static Rboolean any_nan_complex(SEXP x) {
const Rcomplex * xp = COMPLEX_RO(x);
const Rcomplex * const xe = xp + xlength(x);
for (; xp != xe; xp++) {
if (R_IsNaN((*xp).r) || R_IsNaN((*xp).i))
return TRUE;
}
return FALSE;
}
double exp_pnorm(double a, double b)
{
double r=0;
if (R_IsNaN(r) && b < -5.5) r = 1/sqrt(2) * exp(a - b*b/2) * (0.5641882/b/b/b - 1/b/sqrt(M_PI));
else r = exp(a) * pnorm(b,0,1,1,0);
return r;
}
static Rboolean any_nan_double(SEXP x) {
const double * xp = REAL_RO(x);
const double * const xe = xp + xlength(x);
for (; xp != xe; xp++) {
if (R_IsNaN(*xp))
return TRUE;
}
return FALSE;
}
static double R_fun(double x, void *data){
mh_str *da = data ;
SEXP R_x, s ;
PROTECT_INDEX ipx;
PROTECT(R_x = allocVector(REALSXP, 1));
REAL(R_x)[0] = x ;
SETCADR(da->R_fcall, R_x); /* assign the argument */
/* evaluate function calls */
PROTECT_WITH_INDEX(s = eval(da->R_fcall, da->R_env), &ipx);
REPROTECT(s = coerceVector(s, REALSXP), ipx);
if (LENGTH(s) != 1)
error(("objective function evaluates to length %d not 1"), LENGTH(s));
if (!R_FINITE(REAL(s)[0]) || R_IsNaN(REAL(s)[0]) || R_IsNA(REAL(s)[0]))
error("objective funtion evaluates to Inf, NaN or NA");
UNPROTECT(2);
return REAL(s)[0];
}
double pwiener_d(double q, double alpha, double tau, double beta, double delta)
{
double p;
if(!R_finite(q)) return R_PosInf;
if (R_IsNaN(q)) return R_NaN;
if (fabs(q) <= tau) return 0;
if (q < 0) { // lower boundary 0
p = F_lower(fabs(q)-tau, delta, alpha, beta);
}
else { // upper boundary a
p = F_lower(q-tau, (-delta), alpha, (1-beta));
}
return p;
}
double analyseF2(int Nind, int *nummark, cvector *cofactor, MQMMarkerMatrix marker,
vector y, int Backwards, double **QTL,vector
*mapdistance, int **Chromo, int Nrun, int RMLorML, double
windowsize, double stepsize, double stepmin, double stepmax,
double alfa, int em, int out_Naug, int **INDlist, char
reestimate, MQMCrossType crosstype, bool dominance, int verbose) {
if (verbose) Rprintf("INFO: Starting C-part of the MQM analysis\n");
int Naug, Nmark = (*nummark), run = 0;
bool useREML = true, fitQTL = false;
bool warned = false;
ivector chr = newivector(Nmark); // The chr vector contains the chromosome number for every marker
for(int i = 0; i < Nmark; i++){ // Rprintf("INFO: Receiving the chromosome matrix from R");
chr[i] = Chromo[0][i];
}
if(RMLorML == 1) useREML=false; // use ML instead
// Create an array of marker positions - and calculate R[f] based on these locations
cvector position = relative_marker_position(Nmark,chr);
vector r = recombination_frequencies(Nmark, position, (*mapdistance));
//Rprintf("INFO: Initialize Frun and informationcontent to 0.0");
const int Nsteps = (int)(chr[Nmark-1]*((stepmax-stepmin)/stepsize+1));
matrix Frun = newmatrix(Nsteps,Nrun+1);
vector informationcontent = newvector(Nsteps);
for (int i = 0; i < (Nrun+1); i++) {
for (int ii = 0; ii < Nsteps; ii++) {
if(i==0) informationcontent[ii] = 0.0;
Frun[ii][i]= 0.0;
}
}
bool dropj = false;
int jj=0;
// Rprintf("any triple of non-segregating markers is considered to be the result of:\n");
// Rprintf("identity-by-descent (IBD) instead of identity-by-state (IBS)\n");
// Rprintf("no (segregating!) cofactors are fitted in such non-segregating IBD regions\n");
for (int j=0; j < Nmark; j++) { // WRONG: (Nmark-1) Should fix the out of bound in mapdistance, it does fix, but created problems for the last marker
dropj = false;
if(j+1 < Nmark){ // Check if we can look ahead
if(((*mapdistance)[j+1]-(*mapdistance)[j])==0.0){ dropj=true; }
}
if (!dropj) {
marker[jj] = marker[j];
(*cofactor)[jj] = (*cofactor)[j];
(*mapdistance)[jj] = (*mapdistance)[j];
chr[jj] = chr[j];
r[jj] = r[j];
position[jj] = position[j];
jj++;
} else{
if (verbose) Rprintf("INFO: Marker %d at chr %d is dropped\n",j,chr[j]);
if ((*cofactor)[j]==MCOF) {
if (verbose) Rprintf("INFO: Cofactor at chr %d is dropped\n",chr[j]);
}
}
}
//if(verbose) Rprintf("INFO: Number of markers: %d -> %d\n",Nmark,jj);
Nmark = jj;
(*nummark) = jj;
// Update the array of marker positions - and calculate R[f] based on these new locations
position = relative_marker_position(Nmark,chr);
r = recombination_frequencies(Nmark, position, (*mapdistance));
debug_trace("After dropping of uninformative cofactors\n");
ivector newind; // calculate Traits mean and variance
vector newy;
MQMMarkerMatrix newmarker;
double ymean = 0.0, yvari = 0.0;
//Rprintf("INFO: Number of individuals: %d Number Aug: %d",Nind,out_Naug);
int cur = -1;
for (int i=0; i < Nind; i++){
if(INDlist[0][i] != cur){
ymean += y[i];
cur = INDlist[0][i];
}
}
ymean/= out_Naug;
for (int i=0; i < Nind; i++){
if(INDlist[0][i] != cur){
yvari += pow(y[i]-ymean, 2);
cur = INDlist[0][i];
}
}
yvari /= (out_Naug-1);
Naug = Nind; // Fix for not doing dataaugmentation, we just copy the current as the augmented and set Naug to Nind
Nind = out_Naug;
newind = newivector(Naug);
newy = newvector(Naug);
newmarker = newMQMMarkerMatrix(Nmark,Naug);
for (int i=0; i<Naug; i++) {
newy[i]= y[i];
newind[i]= INDlist[0][i];
for (int j=0; j<Nmark; j++) {
newmarker[j][i]= marker[j][i];
}
}
// End fix
vector newweight = newvector(Naug);
double max = rmixture(newmarker, newweight, r, position, newind,Nind, Naug, Nmark, mapdistance,reestimate,crosstype,verbose); //Re-estimation of mapdistances if reestimate=TRUE
if(max > stepmax){ fatal("ERROR: Re-estimation of the map put markers at: %f Cm, run the algorithm with a step.max larger than %f Cm", max, max); }
//Check if everything still is correct positions and R[f]
position = relative_marker_position(Nmark,chr);
r = recombination_frequencies(Nmark, position, (*mapdistance));
/* eliminate individuals with missing trait values */
//We can skip this part iirc because R throws out missing phenotypes beforehand
int oldNind = Nind;
for (int i=0; i<oldNind; i++) {
Nind -= ((y[i]==TRAITUNKNOWN) ? 1 : 0);
}
int oldNaug = Naug;
for (int i=0; i<oldNaug; i++) {
Naug -= ((newy[i]==TRAITUNKNOWN) ? 1 : 0);
}
marker = newMQMMarkerMatrix(Nmark+1,Naug);
y = newvector(Naug);
ivector ind = newivector(Naug);
vector weight = newvector(Naug);
int newi = 0;
for (int i=0; i < oldNaug; i++)
if (newy[i]!=TRAITUNKNOWN) {
y[newi]= newy[i];
ind[newi]= newind[i];
weight[newi]= newweight[i];
for (int j=0; j<Nmark; j++) marker[j][newi]= newmarker[j][i];
newi++;
}
int diff;
for (int i=0; i < (Naug-1); i++) {
diff = ind[i+1]-ind[i];
if (diff>1) {
for (int ii=i+1; ii<Naug; ii++){ ind[ii]=ind[ii]-diff+1; }
}
}
//END throwing out missing phenotypes
double variance=-1.0;
cvector selcofactor = newcvector(Nmark); /* selected cofactors */
int dimx = designmatrixdimensions((*cofactor),Nmark,dominance);
double F1 = inverseF(1,Nind-dimx,alfa,verbose);
double F2 = inverseF(2,Nind-dimx,alfa,verbose);
if (verbose) {
Rprintf("INFO: dimX: %d, nInd: %d\n",dimx,Nind);
Rprintf("INFO: F(Threshold, Degrees of freedom 1, Degrees of freedom 2) = Alfa\n");
Rprintf("INFO: F(%.3f, 1, %d) = %f\n",ftruncate3(F1),(Nind-dimx),alfa);
Rprintf("INFO: F(%.3f, 2, %d) = %f\n",ftruncate3(F2),(Nind-dimx),alfa);
}
F2 = 2.0* F2; // 9-6-1998 using threshold x*F(x,df,alfa)
weight[0]= -1.0;
double logL = QTLmixture(marker,(*cofactor),r,position,y,ind,Nind,Naug,Nmark,&variance,em,&weight,useREML,fitQTL,dominance,crosstype, &warned, verbose);
if(verbose){
if (!R_finite(logL)) {
Rprintf("WARNING: Log-likelihood of full model = INFINITE\n");
}else{
if (R_IsNaN(logL)) {
Rprintf("WARNING: Log-likelihood of full model = NOT A NUMBER (NAN)\n");
}else{
Rprintf("INFO: Log-likelihood of full model = %.3f\n",ftruncate3(logL));
}
}
Rprintf("INFO: Residual variance = %.3f\n",ftruncate3(variance));
Rprintf("INFO: Trait mean= %.3f; Trait variation = %.3f\n",ftruncate3(ymean),ftruncate3(yvari));
}
if (R_finite(logL) && !R_IsNaN(logL)) {
if(Backwards==1){ // use only selected cofactors
logL = backward(Nind, Nmark, (*cofactor), marker, y, weight, ind, Naug, logL,variance, F1, F2, &selcofactor, r,
position, &informationcontent, mapdistance,&Frun,run,useREML,fitQTL,dominance, em, windowsize,
stepsize, stepmin, stepmax,crosstype,verbose);
}else{ // use all cofactors
logL = mapQTL(Nind, Nmark, (*cofactor), (*cofactor), marker, position,(*mapdistance), y, r, ind, Naug, variance,
'n', &informationcontent,&Frun,run,useREML,fitQTL,dominance, em, windowsize, stepsize, stepmin,
stepmax,crosstype,verbose); // printout=='n'
}
}
// Write output and/or send it back to R
// Cofactors that made it to the final model
for (int j=0; j<Nmark; j++) {
if (selcofactor[j]==MCOF) {
(*cofactor)[j]=MCOF;
}else{
(*cofactor)[j]=MNOCOF;
}
}
if (verbose) Rprintf("INFO: Number of output datapoints: %d\n", Nsteps); // QTL likelihood for each location
for (int ii=0; ii<Nsteps; ii++) {
//Convert LR to LOD before sending back
QTL[0][ii] = Frun[ii][0] / 4.60517;
QTL[0][Nsteps+ii] = informationcontent[ii];
}
return logL;
}
