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);
}
}
