void R_set_response(SEXP learnsample, SEXP y) {
double *v, *t, *j, *dy, *p;
int i, n;
n = LENGTH(y);
dy = REAL(y);
if (LENGTH(R_get_response(learnsample)) != n)
error("lengths of arguments don't match");
v = REAL(VECTOR_ELT(GET_SLOT(GET_SLOT(learnsample, PL2_responsesSym),
PL2_variablesSym), 0));
t = REAL(VECTOR_ELT(GET_SLOT(GET_SLOT(learnsample, PL2_responsesSym),
PL2_transformationsSym), 0));
j = REAL(get_test_trafo(GET_SLOT(learnsample, PL2_responsesSym)));
p = REAL(get_predict_trafo(GET_SLOT(learnsample, PL2_responsesSym)));
for (i = 0; i < n; i++) {
v[i] = dy[i];
t[i] = dy[i];
j[i] = dy[i];
p[i] = dy[i];
}
}
SEXP R_TreeGrow(SEXP learnsample, SEXP weights, SEXP controls) {
SEXP ans, tree, where, nweights, fitmem;
double *dnweights, *dweights;
int nobs, i, nodenum = 1, *iwhere;
GetRNGstate();
PROTECT(fitmem = ctree_memory(learnsample, PROTECT(ScalarLogical(1))));
nobs = get_nobs(learnsample);
PROTECT(ans = allocVector(VECSXP, 2));
SET_VECTOR_ELT(ans, 0, where = allocVector(INTSXP, nobs));
iwhere = INTEGER(where);
for (int i = 0; i < nobs; i++) iwhere[i] = 0;
SET_VECTOR_ELT(ans, 1, tree = allocVector(VECSXP, NODE_LENGTH));
C_init_node(tree, nobs, get_ninputs(learnsample), get_maxsurrogate(get_splitctrl(controls)),
ncol(get_predict_trafo(GET_SLOT(learnsample, PL2_responsesSym))));
nweights = S3get_nodeweights(tree);
dnweights = REAL(nweights);
dweights = REAL(weights);
for (i = 0; i < nobs; i++) dnweights[i] = dweights[i];
C_TreeGrow(tree, learnsample, fitmem, controls, iwhere, &nodenum, 1);
if (LOGICAL(GET_SLOT(get_tgctrl(controls), PL2_remove_weightsSym))[0])
C_remove_weights(tree, 0);
PutRNGstate();
UNPROTECT(3);
return(ans);
}
void C_Node(SEXP node, SEXP learnsample, SEXP weights,
SEXP fitmem, SEXP controls, int TERMINAL, int depth) {
int nobs, ninputs, jselect, q, j, k, i;
double mincriterion, sweights, *dprediction;
double *teststat, *pvalue, smax, cutpoint = 0.0, maxstat = 0.0;
double *standstat, *splitstat;
SEXP responses, inputs, x, expcovinf, linexpcov;
SEXP varctrl, splitctrl, gtctrl, tgctrl, split, testy, predy;
double *dxtransf, *thisweights;
int *itable;
nobs = get_nobs(learnsample);
ninputs = get_ninputs(learnsample);
varctrl = get_varctrl(controls);
splitctrl = get_splitctrl(controls);
gtctrl = get_gtctrl(controls);
tgctrl = get_tgctrl(controls);
mincriterion = get_mincriterion(gtctrl);
responses = GET_SLOT(learnsample, PL2_responsesSym);
inputs = GET_SLOT(learnsample, PL2_inputsSym);
testy = get_test_trafo(responses);
predy = get_predict_trafo(responses);
q = ncol(testy);
/* <FIXME> we compute C_GlobalTest even for TERMINAL nodes! </FIXME> */
/* compute the test statistics and the node criteria for each input */
C_GlobalTest(learnsample, weights, fitmem, varctrl,
gtctrl, get_minsplit(splitctrl),
REAL(S3get_teststat(node)), REAL(S3get_criterion(node)), depth);
/* sum of weights: C_GlobalTest did nothing if sweights < mincriterion */
sweights = REAL(GET_SLOT(GET_SLOT(fitmem, PL2_expcovinfSym),
PL2_sumweightsSym))[0];
REAL(VECTOR_ELT(node, S3_SUMWEIGHTS))[0] = sweights;
/* compute the prediction of this node */
dprediction = REAL(S3get_prediction(node));
/* <FIXME> feed raw numeric values OR dummy encoded factors as y
Problem: what happens for survival times ? */
C_prediction(REAL(predy), nobs, ncol(predy), REAL(weights),
sweights, dprediction);
/* </FIXME> */
teststat = REAL(S3get_teststat(node));
pvalue = REAL(S3get_criterion(node));
/* try the two out of ninputs best inputs variables */
/* <FIXME> be more flexible and add a parameter controlling
the number of inputs tried </FIXME> */
for (j = 0; j < 2; j++) {
smax = C_max(pvalue, ninputs);
REAL(S3get_maxcriterion(node))[0] = smax;
/* if the global null hypothesis was rejected */
if (smax > mincriterion && !TERMINAL) {
/* the input variable with largest association to the response */
jselect = C_whichmax(pvalue, teststat, ninputs) + 1;
/* get the raw numeric values or the codings of a factor */
x = get_variable(inputs, jselect);
if (has_missings(inputs, jselect)) {
expcovinf = GET_SLOT(get_varmemory(fitmem, jselect),
PL2_expcovinfSym);
thisweights = C_tempweights(jselect, weights, fitmem, inputs);
} else {
expcovinf = GET_SLOT(fitmem, PL2_expcovinfSym);
thisweights = REAL(weights);
}
/* <FIXME> handle ordered factors separatly??? </FIXME> */
if (!is_nominal(inputs, jselect)) {
/* search for a split in a ordered variable x */
split = S3get_primarysplit(node);
/* check if the n-vector of splitstatistics
should be returned for each primary split */
if (get_savesplitstats(tgctrl)) {
C_init_orderedsplit(split, nobs);
splitstat = REAL(S3get_splitstatistics(split));
} else {
C_init_orderedsplit(split, 0);
splitstat = REAL(get_splitstatistics(fitmem));
}
C_split(REAL(x), 1, REAL(testy), q, thisweights, nobs,
INTEGER(get_ordering(inputs, jselect)), splitctrl,
GET_SLOT(fitmem, PL2_linexpcov2sampleSym),
expcovinf, REAL(S3get_splitpoint(split)), &maxstat,
splitstat);
S3set_variableID(split, jselect);
} else {
/* search of a set of levels (split) in a numeric variable x */
split = S3get_primarysplit(node);
/* check if the n-vector of splitstatistics
should be returned for each primary split */
if (get_savesplitstats(tgctrl)) {
C_init_nominalsplit(split,
LENGTH(get_levels(inputs, jselect)),
nobs);
splitstat = REAL(S3get_splitstatistics(split));
} else {
C_init_nominalsplit(split,
LENGTH(get_levels(inputs, jselect)),
0);
splitstat = REAL(get_splitstatistics(fitmem));
}
linexpcov = get_varmemory(fitmem, jselect);
standstat = Calloc(get_dimension(linexpcov), double);
C_standardize(REAL(GET_SLOT(linexpcov,
PL2_linearstatisticSym)),
REAL(GET_SLOT(linexpcov, PL2_expectationSym)),
REAL(GET_SLOT(linexpcov, PL2_covarianceSym)),
get_dimension(linexpcov), get_tol(splitctrl),
standstat);
C_splitcategorical(INTEGER(x),
LENGTH(get_levels(inputs, jselect)),
REAL(testy), q, thisweights,
nobs, standstat, splitctrl,
GET_SLOT(fitmem, PL2_linexpcov2sampleSym),
expcovinf, &cutpoint,
INTEGER(S3get_splitpoint(split)),
&maxstat, splitstat);
/* compute which levels of a factor are available in this node
(for printing) later on. A real `table' for this node would
induce too much overhead here. Maybe later. */
itable = INTEGER(S3get_table(split));
dxtransf = REAL(get_transformation(inputs, jselect));
for (k = 0; k < LENGTH(get_levels(inputs, jselect)); k++) {
itable[k] = 0;
for (i = 0; i < nobs; i++) {
if (dxtransf[k * nobs + i] * thisweights[i] > 0) {
itable[k] = 1;
continue;
}
}
}
Free(standstat);
}
if (maxstat == 0) {
if (j == 1) {
S3set_nodeterminal(node);
} else {
/* do not look at jselect in next iteration */
pvalue[jselect - 1] = R_NegInf;
}
} else {
S3set_variableID(split, jselect);
break;
}
} else {