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);
}
}
