int AnovaTest::anovaresi(gsl_matrix *bY, const unsigned int i)
{
unsigned int hid=i, aid = i-1;
// count the right-hand tails
calcSS(bY, &(Hats[aid]), mmRef);
calcSS(bY, &(Hats[hid]), mmRef);
testStatCalc(&(Hats[hid]), &(Hats[aid]), mmRef, TRUE, &(bMultStat), bStatj);
// count data related to P-values
if (bMultStat >= multstat[aid]) Pmultstat[aid]++;
// get result ptr corresponds to model i
double *sj = gsl_matrix_ptr (statj, aid, 0);
double *pj = gsl_matrix_ptr (Pstatj, aid, 0);
double *bj = gsl_vector_ptr (bStatj, 0);
calcAdjustP(mmRef->punit, nVars, bj, sj, pj, sortid[aid]);
return 0;
}
int AnovaTest::anovacase(gsl_matrix *bY, gsl_matrix *bX)
{
unsigned int j;
// if Y col is all zeros
for ( j=0; j<nVars; j++ ){
gsl_vector_view colj = gsl_matrix_column(bY, j);
if ( gsl_vector_isnull(&colj.vector) == TRUE ) return GSL_ERANGE;
}
unsigned int i, hid, aid;
double *sj, *pj, *bj;
gsl_matrix *Z = gsl_matrix_alloc(nRows, nVars);
gsl_matrix_memcpy(Z, bY);
// Hats.X
for (i=0; i<nModels-1; i++){
hid = i+1; aid = i;
gsl_vector_view ref1 = gsl_matrix_row(inRef, aid);
subX(bX, &ref1.vector, Hats[aid].X);
gsl_vector_view ref0 = gsl_matrix_row(inRef, hid);
subX(bX, &ref0.vector, Hats[hid].X);
//Y = X*coef
gsl_blas_dgemm(CblasNoTrans,CblasNoTrans,-1.0,Hats[aid].X,Hats[aid].Coef,0.0,Z);
//Z = bY - Yhat;
gsl_matrix_add (Z, bY);
// calc teststats
calcSS(Z, &(Hats[hid]), mmRef);
calcSS(Z, &(Hats[aid]), mmRef);
testStatCalc(&(Hats[hid]), &(Hats[aid]), mmRef, TRUE, &(bMultStat), bStatj);
if (bMultStat >= multstat[i]) Pmultstat[i]++;
sj = gsl_matrix_ptr (statj, i, 0);
pj = gsl_matrix_ptr (Pstatj, i, 0);
bj = gsl_vector_ptr (bStatj, 0);
calcAdjustP(mmRef->punit, nVars, bj, sj, pj, sortid[i]);
}
gsl_matrix_free(Z);
return 0;
}
int GlmTest::summary(glm *fit)
{
double lambda;
unsigned int k;
unsigned int nRows=tm->nRows, nVars=tm->nVars, nParam=tm->nParam;
unsigned int mtype = fit->mmRef->model-1;
PoissonGlm pNull(fit->mmRef), pAlt(fit->mmRef);
BinGlm binNull(fit->mmRef), binAlt(fit->mmRef);
NBinGlm nbNull(fit->mmRef), nbAlt(fit->mmRef);
glm *PtrNull[3] = { &pNull, &nbNull, &binNull };
glm *PtrAlt[3] = { &pAlt, &nbAlt, &binAlt };
gsl_vector_view teststat, unitstat;
gsl_matrix_view L1;
// To estimate initial Beta from PtrNull->Beta
// gsl_vector *ref=gsl_vector_alloc(nParam);
// gsl_matrix *BetaO=gsl_matrix_alloc(nParam, nVars);
smryStat = gsl_matrix_alloc((nParam+1), nVars+1);
Psmry = gsl_matrix_alloc((nParam+1), nVars+1);
gsl_matrix_set_zero (Psmry);
// initialize the design matrix for all hypo tests
GrpMat *GrpXs = (GrpMat *)malloc((nParam+2)*sizeof(GrpMat));
GrpXs[0].matrix = gsl_matrix_alloc(nRows, nParam);
gsl_matrix_memcpy(GrpXs[0].matrix, fit->Xref); // the alt X
GrpXs[1].matrix = gsl_matrix_alloc(nRows, 1); // overall test
gsl_matrix_set_all (GrpXs[1].matrix, 1.0);
for (k=2; k<nParam+2; k++) { // significance tests
GrpXs[k].matrix = gsl_matrix_alloc(nRows, nParam-1);
subX2(fit->Xref, k-2, GrpXs[k].matrix);
}
// Calc test statistics
if ( tm->test == WALD ) {
// the overall test compares to mean
teststat = gsl_matrix_row(smryStat, 0);
L1=gsl_matrix_submatrix(L,1,0,nParam-1,nParam);
lambda=gsl_vector_get(tm->smry_lambda, 0);
GetR(fit->Res, tm->corr, lambda, Rlambda);
GeeWald(fit, &L1.matrix, &teststat.vector);
// the significance test
for (k=2; k<nParam+2; k++) {
teststat = gsl_matrix_row(smryStat, k-1);
L1 = gsl_matrix_submatrix(L, k-2, 0, 1, nParam);
GeeWald(fit, &L1.matrix, &teststat.vector);
}
}
else if (tm->test==SCORE) {
for (k=1; k<nParam+2; k++) {
teststat=gsl_matrix_row(smryStat, k-1);
PtrNull[mtype]->regression(fit->Yref,GrpXs[k].matrix,fit->Oref,NULL);
lambda=gsl_vector_get(tm->smry_lambda, k);
GetR(PtrNull[mtype]->Res, tm->corr, lambda, Rlambda);
GeeScore(GrpXs[0].matrix, PtrNull[mtype], &teststat.vector);
}
}
else {
for (k=1; k<nParam+2; k++) {
teststat=gsl_matrix_row(smryStat, k-1);
PtrNull[mtype]->regression(fit->Yref,GrpXs[k].matrix,fit->Oref,NULL);
GeeLR(fit, PtrNull[mtype], &teststat.vector); // works better
}
}
// sort id if the unitvaraite test is free step-down
gsl_permutation **sortid;
sortid=(gsl_permutation **)malloc((nParam+1)*sizeof(gsl_permutation *));
for ( k=0; k<(nParam+1); k++ ) {
teststat = gsl_matrix_row (smryStat, k);
unitstat = gsl_vector_subvector(&teststat.vector, 1, nVars);
sortid[k] = gsl_permutation_alloc(nVars);
gsl_sort_vector_index (sortid[k], &unitstat.vector);
gsl_permutation_reverse(sortid[k]); // rearrange in descending order
}
if (tm->resamp==MONTECARLO) {
lambda=gsl_vector_get(tm->smry_lambda,0);
GetR(fit->Res, tm->corr, lambda, Sigma);
setMonteCarlo(fit, XBeta, Sigma);
}
nSamp=0;
double *suj, *buj, *puj;
gsl_matrix *bStat = gsl_matrix_alloc((nParam+1), nVars+1);
gsl_matrix_set_zero (bStat);
gsl_matrix *bY = gsl_matrix_alloc(nRows, nVars);
gsl_matrix *bO = gsl_matrix_alloc(nRows, nVars);
gsl_matrix_memcpy (bO, fit->Eta);
double diff, timelast=0;
clock_t clk_start=clock();
for ( unsigned int i=0; i<tm->nboot; i++) {
if ( tm->resamp==CASEBOOT )
resampSmryCase(fit,bY,GrpXs,bO,i);
else resampNonCase(fit, bY, i);
if ( tm->test == WALD ) {
PtrAlt[mtype]->regression(bY,GrpXs[0].matrix,bO,NULL);
// the overall test compares to mean
teststat = gsl_matrix_row(bStat, 0);
L1=gsl_matrix_submatrix(L,1,0,nParam-1,nParam);
lambda=gsl_vector_get(tm->smry_lambda, 0);
GetR(PtrAlt[mtype]->Res, tm->corr, lambda, Rlambda);
GeeWald(PtrAlt[mtype], &L1.matrix, &teststat.vector);
// the significance test
for (k=2; k<nParam+2; k++) {
teststat = gsl_matrix_row(bStat, k-1);
L1 = gsl_matrix_submatrix(L, k-2, 0, 1, nParam);
GeeWald(PtrAlt[mtype], &L1.matrix, &teststat.vector);
}
}
else if (tm->test==SCORE) {
for (k=1; k<nParam+2; k++) {
teststat=gsl_matrix_row(bStat, k-1);
PtrNull[mtype]->regression(bY,GrpXs[k].matrix,bO,NULL);
lambda=gsl_vector_get(tm->smry_lambda,k);
GetR(PtrNull[mtype]->Res, tm->corr, lambda, Rlambda);
GeeScore(GrpXs[0].matrix, PtrNull[mtype], &teststat.vector);
}
}
else { // use single bAlt estimate works better
PtrAlt[mtype]->regression(bY,GrpXs[0].matrix,bO,NULL);
for (k=1; k<nParam+2; k++) {
teststat=gsl_matrix_row(bStat, k-1);
PtrNull[mtype]->regression(bY,GrpXs[k].matrix,bO,NULL);
GeeLR(PtrAlt[mtype], PtrNull[mtype], &teststat.vector);
}
}
for (k=0; k<(nParam+1); k++) {
buj = gsl_matrix_ptr (bStat, k, 0);
suj = gsl_matrix_ptr (smryStat, k, 0);
puj = gsl_matrix_ptr (Psmry, k, 0);
if ( *buj >= *suj ) *puj=*puj+1;
calcAdjustP(tm->punit, nVars, buj+1, suj+1, puj+1, sortid[k]);
} // end for j loop
nSamp++;
// Prompts
if ((tm->showtime==TRUE)&(i%100==0)) {
diff=(float)(clock()-clk_start)/(float)CLOCKS_PER_SEC;
timelast+=(double)diff/60;
printf("\tResampling run %d finished. Time elapsed: %.2f min ...\n", i, timelast);
clk_start=clock();
}
} // end for i loop
// ========= Get P-values ========= //
if ( tm->punit == FREESTEP ) {
for (k=0; k<(nParam+1); k++) {
puj = gsl_matrix_ptr (Psmry, k, 1);
reinforceP( puj, nVars, sortid[k] );
} }
// p = (#exceeding observed stat + 1)/(#nboot+1)
gsl_matrix_add_constant (Psmry, 1.0);
gsl_matrix_scale (Psmry, (double)1.0/(nSamp+1));
for (k=0; k<nVars; k++) aic[k]=-fit->ll[k]+2*(nParam+1);
// === release memory ==== //
PtrAlt[mtype]->releaseGlm();
if ( tm->test!=WALD )
PtrNull[mtype]->releaseGlm();
gsl_matrix_free(bStat);
gsl_matrix_free(bY);
gsl_matrix_free(bO);
for (k=0; k<nParam+1; k++)
if (sortid[k]!=NULL) gsl_permutation_free(sortid[k]);
free(sortid);
if ( GrpXs != NULL ) {
for ( unsigned int k=0; k<nParam+2; k++ )
if ( GrpXs[k].matrix != NULL )
gsl_matrix_free (GrpXs[k].matrix);
free(GrpXs);
}
return SUCCESS;
}
int GlmTest::anova(glm *fit, gsl_matrix *isXvarIn)
{
// Assume the models have been already sorted (in R)
Xin = isXvarIn;
nModels = Xin->size1;
double *rdf = new double [nModels];
unsigned int nP, i, j, k;
unsigned int ID0, ID1, nP0, nP1;
unsigned int nRows=tm->nRows, nVars=tm->nVars, nParam=tm->nParam;
unsigned int mtype = fit->mmRef->model-1;
dfDiff = new unsigned int [nModels-1];
anovaStat = gsl_matrix_alloc((nModels-1), nVars+1);
Panova = gsl_matrix_alloc((nModels-1), nVars+1);
gsl_vector *bStat = gsl_vector_alloc(nVars+1);
gsl_matrix_set_zero (anovaStat);
gsl_matrix_set_zero (Panova);
gsl_vector_set_zero (bStat);
PoissonGlm pNull(fit->mmRef), pAlt(fit->mmRef);
BinGlm binNull(fit->mmRef), binAlt(fit->mmRef);
NBinGlm nbNull(fit->mmRef), nbAlt(fit->mmRef);
PoissonGlm pNullb(fit->mmRef), pAltb(fit->mmRef);
BinGlm binNullb(fit->mmRef), binAltb(fit->mmRef);
NBinGlm nbNullb(fit->mmRef), nbAltb(fit->mmRef);
glm *PtrNull[3] = { &pNull, &nbNull, &binNull };
glm *PtrAlt[3] = { &pAlt, &nbAlt, &binAlt };
glm *bNull[3] = { &pNullb, &nbNullb, &binNullb };
glm *bAlt[3] = { &pAltb, &nbAltb, &binAltb };
double *suj, *buj, *puj;
gsl_vector_view teststat, unitstat,ref1, ref0;
gsl_matrix *X0=NULL, *X1=NULL, *L1=NULL, *tmp1=NULL, *BetaO=NULL;
gsl_matrix *bO=NULL, *bY=gsl_matrix_alloc(nRows, nVars);
bO = gsl_matrix_alloc(nRows, nVars);
gsl_permutation *sortid=NULL;
if (tm->punit==FREESTEP) sortid = gsl_permutation_alloc(nVars);
// ======= Fit the (first) Alt model =========//
for (i=0; i<nModels; i++) {
nP = 0;
for (k=0; k<nParam; k++)
if (gsl_matrix_get(Xin,i,k)!=FALSE) nP++;
rdf[i] = nRows-nP;
}
for (i=1; i<nModels; i++) {
// ======= Fit the Null model =========//
ID0 = i; ID1 = i-1;
nP0 = nRows - (unsigned int)rdf[ID0];
nP1 = nRows - (unsigned int)rdf[ID1];
// Degrees of freedom
dfDiff[i-1] = nP1 - nP0;
ref1=gsl_matrix_row(Xin, ID1);
ref0=gsl_matrix_row(Xin, ID0);
X0 = gsl_matrix_alloc(nRows, nP0);
subX(fit->Xref, &ref0.vector, X0);
X1 = gsl_matrix_alloc(nRows, nP1);
subX(fit->Xref, &ref1.vector, X1);
// ======= Get multivariate test statistics =======//
// Estimate shrinkage parametr only once under H1
// See "FW: Doubts R package "mvabund" (12/14/11)
teststat = gsl_matrix_row(anovaStat, (i-1));
PtrNull[mtype]->regression(fit->Yref, X0, fit->Oref, NULL);
if (tm->test == SCORE) {
lambda = gsl_vector_get(tm->anova_lambda, ID0);
GetR(PtrNull[mtype]->Res, tm->corr, lambda, Rlambda);
GeeScore(X1, PtrNull[mtype], &teststat.vector);
}
else if (tm->test==WALD) {
PtrAlt[mtype]->regression(fit->Yref, X1, fit->Oref, NULL);
L1 = gsl_matrix_alloc (nP1-nP0, nP1);
tmp1 = gsl_matrix_alloc (nParam, nP1);
subX(L, &ref1.vector, tmp1);
subXrow1(tmp1, &ref0.vector, &ref1.vector, L1);
lambda = gsl_vector_get(tm->anova_lambda, ID1);
GetR(PtrAlt[mtype]->Res, tm->corr, lambda, Rlambda);
GeeWald(PtrAlt[mtype], L1, &teststat.vector);
}
else {
BetaO = gsl_matrix_alloc(nP1, nVars);
addXrow2(PtrNull[mtype]->Beta, &ref1.vector, BetaO);
PtrAlt[mtype]->regression(fit->Yref, X1, fit->Oref, BetaO);
GeeLR(PtrAlt[mtype], PtrNull[mtype], &teststat.vector);
}
if (tm->resamp==MONTECARLO) {
lambda=gsl_vector_get(tm->anova_lambda,ID0);
GetR(fit->Res, tm->corr, lambda, Sigma);
setMonteCarlo (PtrNull[mtype], XBeta, Sigma);
}
// ======= Get univariate test statistics =======//
if (tm->punit == FREESTEP) {
unitstat=gsl_vector_subvector(&teststat.vector,1,nVars);
gsl_sort_vector_index (sortid, &unitstat.vector);
gsl_permutation_reverse(sortid);
}
// ======= Get resampling distribution under H0 ===== //
nSamp=0;
double dif, timelast=0;
clock_t clk_start=clock();
if (tm->showtime==TRUE)
printf("Resampling begins for test %d.\n", i);
for (j=0; j<tm->nboot; j++) {
// printf("simu %d :", j);
gsl_vector_set_zero (bStat);
if ( tm->resamp == CASEBOOT ) {
resampAnovaCase(PtrAlt[mtype],bY,X1,bO,j);
subX(X1, &ref0.vector, X0);
}
else {
resampNonCase(PtrNull[mtype], bY, j);
gsl_matrix_memcpy(bO, fit->Oref);
}
if ( tm->test == WALD ) {
bAlt[mtype]->regression(bY,X1,bO,NULL);
lambda = gsl_vector_get(tm->anova_lambda, ID1);
GetR(bAlt[mtype]->Res, tm->corr, lambda, Rlambda);
GeeWald(bAlt[mtype], L1, bStat);
}
else if ( tm->test == SCORE ) {
bNull[mtype]->regression(bY,X0,bO,NULL);
lambda = gsl_vector_get(tm->anova_lambda, ID0);
GetR(bNull[mtype]->Res, tm->corr, lambda, Rlambda);
GeeScore(X1, bNull[mtype], bStat);
}
else {
bNull[mtype]->regression(bY,X0,bO,NULL);
addXrow2(bNull[mtype]->Beta, &ref1.vector, BetaO);
bAlt[mtype]->regression(bY,X1,bO,BetaO);
GeeLR(bAlt[mtype], bNull[mtype], bStat);
}
// ----- get multivariate counts ------- //
buj = gsl_vector_ptr (bStat,0);
suj = gsl_matrix_ptr (anovaStat, i-1, 0);
puj = gsl_matrix_ptr (Panova, i-1, 0);
if ( *(buj) > (*(suj)-1e-8) ) *puj=*puj+1;
// ------ get univariate counts ---------//
calcAdjustP(tm->punit,nVars,buj+1,suj+1,puj+1,sortid);
nSamp++;
// Prompts
if ((tm->showtime==TRUE)&(j%100==0)) {
dif = (float)(clock() - clk_start)/(float)CLOCKS_PER_SEC;
timelast+=(double)dif/60;
printf("\tResampling run %d finished. Time elapsed: %.2f minutes...\n", j, timelast);
clk_start=clock();
}
} // end j for loop
// ========= get p-values ======== //
if ( tm->punit == FREESTEP) {
puj = gsl_matrix_ptr (Panova, i-1, 1);
reinforceP(puj, nVars, sortid);
}
if (BetaO!=NULL) gsl_matrix_free(BetaO);
if (X0!=NULL) gsl_matrix_free(X0);
if (X1!=NULL) gsl_matrix_free(X1);
if (tm->test == WALD) {
if (L1!=NULL) gsl_matrix_free(L1);
if (tmp1!=NULL) gsl_matrix_free(tmp1);
}
} // end i for loop and test for loop
// p = (#exceeding observed stat + 1)/(#nboot+1)
gsl_matrix_add_constant (Panova, 1.0);
gsl_matrix_scale (Panova, (double)1/(nSamp+1.0));
bAlt[mtype]->releaseGlm();
PtrAlt[mtype]->releaseGlm();
if ( tm->test!=WALD ) {
bNull[mtype]->releaseGlm();
PtrNull[mtype]->releaseGlm();
}
delete []rdf;
if (sortid != NULL )
gsl_permutation_free(sortid);
gsl_vector_free(bStat);
gsl_matrix_free(bY);
if (bO!=NULL) gsl_matrix_free(bO);
return SUCCESS;
}