omxExpectation* omxNewIncompleteExpectation(SEXP rObj, int expNum, omxState* os) { SEXP ExpectationClass; const char *expType; {ScopedProtect p1(ExpectationClass, STRING_ELT(Rf_getAttrib(rObj, Rf_install("class")), 0)); expType = CHAR(ExpectationClass); } omxExpectation* expect = omxNewInternalExpectation(expType, os); expect->rObj = rObj; expect->expNum = expNum; SEXP nextMatrix; {ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("data"))); expect->data = omxDataLookupFromState(nextMatrix, os); } return expect; }
omxExpectation* omxNewIncompleteExpectation(SEXP rObj, int expNum, omxState* os) { SEXP ExpectationClass; const char *expType; {ScopedProtect p1(ExpectationClass, STRING_ELT(Rf_getAttrib(rObj, R_ClassSymbol), 0)); expType = CHAR(ExpectationClass); } omxExpectation* expect = omxNewInternalExpectation(expType, os); expect->rObj = rObj; expect->expNum = expNum; ProtectedSEXP Rdata(R_do_slot(rObj, Rf_install("data"))); if (TYPEOF(Rdata) == INTSXP) { expect->data = omxDataLookupFromState(Rdata, os); } return expect; }
void omxInitRowFitFunction(omxFitFunction* oo) { if(OMX_DEBUG) { mxLog("Initializing Row/Reduce fit function."); } SEXP rObj = oo->rObj; SEXP nextMatrix, nextItem; int numDeps; omxRowFitFunction *newObj = new omxRowFitFunction; if(OMX_DEBUG) {mxLog("Accessing data source."); } {ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("data"))); newObj->data = omxDataLookupFromState(nextMatrix, oo->matrix->currentState); if(newObj->data == NULL) { char *errstr = (char*) calloc(250, sizeof(char)); sprintf(errstr, "No data provided to omxRowFitFunction."); omxRaiseError(errstr); free(errstr); } } {ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("rowAlgebra"))); newObj->rowAlgebra = omxMatrixLookupFromState1(nextMatrix, oo->matrix->currentState); if(newObj->rowAlgebra == NULL) { char *errstr = (char*) calloc(250, sizeof(char)); sprintf(errstr, "No row-wise algebra in omxRowFitFunction."); omxRaiseError(errstr); free(errstr); } } { ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("units"))); oo->setUnitsFromName(CHAR(STRING_ELT(nextMatrix, 0))); } {ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("filteredDataRow"))); newObj->filteredDataRow = omxMatrixLookupFromState1(nextMatrix, oo->matrix->currentState); } if(newObj->filteredDataRow == NULL) { char *errstr = (char*) calloc(250, sizeof(char)); sprintf(errstr, "No row results matrix in omxRowFitFunction."); omxRaiseError(errstr); free(errstr); } // Create the original data row from which to filter. newObj->dataRow = omxInitMatrix(newObj->filteredDataRow->rows, newObj->filteredDataRow->cols, TRUE, oo->matrix->currentState); omxCopyMatrix(newObj->filteredDataRow, newObj->dataRow); {ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("existenceVector"))); newObj->existenceVector = omxMatrixLookupFromState1(nextMatrix, oo->matrix->currentState); } // Do we allow NULL existence? (Whoa, man. That's, like, deep, or something.) if(newObj->existenceVector == NULL) { char *errstr = (char*) calloc(250, sizeof(char)); sprintf(errstr, "No existance matrix in omxRowFitFunction."); omxRaiseError(errstr); free(errstr); } {ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("rowResults"))); newObj->rowResults = omxMatrixLookupFromState1(nextMatrix, oo->matrix->currentState); } if(newObj->rowResults == NULL) { char *errstr = (char*) calloc(250, sizeof(char)); sprintf(errstr, "No row results matrix in omxRowFitFunction."); omxRaiseError(errstr); free(errstr); } {ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("reduceAlgebra"))); newObj->reduceAlgebra = omxMatrixLookupFromState1(nextMatrix, oo->matrix->currentState); } if(newObj->reduceAlgebra == NULL) { char *errstr = (char*) calloc(250, sizeof(char)); sprintf(errstr, "No row reduction algebra in omxRowFitFunction."); omxRaiseError(errstr); free(errstr); } if(OMX_DEBUG) {mxLog("Accessing variable mapping structure."); } {ScopedProtect p1(nextMatrix, R_do_slot(rObj, Rf_install("dataColumns"))); newObj->dataColumns = omxNewMatrixFromRPrimitive(nextMatrix, oo->matrix->currentState, 0, 0); } if(OMX_DEBUG) { omxPrint(newObj->dataColumns, "Variable mapping"); } if(OMX_DEBUG) {mxLog("Accessing data row dependencies."); } { ScopedProtect p1(nextItem, R_do_slot(rObj, Rf_install("dataRowDeps"))); numDeps = LENGTH(nextItem); newObj->numDataRowDeps = numDeps; newObj->dataRowDeps = (int*) R_alloc(numDeps, sizeof(int)); for(int i = 0; i < numDeps; i++) { newObj->dataRowDeps[i] = INTEGER(nextItem)[i]; } } /* Set up data columns */ EigenVectorAdaptor dc(newObj->dataColumns); omxSetContiguousDataColumns(&(newObj->contiguous), newObj->data, dc); oo->computeFun = omxCallRowFitFunction; oo->destructFun = omxDestroyRowFitFunction; oo->canDuplicate = true; oo->openmpUser = true; oo->argStruct = (void*) newObj; }
void omxInitExpectationBA81(omxExpectation* oo) { omxState* currentState = oo->currentState; SEXP rObj = oo->rObj; SEXP tmp; if(OMX_DEBUG) { mxLog("Initializing %s.", oo->name); } if (!Glibrpf_model) { #if USE_EXTERNAL_LIBRPF get_librpf_t get_librpf = (get_librpf_t) R_GetCCallable("rpf", "get_librpf_model_GPL"); (*get_librpf)(LIBIFA_RPF_API_VERSION, &Glibrpf_numModels, &Glibrpf_model); #else // if linking against included source code Glibrpf_numModels = librpf_numModels; Glibrpf_model = librpf_model; #endif } BA81Expect *state = new BA81Expect; // These two constants should be as identical as possible state->name = oo->name; if (0) { state->LogLargestDouble = 0.0; state->LargestDouble = 1.0; } else { state->LogLargestDouble = log(std::numeric_limits<double>::max()) - 1; state->LargestDouble = exp(state->LogLargestDouble); ba81NormalQuad &quad = state->getQuad(); quad.setOne(state->LargestDouble); } state->expectedUsed = false; state->estLatentMean = NULL; state->estLatentCov = NULL; state->type = EXPECTATION_OBSERVED; state->itemParam = NULL; state->EitemParam = NULL; state->itemParamVersion = 0; state->latentParamVersion = 0; oo->argStruct = (void*) state; {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("data"))); state->data = omxDataLookupFromState(tmp, currentState); } if (strcmp(omxDataType(state->data), "raw") != 0) { omxRaiseErrorf("%s unable to handle data type %s", oo->name, omxDataType(state->data)); return; } {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("verbose"))); state->verbose = Rf_asInteger(tmp); } int targetQpoints; {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("qpoints"))); targetQpoints = Rf_asInteger(tmp); } {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("qwidth"))); state->grp.setGridFineness(Rf_asReal(tmp), targetQpoints); } {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("ItemSpec"))); state->grp.importSpec(tmp); if (state->verbose >= 2) mxLog("%s: found %d item specs", oo->name, state->numItems()); } state->_latentMeanOut = omxNewMatrixFromSlot(rObj, currentState, "mean"); state->_latentCovOut = omxNewMatrixFromSlot(rObj, currentState, "cov"); state->itemParam = omxNewMatrixFromSlot(rObj, currentState, "item"); state->grp.param = state->itemParam->data; // algebra not allowed yet TODO const int numItems = state->itemParam->cols; if (state->numItems() != numItems) { omxRaiseErrorf("ItemSpec length %d must match the number of item columns (%d)", state->numItems(), numItems); return; } if (state->itemParam->rows != state->grp.impliedParamRows) { omxRaiseErrorf("item matrix must have %d rows", state->grp.impliedParamRows); return; } state->grp.paramRows = state->itemParam->rows; // for algebra item param, will need to defer until later? state->grp.learnMaxAbilities(); int maxAbilities = state->grp.itemDims; state->grp.setFactorNames(state->itemParam->rownames); { ProtectedSEXP tmp2(R_do_slot(rObj, Rf_install(".detectIndependence"))); state->grp.detectIndependence = Rf_asLogical(tmp2); } {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("EstepItem"))); if (!Rf_isNull(tmp)) { int rows, cols; getMatrixDims(tmp, &rows, &cols); if (rows != state->itemParam->rows || cols != state->itemParam->cols) { Rf_error("EstepItem must have the same dimensions as the item MxMatrix"); } state->EitemParam = REAL(tmp); } } oo->computeFun = ba81compute; oo->setVarGroup = ignoreSetVarGroup; oo->destructFun = ba81Destroy; oo->populateAttrFun = ba81PopulateAttributes; oo->componentFun = getComponent; oo->canDuplicate = false; // TODO: Exactly identical rows do not contribute any information. // The sorting algorithm ought to remove them so we get better cache behavior. // The following summary stats would be cheaper to calculate too. omxData *data = state->data; if (data->hasDefinitionVariables()) Rf_error("%s: not implemented yet", oo->name); std::vector<int> &rowMap = state->grp.rowMap; int weightCol; {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("weightColumn"))); weightCol = INTEGER(tmp)[0]; } if (weightCol == NA_INTEGER) { // Should rowMap be part of omxData? This is essentially a // generic compression step that shouldn't be specific to IFA models. state->grp.rowWeight = (double*) R_alloc(data->rows, sizeof(double)); rowMap.resize(data->rows); int numUnique = 0; for (int rx=0; rx < data->rows; ) { int rw = 1; state->grp.rowWeight[numUnique] = rw; rowMap[numUnique] = rx; rx += rw; ++numUnique; } rowMap.resize(numUnique); state->weightSum = state->data->rows; } else { if (omxDataColumnIsFactor(data, weightCol)) { omxRaiseErrorf("%s: weightColumn %d is a factor", oo->name, 1 + weightCol); return; } state->grp.rowWeight = omxDoubleDataColumn(data, weightCol); state->weightSum = 0; for (int rx=0; rx < data->rows; ++rx) { state->weightSum += state->grp.rowWeight[rx]; } rowMap.resize(data->rows); for (size_t rx=0; rx < rowMap.size(); ++rx) { rowMap[rx] = rx; } } // complain about non-integral rowWeights (EAP can't work) TODO auto colMap = oo->getDataColumns(); for (int cx = 0; cx < numItems; cx++) { int *col = omxIntDataColumnUnsafe(data, colMap[cx]); state->grp.dataColumns.push_back(col); } // sanity check data for (int cx = 0; cx < numItems; cx++) { if (!omxDataColumnIsFactor(data, colMap[cx])) { data->omxPrintData("diagnostic", 3); omxRaiseErrorf("%s: column %d is not a factor", oo->name, int(1 + colMap[cx])); return; } } // TODO the max outcome should be available from omxData for (int rx=0; rx < data->rows; rx++) { int cols = 0; for (int cx = 0; cx < numItems; cx++) { const int *col = state->grp.dataColumns[cx]; int pick = col[rx]; if (pick == NA_INTEGER) continue; ++cols; const int no = state->grp.itemOutcomes[cx]; if (pick > no) { Rf_error("Data for item '%s' has at least %d outcomes, not %d", state->itemParam->colnames[cx], pick, no); } } if (cols == 0) { Rf_error("Row %d has all NAs", 1+rx); } } if (state->_latentMeanOut && state->_latentMeanOut->rows * state->_latentMeanOut->cols != maxAbilities) { Rf_error("The mean matrix '%s' must be a row or column vector of size %d", state->_latentMeanOut->name(), maxAbilities); } if (state->_latentCovOut && (state->_latentCovOut->rows != maxAbilities || state->_latentCovOut->cols != maxAbilities)) { Rf_error("The cov matrix '%s' must be %dx%d", state->_latentCovOut->name(), maxAbilities, maxAbilities); } state->grp.setLatentDistribution(state->_latentMeanOut? state->_latentMeanOut->data : NULL, state->_latentCovOut? state->_latentCovOut->data : NULL); { EigenArrayAdaptor Eparam(state->itemParam); Eigen::Map< Eigen::VectorXd > meanVec(state->grp.mean, maxAbilities); Eigen::Map< Eigen::MatrixXd > covMat(state->grp.cov, maxAbilities, maxAbilities); state->grp.quad.setStructure(state->grp.qwidth, state->grp.qpoints, Eparam, meanVec, covMat); } {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("minItemsPerScore"))); state->grp.setMinItemsPerScore(Rf_asInteger(tmp)); } state->grp.buildRowSkip(); if (isErrorRaised()) return; {ScopedProtect p1(tmp, R_do_slot(rObj, Rf_install("debugInternal"))); state->debugInternal = Rf_asLogical(tmp); } state->ElatentVersion = 0; if (state->_latentMeanOut) { state->estLatentMean = omxInitMatrix(maxAbilities, 1, TRUE, currentState); omxCopyMatrix(state->estLatentMean, state->_latentMeanOut); // rename matrices TODO } if (state->_latentCovOut) { state->estLatentCov = omxInitMatrix(maxAbilities, maxAbilities, TRUE, currentState); omxCopyMatrix(state->estLatentCov, state->_latentCovOut); } }