static SEXP rpf_logprob_wrapper(SEXP r_spec, SEXP r_param, SEXP r_theta) { omxManageProtectInsanity mpi; if (Rf_length(r_spec) < RPF_ISpecCount) Rf_error("Item spec must be of length %d, not %d", RPF_ISpecCount, Rf_length(r_spec)); double *spec = REAL(r_spec); int id = spec[RPF_ISpecID]; if (id < 0 || id >= Glibrpf_numModels) Rf_error("Item model %d out of range", id); int numSpec = (*Glibrpf_model[id].numSpec)(spec); if (Rf_length(r_spec) < numSpec) Rf_error("Item spec must be of length %d, not %d", numSpec, Rf_length(r_spec)); int numParam = (*Glibrpf_model[id].numParam)(spec); if (Rf_length(r_param) < numParam) Rf_error("Item has %d parameters, only %d given", numParam, Rf_length(r_param)); const int numOutcomes = spec[RPF_ISpecOutcomes]; const int dims = spec[RPF_ISpecDims]; int numPeople = 1; double *param = REAL(r_param); int numAbilities = 1; if (dims == 0) { if (Rf_length(r_theta)) numPeople = Rf_length(r_theta); } else if (dims == 1) { numPeople = Rf_length(r_theta); } else { getMatrixDims(r_theta, &numAbilities, &numPeople); } SEXP outsxp; Rf_protect(outsxp = Rf_allocMatrix(REALSXP, numOutcomes, numPeople)); double *out = REAL(outsxp); double *theta = NULL; if (dims) theta = REAL(r_theta); Eigen::VectorXd thBuf(dims); for (int px=0; px < numPeople; px++) { if (dims && !unpack_theta(dims, param, numAbilities, theta + px*numAbilities, thBuf.data())) { for (int ox=0; ox < numOutcomes; ox++) { out[px*numOutcomes + ox] = NA_REAL; } continue; } (*Glibrpf_model[id].logprob)(spec, param, thBuf.data(), out+px*numOutcomes); for (int ox=0; ox < numOutcomes; ox++) { double prob = out[px*numOutcomes + ox]; if (!std::isfinite(prob)) { out[px*numOutcomes + ox] = NA_REAL; // legitimate (e.g., grm thresholds misordered) } } } return outsxp; }
static SEXP rpf_rescale_wrapper(SEXP r_spec, SEXP r_param, SEXP r_mean, SEXP r_cov) { if (Rf_length(r_spec) < RPF_ISpecCount) Rf_error("Item spec must be of length %d, not %d", RPF_ISpecCount, Rf_length(r_spec)); double *spec = REAL(r_spec); int id = spec[RPF_ISpecID]; if (id < 0 || id >= Glibrpf_numModels) Rf_error("Item model %d out of range", id); int numSpec = (*Glibrpf_model[id].numSpec)(spec); if (Rf_length(r_spec) < numSpec) Rf_error("Item spec must be of length %d, not %d", numSpec, Rf_length(r_spec)); int numParam = (*Glibrpf_model[id].numParam)(spec); if (Rf_length(r_param) < numParam) Rf_error("Item has %d parameters, only %d given", numParam, Rf_length(r_param)); int dims = spec[RPF_ISpecDims]; if (dims == 0) Rf_error("Item has no factors"); if (Rf_length(r_mean) != dims) Rf_error("Item has %d dimensions, but mean is of length %d", dims, Rf_length(r_mean)); int cov_rows, cov_cols; getMatrixDims(r_cov, &cov_rows, &cov_cols); if (cov_rows != dims || cov_rows != dims) Rf_error("Item has %d dimensions, but cov is %dx%d", dims, cov_rows, cov_cols); Eigen::VectorXi mask(numParam); mask.setZero(); SEXP ret; Rf_protect(ret = Rf_allocVector(REALSXP, numParam)); memcpy(REAL(ret), REAL(r_param), sizeof(double) * numParam); (*Glibrpf_model[id].rescale)(spec, REAL(ret), mask.data(), REAL(r_mean), REAL(r_cov)); UNPROTECT(1); return ret; }
void ifaGroup::import(SEXP Rlist) { SEXP argNames; Rf_protect(argNames = Rf_getAttrib(Rlist, R_NamesSymbol)); if (Rf_length(Rlist) != Rf_length(argNames)) { mxThrow("All list elements must be named"); } std::vector<const char *> dataColNames; paramRows = -1; int pmatCols=-1; int mips = 1; int dataRows = 0; SEXP Rmean=0, Rcov=0; for (int ax=0; ax < Rf_length(Rlist); ++ax) { const char *key = R_CHAR(STRING_ELT(argNames, ax)); SEXP slotValue = VECTOR_ELT(Rlist, ax); if (strEQ(key, "spec")) { importSpec(slotValue); } else if (strEQ(key, "param")) { if (!Rf_isReal(slotValue)) mxThrow("'param' must be a numeric matrix of item parameters"); param = REAL(slotValue); getMatrixDims(slotValue, ¶mRows, &pmatCols); SEXP dimnames; Rf_protect(dimnames = Rf_getAttrib(slotValue, R_DimNamesSymbol)); if (!Rf_isNull(dimnames) && Rf_length(dimnames) == 2) { SEXP names; Rf_protect(names = VECTOR_ELT(dimnames, 0)); int nlen = Rf_length(names); factorNames.resize(nlen); for (int nx=0; nx < nlen; ++nx) { factorNames[nx] = CHAR(STRING_ELT(names, nx)); } Rf_protect(names = VECTOR_ELT(dimnames, 1)); nlen = Rf_length(names); itemNames.resize(nlen); for (int nx=0; nx < nlen; ++nx) { itemNames[nx] = CHAR(STRING_ELT(names, nx)); } } } else if (strEQ(key, "mean")) { Rmean = slotValue; if (!Rf_isReal(slotValue)) mxThrow("'mean' must be a numeric vector or matrix"); mean = REAL(slotValue); } else if (strEQ(key, "cov")) { Rcov = slotValue; if (!Rf_isReal(slotValue)) mxThrow("'cov' must be a numeric matrix"); cov = REAL(slotValue); } else if (strEQ(key, "data")) { Rdata = slotValue; dataRows = Rf_length(VECTOR_ELT(Rdata, 0)); SEXP names; Rf_protect(names = Rf_getAttrib(Rdata, R_NamesSymbol)); int nlen = Rf_length(names); dataColNames.reserve(nlen); for (int nx=0; nx < nlen; ++nx) { dataColNames.push_back(CHAR(STRING_ELT(names, nx))); } Rf_protect(dataRowNames = Rf_getAttrib(Rdata, R_RowNamesSymbol)); } else if (strEQ(key, "weightColumn")) { if (Rf_length(slotValue) != 1) { mxThrow("You can only have one %s", key); } weightColumnName = CHAR(STRING_ELT(slotValue, 0)); } else if (strEQ(key, "freqColumn")) { if (Rf_length(slotValue) != 1) { mxThrow("You can only have one %s", key); } freqColumnName = CHAR(STRING_ELT(slotValue, 0)); } else if (strEQ(key, "qwidth")) { qwidth = Rf_asReal(slotValue); } else if (strEQ(key, "qpoints")) { qpoints = Rf_asInteger(slotValue); } else if (strEQ(key, "minItemsPerScore")) { mips = Rf_asInteger(slotValue); } else { // ignore } } learnMaxAbilities(); if (itemDims < (int) factorNames.size()) factorNames.resize(itemDims); if (int(factorNames.size()) < itemDims) { factorNames.reserve(itemDims); const int SMALLBUF = 24; char buf[SMALLBUF]; while (int(factorNames.size()) < itemDims) { snprintf(buf, SMALLBUF, "s%d", int(factorNames.size()) + 1); factorNames.push_back(CHAR(Rf_mkChar(buf))); } } if (Rmean) { if (Rf_isMatrix(Rmean)) { int nrow, ncol; getMatrixDims(Rmean, &nrow, &ncol); if (!(nrow * ncol == itemDims && (nrow==1 || ncol==1))) { mxThrow("mean must be a column or row matrix of length %d", itemDims); } } else { if (Rf_length(Rmean) != itemDims) { mxThrow("mean must be a vector of length %d", itemDims); } } verifyFactorNames(Rmean, "mean"); } if (Rcov) { if (Rf_isMatrix(Rcov)) { int nrow, ncol; getMatrixDims(Rcov, &nrow, &ncol); if (nrow != itemDims || ncol != itemDims) { mxThrow("cov must be %dx%d matrix", itemDims, itemDims); } } else { if (Rf_length(Rcov) != 1) { mxThrow("cov must be %dx%d matrix", itemDims, itemDims); } } verifyFactorNames(Rcov, "cov"); } setLatentDistribution(mean, cov); setMinItemsPerScore(mips); if (numItems() != pmatCols) { mxThrow("item matrix implies %d items but spec is length %d", pmatCols, numItems()); } if (Rdata) { if (itemNames.size() == 0) mxThrow("Item matrix must have colnames"); for (int ix=0; ix < numItems(); ++ix) { bool found=false; for (int dc=0; dc < int(dataColNames.size()); ++dc) { if (strEQ(itemNames[ix], dataColNames[dc])) { SEXP col = VECTOR_ELT(Rdata, dc); if (!Rf_isFactor(col)) { if (TYPEOF(col) == INTSXP) { mxThrow("Column '%s' is an integer but " "not an ordered factor", dataColNames[dc]); } else { mxThrow("Column '%s' is of type %s; expecting an " "ordered factor (integer)", dataColNames[dc], Rf_type2char(TYPEOF(col))); } } dataColumns.push_back(INTEGER(col)); found=true; break; } } if (!found) { mxThrow("Cannot find item '%s' in data", itemNames[ix]); } } if (weightColumnName) { for (int dc=0; dc < int(dataColNames.size()); ++dc) { if (strEQ(weightColumnName, dataColNames[dc])) { SEXP col = VECTOR_ELT(Rdata, dc); if (TYPEOF(col) != REALSXP) { mxThrow("Column '%s' is of type %s; expecting type numeric (double)", dataColNames[dc], Rf_type2char(TYPEOF(col))); } rowWeight = REAL(col); break; } } if (!rowWeight) { mxThrow("Cannot find weight column '%s'", weightColumnName); } } if (freqColumnName) { for (int dc=0; dc < int(dataColNames.size()); ++dc) { if (strEQ(freqColumnName, dataColNames[dc])) { SEXP col = VECTOR_ELT(Rdata, dc); if (TYPEOF(col) != INTSXP) { mxThrow("Column '%s' is of type %s; expecting type integer", dataColNames[dc], Rf_type2char(TYPEOF(col))); } rowFreq = INTEGER(col); break; } } if (!rowFreq) { mxThrow("Cannot find frequency column '%s'", freqColumnName); } } rowMap.reserve(dataRows); for (int rx=0; rx < dataRows; ++rx) rowMap.push_back(rx); } Eigen::Map< Eigen::ArrayXXd > Eparam(param, paramRows, numItems()); Eigen::Map< Eigen::VectorXd > meanVec(mean, itemDims); Eigen::Map< Eigen::MatrixXd > covMat(cov, itemDims, itemDims); quad.setStructure(qwidth, qpoints, Eparam, meanVec, covMat); if (paramRows < impliedParamRows) { mxThrow("At least %d rows are required in the item parameter matrix, only %d found", impliedParamRows, paramRows); } quad.refresh(meanVec, covMat); }
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); } }