void KbacTest::calcKbacP(double* pvalue, int* sided)
{
/*! * the KBAC Statistic: sum of genotype pattern frequencies differences, weighted by hypergeometric kernel. <br>
* * It is a permutation based two-sided test. <br>
* * See <em> Liu DJ 2010 PLoS Genet. </em> <br><br>
*Implementation:
*/
if (*sided == 0 || *sided > 2)
*sided = 1;
//!- trim data by mafs upper-lower bounds
m_trimXdat();
unsigned int sampleSize = __ydat.size();
// sample size
unsigned int regionLen = __xdat[0].size();
// candidate region length
unsigned int nCases = 0;
// case size
for(unsigned int i = 0; i != __ydat.size(); ++i) {
if(__ydat[i] == AFFECTED)
++nCases;
}
unsigned int nCtrls = sampleSize - nCases;
if (__quiet == false) {
std::cout << "Sample size: " << sampleSize << std::endl;
std::cout << "Number of cases: " << nCases << std::endl;
std::cout << "Candidate region length: " << regionLen << std::endl;
}
std::vector<double> genotypeId(sampleSize);
//!-Compute unique genotype patterns (string) as ID scores (double)
for (unsigned int i = 0; i != sampleSize; ++i) {
double vntIdL = 0.0;
double vntIdR = 0.0;
const double ixiix= pow(9.0, 10.0);
unsigned int lastCnt = 0;
unsigned int tmpCnt = 0;
for (unsigned int j = 0; j != regionLen; ++j) {
if (__xdat[i][j] != MISSING_ALLELE && __xdat[i][j] != MAJOR_ALLELE)
vntIdR += pow(3.0, 1.0 * (j - lastCnt)) * __xdat[i][j];
else
continue;
if (vntIdR >= ixiix) {
vntIdL = vntIdL + 1.0;
vntIdR = vntIdR - ixiix;
lastCnt = lastCnt + tmpCnt + 1;
tmpCnt = 0;
continue;
}
else {
++tmpCnt;
continue;
}
}
genotypeId[i] = vntIdL + vntIdR * 1e-10;
// one-to-one "ID number" for a genotype pattern
}
// unique genotype patterns
// use "list" and some STL algorithms
std::list<double> uniqueId(genotypeId.begin(), genotypeId.end());
uniqueId.remove(MAJOR_ALLELE);
if (uniqueId.size() == 0) {
std::cout << "**Warning** non-wildtype genotype data is empty. KBAC has nothing to work on. Return p-value 1.0" << std::endl;
*pvalue = 1.0;
return;
}
uniqueId.sort();
uniqueId.unique();
// remove wildtype and get unique genotype patterns
//!- Unique genotype patterns that occur in the sample
std::vector<double> uniquePattern(uniqueId.size());
copy(uniqueId.begin(), uniqueId.end(), uniquePattern.begin());
uniqueId.clear();
if (__quiet == false) {
std::cout << "All individual genotype patterns: " << "\n" << std::endl;
std::cout << genotypeId << std::endl;
std::cout << "Unique individual genotype patterns: " << "\n" << std::endl;
std::cout << uniquePattern << std::endl;
}
// count number of sample individuals for each genotype pattern
unsigned int uniquePatternCounts[uniquePattern.size()];
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternCounts[u] = 0;
for (unsigned int i = 0; i != sampleSize; ++i) {
// for each sample, identify/count its genotype pattern
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (genotypeId[i] == uniquePattern[u]) {
// genotype pattern identified
++uniquePatternCounts[u];
// count this genotype pattern
break;
}
else;
// genotype pattern not found -- move on to next pattern
}
}
if (__quiet == false) {
std::cout << "Number of each unique individual genotype patterns (totaling " << uniquePattern.size() << " patterns excluding wildtype): " << "\n" << std::endl;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) std::cout << uniquePatternCounts[u] << ", ";
std::cout << "\n" << std::endl;
}
unsigned int iPermutation = 0;
unsigned int permcount1 = 0, permcount2 = 0;
double observedStatistic = 0.0;
while (iPermutation <= __nPermutations) {
// the KBAC statistic. Will be of length 1 or 2
std::vector<double> kbacStatistics(0);
// two models
for (int s = 0; s != *sided; ++s) {
//!- count number of sample cases (for the 1st model, or ctrls for the 2nd model) for each genotype pattern
unsigned int uniquePatternCountsSub[uniquePattern.size()];
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternCountsSub[u] = 0;
// genotype pattern counts in cases (for the 1st model, or ctrls for the 2nd model)
for (unsigned int i = 0; i != sampleSize; ++i) {
if ( __ydat[i] == (AFFECTED - 1.0 * s) ) {
// for each "case (for the 1st model, or ctrls for 2nd model)", identify/count its genotype pattern
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (genotypeId[i] == uniquePattern[u]) {
// genotype pattern identified in cases (for the 1st model, or ctrls for 2nd model)
++uniquePatternCountsSub[u];
// count this genotype pattern
break;
}
else;
// genotype pattern not found -- move on to next pattern
}
}
else;
}
//!- KBAC weights
double uniquePatternWeights[uniquePattern.size()];
// genotype pattern weights, the hypergeometric distribution cmf
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternWeights[u] = 0.0;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (s == 0)
uniquePatternWeights[u] = gsl_cdf_hypergeometric_P(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCases);
//uniquePatternWeights[u] = gw_hypergeometric_cmf(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCases);
else
uniquePatternWeights[u] = gsl_cdf_hypergeometric_P(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCtrls);
//uniquePatternWeights[u] = gw_hypergeometric_cmf(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCtrls);
}
//!- KBAC statistic: sum of genotype pattern frequencies differences in cases vs. controls, weighted by the hypergeometric distribution kernel
double kbac = 0.0;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (s == 0)
kbac = kbac + ( (1.0 * uniquePatternCountsSub[u]) / (1.0 * nCases) - (1.0 * (uniquePatternCounts[u] - uniquePatternCountsSub[u])) / (1.0 * nCtrls) ) * uniquePatternWeights[u];
else
kbac = kbac + ( (1.0 * uniquePatternCountsSub[u]) / (1.0 * nCtrls) - (1.0 * (uniquePatternCounts[u] - uniquePatternCountsSub[u])) / (1.0 * nCases) ) * uniquePatternWeights[u];
}
//std::cout << kbac << std::endl;
//FIXME
//gw_round(kbac, 0.0001);
kbacStatistics.push_back(kbac);
}
double statistic = 0.0;
//!- one model statistic
if (kbacStatistics.size() == 1) {
statistic = kbacStatistics[0];
}
//!- two model statistic
else if (kbacStatistics.size() == 2) {
statistic = fmax(kbacStatistics[0], kbacStatistics[1]);
}
else {
std::cerr << "**Error KBAC statistic (Error code -5)" << std::endl;
exit(-1);
}
if (iPermutation == 0)
observedStatistic = statistic;
else {
if (statistic >= observedStatistic)
++permcount1;
if (statistic <= observedStatistic)
++permcount2;
if (__adaptive != 0)
*pvalue = m_checkAdaptivePvalue(permcount1, permcount2, iPermutation, __adaptive, 0);
}
if (*pvalue <= 1.0)
break;
//!- Permutation
random_shuffle(__ydat.begin(), __ydat.end());
++iPermutation;
}
if (*pvalue <= 1.0);
else {
*pvalue = (1.0 * permcount1 + 1.0) / (1.0 * __nPermutations + 1.0);
}
return;
}
void KbacTest::calcKbacP(double* pvalue, int* sided, double* test_statistic)
{
/*! * the KBAC Statistic: sum of genotype pattern frequencies differences, weighted by hypergeometric kernel. <br>
* * It is a permutation based two-sided test. <br>
* * See <em> Liu DJ 2010 PLoS Genet. </em> <br><br>
*Implementation:
*/
if (*sided == 0 || *sided > 2)
*sided = 1;
//!- trim data by mafs upper-lower bounds
m_trimXdat();
unsigned int sampleSize = m_ydat.size();
// sample size
unsigned int regionLen = m_xdat[0].size();
// candidate region length
unsigned int nCases = 0;
// case size
for(unsigned int i = 0; i != m_ydat.size(); ++i) {
if(m_ydat[i] == AFFECTED)
++nCases;
}
unsigned int nCtrls = sampleSize - nCases;
if (m_quiet == false) {
std::cout << "Sample size: " << sampleSize << std::endl;
std::cout << "Number of cases: " << nCases << std::endl;
std::cout << "Candidate region length: " << regionLen << std::endl;
}
std::vector<double> genotypeId(sampleSize);
//!-Compute unique genotype patterns (string) as ID scores (double)
bool hasWt = false;
for (unsigned int i = 0; i != sampleSize; ++i) {
double vntIdL = 0.0;
double vntIdR = 0.0;
const double ixiix= pow(9.0, 10.0);
unsigned int lastCnt = 0;
unsigned int tmpCnt = 0;
for (unsigned int j = 0; j != regionLen; ++j) {
if (m_xdat[i][j] != MISSING_ALLELE && m_xdat[i][j] != MAJOR_ALLELE)
vntIdR += pow(3.0, 1.0 * (j - lastCnt)) * m_xdat[i][j];
else
continue;
if (vntIdR >= ixiix) {
vntIdL = vntIdL + 1.0;
vntIdR = vntIdR - ixiix;
lastCnt = lastCnt + tmpCnt + 1;
tmpCnt = 0;
continue;
}
else {
++tmpCnt;
continue;
}
}
// one-to-one "ID number" for a genotype pattern
genotypeId[i] = vntIdL + vntIdR * 1e-10;
if (!(genotypeId[i] > MAJOR_ALLELE) && !hasWt)
hasWt = true;
}
rank(genotypeId, genotypeId, "default");
// remove wildtype and get unique genotype patterns
//!- Unique genotype patterns that occur in the sample
std::vector<double> uniquePattern = genotypeId;
std::sort(uniquePattern.begin(), uniquePattern.end());
std::vector<double>::iterator it = std::unique(uniquePattern.begin(), uniquePattern.end());
uniquePattern.resize(it - uniquePattern.begin());
if (hasWt) uniquePattern.erase(uniquePattern.begin());
if (uniquePattern.size() == 0) {
std::cout << "**Warning** non-wildtype genotype data is empty. KBAC has nothing to work on. Return p-value 1.0" << std::endl;
*pvalue = 1.0;
return;
}
// count number of sample individuals for each genotype pattern
unsigned int uniquePatternCounts[uniquePattern.size()];
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternCounts[u] = 0;
for (unsigned int i = 0; i != sampleSize; ++i) {
// for each sample, identify/count its genotype pattern
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (genotypeId[i] == uniquePattern[u]) {
// genotype pattern identified
++uniquePatternCounts[u];
// count this genotype pattern
break;
}
else;
// genotype pattern not found -- move on to next pattern
}
}
if (m_quiet == false) {
std::cout << "\nGenotype pattern loading ranks: " << std::endl;
std::cout << genotypeId << std::endl;
std::cout << "\nNumber of each unique individual genotype patterns (totaling " << uniquePattern.size() << " patterns excluding wildtype): " << std::endl;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) std::cout << uniquePatternCounts[u] << ", ";
std::cout << std::endl;
}
unsigned int iPermutation = 0;
unsigned int permcount1 = 0, permcount2 = 0;
double observedStatistic = 0.0;
if(m_nPermutations > 0) {
while (iPermutation <= m_nPermutations) {
// the KBAC statistic. Will be of length 1 or 2
std::vector<double> kbacStatistics(0);
// two models
for (unsigned int s = 0; s != *sided; ++s) {
//!- count number of sample cases (for the 1st model, or ctrls for the 2nd model) for each genotype pattern
unsigned int uniquePatternCountsSub[uniquePattern.size()];
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternCountsSub[u] = 0;
// genotype pattern counts in cases (for the 1st model, or ctrls for the 2nd model)
for (unsigned int i = 0; i != sampleSize; ++i) {
if ( m_ydat[i] == (AFFECTED - 1.0 * s) ) {
// for each "case (for the 1st model, or ctrls for 2nd model)", identify/count its genotype pattern
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (genotypeId[i] == uniquePattern[u]) {
// genotype pattern identified in cases (for the 1st model, or ctrls for 2nd model)
++uniquePatternCountsSub[u];
// count this genotype pattern
break;
}
else;
// genotype pattern not found -- move on to next pattern
}
}
else;
}
//!- KBAC weights
std::vector<double> uniquePatternWeights(uniquePattern.size());
// genotype pattern weights, the hypergeometric distribution cmf
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternWeights[u] = 0.0;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (s == 0)
uniquePatternWeights[u] = gsl_cdf_hypergeometric_P(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCases);
//uniquePatternWeights[u] = gw_hypergeometric_cmf(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCases);
else
uniquePatternWeights[u] = gsl_cdf_hypergeometric_P(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCtrls);
//uniquePatternWeights[u] = gw_hypergeometric_cmf(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCtrls);
}
if (m_quiet == false && iPermutation == 0) {
std::cout << "\nUnique genotype patterns weights (model " << s+1 << "):" << std::endl;
std::cout << uniquePatternWeights << std::endl;
}
//!- KBAC statistic: sum of genotype pattern frequencies differences in cases vs. controls, weighted by the hypergeometric distribution kernel
double kbac = 0.0;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (s == 0)
kbac = kbac + ( (1.0 * uniquePatternCountsSub[u]) / (1.0 * nCases) - (1.0 * (uniquePatternCounts[u] - uniquePatternCountsSub[u])) / (1.0 * nCtrls) ) * uniquePatternWeights[u];
else
kbac = kbac + ( (1.0 * uniquePatternCountsSub[u]) / (1.0 * nCtrls) - (1.0 * (uniquePatternCounts[u] - uniquePatternCountsSub[u])) / (1.0 * nCases) ) * uniquePatternWeights[u];
}
//std::cout << kbac << std::endl;
//FIXME
//gw_round(kbac, 0.0001);
kbacStatistics.push_back(kbac);
}
double statistic = 0.0;
//!- one model statistic
if (kbacStatistics.size() == 1) {
statistic = kbacStatistics[0];
}
//!- two model statistic
else if (kbacStatistics.size() == 2) {
statistic = fmax(kbacStatistics[0], kbacStatistics[1]);
}
else {
std::cerr << "**Error KBAC statistic (Error code -5)" << std::endl;
exit(-1);
}
if (iPermutation == 0)
observedStatistic = statistic;
else {
if (statistic >= observedStatistic)
++permcount1;
if (statistic <= observedStatistic)
++permcount2;
if (m_adaptive != 0)
*pvalue = m_checkAdaptivePvalue(permcount1, permcount2, iPermutation, m_adaptive, 0);
}
*test_statistic = statistic;
if (*pvalue <= 1.0) {
break;
}
//!- Permutation
random_shuffle(m_ydat.begin(), m_ydat.end());
++iPermutation;
}
if (*pvalue <= 1.0);
else {
*pvalue = (1.0 * permcount1 + 1.0) / (1.0 * m_nPermutations + 1.0);
}
//}
} else { // Monte-Carlo approximation
// the KBAC statistic. Will be of length 1 or 2
std::vector<double> kbacStatistics(0);
// two models
for (unsigned int s = 0; s != *sided; ++s) {
//!- count number of sample cases (for the 1st model, or ctrls for the 2nd model) for each genotype pattern
unsigned int uniquePatternCountsSub[uniquePattern.size()];
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternCountsSub[u] = 0;
// genotype pattern counts in cases (for the 1st model, or ctrls for the 2nd model)
for (unsigned int i = 0; i != sampleSize; ++i) {
if ( m_ydat[i] == (AFFECTED - 1.0 * s) ) {
// for each "case (for the 1st model, or ctrls for 2nd model)", identify/count its genotype pattern
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (genotypeId[i] == uniquePattern[u]) {
// genotype pattern identified in cases (for the 1st model, or ctrls for 2nd model)
++uniquePatternCountsSub[u];
// count this genotype pattern
break;
}
else;
// genotype pattern not found -- move on to next pattern
}
}
else;
}
//!- KBAC weights
std::vector<double> uniquePatternWeights(uniquePattern.size());
// genotype pattern weights, the hypergeometric distribution cmf
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternWeights[u] = 0.0;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (s == 0)
uniquePatternWeights[u] = gsl_cdf_hypergeometric_P(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCases);
//uniquePatternWeights[u] = gw_hypergeometric_cmf(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCases);
else
uniquePatternWeights[u] = gsl_cdf_hypergeometric_P(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCtrls);
//uniquePatternWeights[u] = gw_hypergeometric_cmf(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCtrls);
}
if (m_quiet == false && iPermutation == 0) {
std::cout << "\nUnique genotype patterns weights (model " << s+1 << "):" << std::endl;
std::cout << uniquePatternWeights << std::endl;
}
//!- KBAC statistic: sum of genotype pattern frequencies differences in cases vs. controls, weighted by the hypergeometric distribution kernel
double kbac = 0.0;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (s == 0)
kbac = kbac + ( (1.0 * uniquePatternCountsSub[u]) / (1.0 * nCases) - (1.0 * (uniquePatternCounts[u] - uniquePatternCountsSub[u])) / (1.0 * nCtrls) ) * uniquePatternWeights[u];
else
kbac = kbac + ( (1.0 * uniquePatternCountsSub[u]) / (1.0 * nCtrls) - (1.0 * (uniquePatternCounts[u] - uniquePatternCountsSub[u])) / (1.0 * nCases) ) * uniquePatternWeights[u];
}
//std::cout << kbac << std::endl;
//FIXME
//gw_round(kbac, 0.0001);
kbacStatistics.push_back(kbac);
}
double statistic = 0.0;
//!- one model statistic
if (kbacStatistics.size() == 1) {
statistic = kbacStatistics[0];
}
//!- two model statistic
else if (kbacStatistics.size() == 2) {
statistic = fmax(kbacStatistics[0], kbacStatistics[1]);
}
else {
std::cerr << "**Error KBAC statistic (Error code -5)" << std::endl;
exit(-1);
}
/*if (iPermutation == 0)
observedStatistic = statistic;
else {
if (statistic >= observedStatistic)
++permcount1;
if (statistic <= observedStatistic)
++permcount2;
if (m_adaptive != 0)
*pvalue = m_checkAdaptivePvalue(permcount1, permcount2, iPermutation, m_adaptive, 0);
}
*/
*test_statistic = statistic;
/*
if (*pvalue <= 1.0) {
break;
}
*/
// Monte-Carlo approximation
std::vector<double> mcStat;
unsigned long nn = nCases+nCtrls;
while(nn > 0) {
std::vector<double> mcStatistics(0);
// two models
for (unsigned int s = 0; s != *sided; ++s) {
//unsigned int s = 0;
//!- count number of sample cases (for the 1st model, or ctrls for the 2nd model) for each genotype pattern
unsigned int uniquePatternCountsSub[uniquePattern.size()];
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternCountsSub[u] = 0;
// genotype pattern counts in cases (for the 1st model, or ctrls for the 2nd model)
for (unsigned int i = 0; i != sampleSize; ++i) {
if ( m_ydat[i] == (AFFECTED - 1.0 * s) ) {
// for each "case (for the 1st model, or ctrls for 2nd model)", identify/count its genotype pattern
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (genotypeId[i] == uniquePattern[u]) {
// genotype pattern identified in cases (for the 1st model, or ctrls for 2nd model)
++uniquePatternCountsSub[u];
// count this genotype pattern
break;
}
else;
// genotype pattern not found -- move on to next pattern
}
}
else;
}
// binomial random number
std::vector<double> mBinomial(uniquePattern.size());
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
mBinomial[u] = 0;
//std::cout << uniquePatternCounts[u] << std::endl;
mBinomial[u] = Rcpp::rbinom(1, uniquePatternCounts[u], (1.0*nCases)/(1.0*(nCases+nCtrls)))[0];
//std::cout << uniquePatternCounts[u] << std::endl;
}
//!- MC weights
std::vector<double> uniquePatternWeights(uniquePattern.size());
// genotype pattern weights, the hypergeometric distribution cmf
for (unsigned int u = 0; u != uniquePattern.size(); ++u)
uniquePatternWeights[u] = 0.0;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
if (s == 0)
uniquePatternWeights[u] = gsl_cdf_hypergeometric_P(mBinomial[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCases);
//uniquePatternWeights[u] = gw_hypergeometric_cmf(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCases);
else
uniquePatternWeights[u] = gsl_cdf_hypergeometric_P(mBinomial[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCtrls);
//uniquePatternWeights[u] = gw_hypergeometric_cmf(uniquePatternCountsSub[u], uniquePatternCounts[u], sampleSize - uniquePatternCounts[u], nCtrls);
}
if (m_quiet == false && iPermutation == 0) {
std::cout << "\nUnique genotype patterns weights (model " << s+1 << "):" << std::endl;
std::cout << uniquePatternWeights << std::endl;
}
//!- MC KBAC statistic: sum of genotype pattern frequencies differences in cases vs. controls, weighted by the hypergeometric distribution kernel
double mc = 0.0;
for (unsigned int u = 0; u != uniquePattern.size(); ++u) {
//std::cout << mBinomial[u] << " " << uniquePatternCounts[u] << " " << uniquePatternWeights[u] << std::endl;
if (s == 0) {
// mc = mc + ( (1.0 * mBinomial[u]) / (1.0 * nCases) - (1.0 * (uniquePatternCounts[u] - mBinomial[u])) / (1.0 * nCtrls) ) * uniquePatternWeights[u];
mc += ( (1.0 * mBinomial[u]) / (1.0 * nCases) - (1.0 * (uniquePatternCounts[u] - mBinomial[u])) / (1.0 * nCtrls) ) * uniquePatternWeights[u];
} else {
mc = mc + ( (1.0 * mBinomial[u]) / (1.0 * nCtrls) - (1.0 * (uniquePatternCounts[u] - mBinomial[u])) / (1.0 * nCases) ) * uniquePatternWeights[u];
}
}
//std::cout << mc << std::endl;
mcStatistics.push_back(mc);
}
double mc_statistic = 0.0;
//!- one model statistic
if (mcStatistics.size() == 1) {
mc_statistic = mcStatistics[0];
}
//!- two model statistic
else if (mcStatistics.size() == 2) {
mc_statistic = fmax(mcStatistics[0], mcStatistics[1]);
}
else {
std::cerr << "**Error MC statistic (Error code -5)" << std::endl;
exit(-1);
}
mcStat.push_back(mc_statistic);
nn--;
}
// Calculate p-value from MC approximation:
double sum = 0.0;
for(unsigned long i=0; i<nCases+nCtrls; i++) {
if(mcStat[i] >= statistic)
sum += 1.0;
}
///std::cout << mcStat << std::endl;
*pvalue = sum/(1.0*(nCases+nCtrls));
}
return;
}