static double uiqi(float *x, float *y, int n)
{
double mx, my, sx, sy, sxy = 0;
meanvar(&mx, &sx, x, n);
meanvar(&my, &sy, y, n);
for (int i = 0; i < n; i++)
sxy += (x[i] - mx)*(y[i] - my);
sxy /= n - 1;
double q = (4 * sxy * mx * my);
q /= (sx + sy) * (mx*mx + my*my);
return q;
}
static double ssim(float *x, float *y, int n)
{
double mx, my, sx, sy, sxy = 0;
meanvar(&mx, &sx, x, n);
meanvar(&my, &sy, y, n);
for (int i = 0; i < n; i++)
sxy += (x[i] - mx)*(y[i] - my);
sxy /= n - 1;
double Lx = dynamic_range(x, n);
double Ly = dynamic_range(x, n);
double L = (Lx + Ly)/2;
double C1 = SSIM_K1 * L;
double C2 = SSIM_K2 * L;
C1 *= C1;
C2 *= C2;
double r = (2*mx*my + C1) * (2*sxy + C2);
r /= (mx*mx + my*my + C1) * (sx + sx + C2);
return r;
}
void ttest(float X[], unsigned int nx, float Y[], unsigned int ny, \
float *t, float *p)
{
// Simple t-test, assuming equal variance
// Sokal & Rohlf, Box 9.6
// For unequal variance, need to code up Box 13.3
double sumv;
float mnx, mny, varx, vary;
unsigned long int df;
meanvar(X, nx, &mnx, &varx);
meanvar(Y, ny, &mny, &vary);
df = nx + ny - 2;
sumv = ( ( (nx-1) * varx ) + ( ( ny-1) * vary) ) / df;
*t = (mnx-mny) / sqrt( sumv * ( (float) (nx+ny) / (float) (nx*ny) ) );
*p = ttable(*t, df, 2);
}
//ComTraitMetric - calculate metrics of trait variation in each sample
void ComTraitMetric(sample S, traits T, int SwapMethod, int XVAR)
{
//initialize variables
int samp,trait, rec, run;
int *STattach;
int **rankLowSTMetricRnd;
int **rankHighSTMetricRnd;
float **STMeanObs;
float ***STMeanRnd;
float **MeanSTMetricRnd;
float **STMetricObs;
float ***STMetricRnd;
float **StDevSTMetricRnd;
float **SESSTMetricRnd;
float *sampleTraits;
float *randomMetrics;
float mean, var, metric;
rankLowSTMetricRnd = imatrix(0,S.nsamples-1,0,T.ntraits-1);
rankHighSTMetricRnd = imatrix(0,S.nsamples-1,0,T.ntraits-1);
STMeanObs = matrix(0,S.nsamples-1,0,T.ntraits-1);
STMeanRnd = f3tensor(0,S.nsamples-1,0,T.ntraits-1,0,RUNS);
MeanSTMetricRnd = matrix(0,S.nsamples-1,0,T.ntraits-1);
STMetricObs = matrix(0,S.nsamples-1,0,T.ntraits-1);
STMetricRnd = f3tensor(0,S.nsamples-1,0,T.ntraits-1,0,RUNS);
StDevSTMetricRnd = matrix(0,S.nsamples-1,0,T.ntraits-1);
SESSTMetricRnd = matrix(0,S.nsamples-1,0,T.ntraits-1);
// attach sample to traits
STattach = ivector(0, T.ntaxa-1);
AttachSampleToTraits(S,T,STattach);
//loop through samples and traits
//calculate trait metric of each sample
//store in an array
for (trait = 0; trait < T.ntraits; trait++)
{
for (samp = 0; samp < S.nsamples; samp++)
{
//(re)dimension a vector to fill with trait values for this sample
sampleTraits = vector(0,S.srec[samp]-1);
//fill the vector
for (rec = 0;rec < S.srec[samp];rec++)
{
sampleTraits[rec] = T.tr[STattach[ S.id[samp][rec] ]][trait];
}
//calculate the metric of the trait vector for this sample/trait combo
meanvar(sampleTraits, S.srec[samp], &mean, &metric);
STMeanObs[samp][trait] = mean;
traitMetric(sampleTraits, S.srec[samp], &metric, XVAR);
STMetricObs[samp][trait] = metric;
free_vector(sampleTraits,0,S.srec[samp]-1);
}
}
// if BURNIN > 0 and independent or trial swap null, randomize and discard (burn in)
if ((BURNIN > 0) && (SwapMethod==3 || SwapMethod==4)) {
//burn in using appropriate algorithm
if (SwapMethod == 3) {
IndependentSwap(S, BURNIN);
} else {
TrialSwap(S, BURNIN);
}
}
//now repeat calculations for randomized matrices
for (run = 0;run<RUNS;run++)
{
//Swap the sample or trait labels
switch (SwapMethod)
{
case 0:
//SwapMethod 0 = shuffle traits across species
//(sample remains unshuffled)
TraitsAttachShuffle(S,T,STattach);
break;
case 1:
//SwapMethod 1 = samples become random draws from sample taxa list
//(maintains sample species richnesses but not species frequencies)
RandomizeSampleTaxaShuffle(S);
break;
case 2:
//SwapMethod 2 = samples become random draws from traits
//note this is redundant for traits but included for completeness
RandomizeSampleTaxaShuffle(S);
TraitsAttachShuffle(S,T,STattach);
break;
case 3:
//SwapMethod 3 = independent checkerboard swap of sample matrix
//(maintains species frequencies and sample species richnesses)
IndependentSwap(S,SWAPS);
break;
case 4:
//SwapMethod 4 = trial swap of sample matrix
//(maintains species frequencies and sample species richnesses)
TrialSwap(S, SWAPS);
break;
default:
printf("Please use -m command line switch to specify a randomization method.\n");
printf("See documentation for a list of possible null models.\n");
exit(EXIT_FAILURE);
break;
}
for (trait = 0; trait < T.ntraits; trait++)
{
for (samp = 0; samp < S.nsamples; samp++)
{
//(re)dimension a vector to fill with trait values for this sample
sampleTraits = vector(0,S.srec[samp]-1);
//fill the vector
for (rec = 0;rec < S.srec[samp];rec++)
{
sampleTraits[rec] = T.tr[STattach[ S.id[samp][rec] ]][trait];
}
//calculate the metric of the trait vector for this sample/trait combo
meanvar(sampleTraits, S.srec[samp], &mean, &metric);
STMeanRnd[samp][trait][run] = mean;
traitMetric(sampleTraits, S.srec[samp], &metric, XVAR);
STMetricRnd[samp][trait][run] = metric;
free_vector(sampleTraits,0,S.srec[samp]-1);
}
}
}
//calculate summary statistics for randomized data
for (trait = 0;trait < T.ntraits;trait++)
{
for (samp = 0;samp < S.nsamples;samp++)
{
randomMetrics = vector(0,RUNS-1);
for (run = 0;run < RUNS;run++)
{
randomMetrics[run] = STMetricRnd[samp][trait][run];
if (randomMetrics[run] > STMetricObs[samp][trait]) rankHighSTMetricRnd[samp][trait]++;
if (randomMetrics[run] < STMetricObs[samp][trait]) rankLowSTMetricRnd[samp][trait]++;
}
meanvar(randomMetrics,RUNS,&mean,&var);
MeanSTMetricRnd[samp][trait] = mean;
StDevSTMetricRnd[samp][trait] = sqrt(var);
SESSTMetricRnd[samp][trait] = (STMetricObs[samp][trait] - MeanSTMetricRnd[samp][trait]) \
/ StDevSTMetricRnd[samp][trait];
free_vector(randomMetrics,0,RUNS-1);
}
}
//summarize and output
//print header
printf("Phylocom output: randomization method %d, %d runs, trait metric %d (",SwapMethod,RUNS,XVAR);
switch (XVAR)
{
case 1:
printf("variance)\n");
break;
case 2:
printf("MPD)\n");
break;
case 3:
printf("MNTD)\n");
break;
case 4:
printf("range)\n");
break;
default:
printf(")\n");
break;
}
//print results with separate entry for each trait/plot
printf("Trait\tSample\tNTaxa\tMean\tMetric\tMeanRndMetric\tSDRndMetric\tSESMetric\trankLow\trankHigh\truns\n");
for (trait = 0;trait < T.ntraits;trait++)
{
for (samp = 0;samp < S.nsamples;samp++)
{
printf("%s\t%s\t%d\t%f\t%f\t%f\t%f\t%f\t%d\t%d\t%d\n",T.trname[trait],S.pname[samp],S.srec[samp],STMeanObs[samp][trait],STMetricObs[samp][trait],MeanSTMetricRnd[samp][trait],StDevSTMetricRnd[samp][trait],SESSTMetricRnd[samp][trait],rankLowSTMetricRnd[samp][trait],rankHighSTMetricRnd[samp][trait],RUNS);
}
}
}
