd-col0<500){
fprintf(stderr,"colTrouble %d %d\n",col0,keepInd-col0);
}
}
}
//
double *yfit = new double[keepInd];
if(nEnv==1){
double mean=0;
for(int i=0;i<keepInd;i++)
mean+=y[i];
mean=mean/keepInd;
for(int i=0;i<keepInd;i++)
yfit[i]=mean;
}
else{
if(isBinary){
if(getFitBin(yfit,y,covMatrix,keepInd,nEnv))
return -999;
}
else
if(getFit(yfit,y,covMatrix,keepInd,nEnv))
return -999;
}
//for(int i=0;i<keepInd;i++)
// fprintf(stdout,"%f\t",y[i]);
// exit(0);
if(model==2){
for(int i=0 ; i<keepInd ;i++) {
post[i*3+1]+=post[i*3+2];
post[i*3+2]=0;
}
}
if(model==3){
for(int i=0 ; i<keepInd ;i++) {
post[i*3+0]+=post[i*3+1];
post[i*3+1]=post[i*3+2];
post[i*3+2]=0;
}
}
double stat;
if(isBinary)
stat = binomScoreEnv(post,keepInd,y,yfit,covMatrix,nEnv,freq,assoc,s);
else
stat = normScoreEnv(post,keepInd,y,yfit,covMatrix,nEnv,freq,assoc,s);
delete[] yfit;
return stat;
}
double abcAsso::normScoreEnv(double *post,int numInds, double *y, double *ytilde,double *cov,int nEnv,double freq,assoStruct *assoc,int s){
if(doPrint)
fprintf(stderr,"staring [%s]\t[%s]\n",__FILE__,__FUNCTION__);
int rankProb=0;
double sum=0;
double *Ex = angsd::allocArray<double>(numInds,0);
double *Ex2 = angsd::allocArray<double>(numInds,0);
double U=0;
int highWT=0;
int highHE=0;
int highHO=0;
double sumEx=0;
for(int i=0;i<numInds;i++)
sum+=pow(y[i]-ytilde[i],2);
double var=sum/(numInds-nEnv);
double Vaa[nEnv*nEnv];
for(int x=0;x<nEnv*nEnv;x++)
Vaa[x]=0;
double Vab[nEnv];
for(int x=0;x<nEnv;x++)
Vab[x]=0;
for(int i=0 ; i<numInds ;i++) {
Ex[i]=post[i*3+1]+2*post[i*3+2];
Ex2[i]=post[i*3+1]+4*post[i*3+2];
U+=Ex[i]*(y[i]-ytilde[i])/var;
//Vaa<-Vaa+1/var*Xe[tal,]%*%t(Xe[tal,])
for(int Nx=0;Nx<nEnv;Nx++)
for(int Ny=0;Ny<nEnv;Ny++){
Vaa[Nx*nEnv+Ny]+= (1/var)*cov[Nx*numInds+i]*cov[Ny*numInds+i];
}
for(int x=0;x<nEnv;x++)
Vab[x]+= (1/var)*Ex[i]*cov[x*numInds+i];
//Vab<-Vab+1/var*Ex[tal]*cbind(Xe[tal,])
if(post[i*3+0]>0.90)
highWT++;
if(post[i*3+1]>0.90)
highHE++;
if(post[i*3+2]>0.90)
highHO++;
}//recursion done
assoc->highWt[s] = highWT;
assoc->highHe[s] = highHE;
assoc->highHo[s] = highHO;
for(int i =0; i<numInds;i++)
sumEx+=Ex[i];
// double Vab=sumEx/var;
double Vbb=0;
for(int i =0; i<numInds;i++)
Vbb+=(1/var-pow(y[i]-ytilde[i],2)/pow(var,2))*Ex2[i]+pow(y[i]-ytilde[i],2)/pow(var,2)*pow(Ex[i],2);
//I<-Vbb-t(Vab)%*%MASS::ginv(Vaa)%*%Vab
double workspace[2*nEnv];
rankProb=angsd::matinv(Vaa, nEnv, nEnv, workspace);
double I =0;
//inv(Vaa)%*%Vab
double invVaa_Vab[nEnv];
for(int x=0;x<nEnv;x++)
invVaa_Vab[x]=0;
//NB! Vaa is now the inverse
for(int Nx=0;Nx<nEnv;Nx++)
for(int Ny=0;Ny<nEnv;Ny++)
invVaa_Vab[Nx]+=Vaa[Nx*nEnv+Ny]*Vab[Ny];
//I<-t(Vab)%*%MASS::ginv(Vaa)%*%Vab
for(int x=0;x<nEnv;x++)
I+=Vab[x]*invVaa_Vab[x];
//I<-Vbb-t(Vab)%*%MASS::ginv(Vaa)%*%Vab
//s fprintf(stderr,"tVab_invVaa_Vab: %f\n",I);
I=Vbb-I;
//the observed varians of the dispersion
double Vbs=0;
for(int i =0; i<numInds;i++)
Vbs+=Ex[i]*(y[i]-ytilde[i])/pow(var,2);
double Vss=0;
for(int i =0; i<numInds;i++)
Vss+=pow(y[i],2)+2*y[i]*ytilde[i];
Vss=Vss*pow(var,-3)-numInds/(4*M_PI*pow(var,2));
//fprintf(stderr,"Vbs %f Vss %f\n",Vbs,Vss);
I=I-pow(Vbs,2)/Vss;
double lrt =pow(U,2)/I;
int nGeno=0;
if(highWT >= minHigh)
nGeno++;
if(highHE >= minHigh)
nGeno++;
if(highHO >= minHigh)
nGeno++;
if(nGeno<2)
lrt=-999;//set_snan(lrt);
if(freq*numInds*2 < minCount || (1-freq)*numInds*2 < minCount)
lrt=-999;//set_snan(lrt); set_snan(lrt);
if(rankProb!=0)
lrt=-99;
// fprintf(lrtfile,"\t%d\t%f\t%f\t%f\t%f\t%f\t%f\t%f\t%f",numInds,ytilde,var,U,Vaa,Vab,Vbb,I,lrt);
//fprintf(lrtfile,"\t%d\t%f",numInds,lrt);
/*
if(verbose>0)
fprintf(fp,"\t%d\t%f\t%f\t%f\t%f\t%f\t%f\t%f\t%f",numInds,ytilde[0],var,U,Vaa[0],Vab[0],Vbb,I,lrt);
else
fprintf(fp,"\t%d\t%f",numInds,lrt);
*/
if((0||lrt>1000||I<-0.01||lrt<0)&&lrt!=-999&&lrt!=-99){//!std::isnan(lrt)){
for(int i=0 ; i<numInds ;i++) {
fprintf(stderr,"y: %f\t yfit: %f \t post %f %f %f\tEx %f %f\tU %f cov: ",y[i],ytilde[i],post[i*3+0],post[i*3+1],post[i*3+2],Ex[i],Ex2[i],U);
for(int j=0;j<nEnv;j++)
fprintf(stderr,"%f\t",cov[j*numInds+i]);
fprintf(stderr,"\n");
}
for(int j=0;j<pow(nEnv,2);j++)
fprintf(stderr,"Vaa: %f\t",Vaa[j]);
fprintf(stderr,"rank %d\tlrt: %f\t",rankProb,lrt);
fprintf(stderr,"\n");
for(int j=0;j<nEnv;j++)
fprintf(stderr,"Vab: %f\t",Vab[j]);
fprintf(stderr,"\n");
fflush(stderr);
fflush(stdout);
fprintf(stderr,"The test is unreliable. You should increase -minHigh\n");
// exit(0);