00001
00009 #include "party.h"
00010
00011
00021 void C_TeststatPvalue(const SEXP linexpcov, const SEXP varctrl,
00022 double *ans_teststat, double *ans_pvalue) {
00023
00024 double releps, abseps, tol;
00025 int maxpts;
00026
00027 maxpts = get_maxpts(varctrl);
00028 tol = get_tol(varctrl);
00029 abseps = get_abseps(varctrl);
00030 releps = get_releps(varctrl);
00031
00032
00033 ans_teststat[0] = C_TestStatistic(linexpcov, get_teststat(varctrl),
00034 get_tol(varctrl));
00035
00036
00037 if (get_pvalue(varctrl))
00038 ans_pvalue[0] = C_ConditionalPvalue(ans_teststat[0], linexpcov,
00039 get_teststat(varctrl),
00040 tol, &maxpts, &releps, &abseps);
00041 else
00042 ans_pvalue[0] = 1.0;
00043 }
00044
00053 void C_TeststatCriterion(const SEXP linexpcov, const SEXP varctrl,
00054 double *ans_teststat, double *ans_criterion) {
00055
00056 C_TeststatPvalue(linexpcov, varctrl, ans_teststat, ans_criterion);
00057
00058
00059
00060 if (get_pvalue(varctrl))
00061 ans_criterion[0] = 1 - ans_criterion[0];
00062 else
00063 ans_criterion[0] = ans_teststat[0];
00064
00065 }
00066
00067
00078 void C_IndependenceTest(const SEXP x, const SEXP y, const SEXP weights,
00079 SEXP linexpcov, SEXP varctrl,
00080 SEXP ans) {
00081
00082
00083
00084
00085 C_LinStatExpCov(REAL(x), ncol(x), REAL(y), ncol(y),
00086 REAL(weights), nrow(x), 1,
00087 GET_SLOT(linexpcov, PL2_expcovinfSym), linexpcov);
00088
00089
00090 if (get_teststat(varctrl) == 2)
00091 C_LinStatExpCovMPinv(linexpcov, get_tol(varctrl));
00092 C_TeststatPvalue(linexpcov, varctrl, &REAL(ans)[0], &REAL(ans)[1]);
00093 }
00094
00095
00105 SEXP R_IndependenceTest(SEXP x, SEXP y, SEXP weights, SEXP linexpcov, SEXP varctrl) {
00106
00107 SEXP ans;
00108
00109 PROTECT(ans = allocVector(REALSXP, 2));
00110 C_IndependenceTest(x, y, weights, linexpcov, varctrl, ans);
00111 UNPROTECT(1);
00112 return(ans);
00113 }
00114
00115
00129 void C_GlobalTest(const SEXP learnsample, const SEXP weights,
00130 SEXP fitmem, const SEXP varctrl,
00131 const SEXP gtctrl, const double minsplit,
00132 double *ans_teststat, double *ans_criterion) {
00133
00134 int ninputs, nobs, j, i, k, RECALC = 1, type;
00135 SEXP responses, inputs, y, x, xmem, expcovinf;
00136 SEXP thiswhichNA;
00137 double *thisweights, *dweights, *pvaltmp, stweights = 0.0;
00138 int *ithiswhichNA, RANDOM, mtry, *randomvar, *index;
00139 int *dontuse, *dontusetmp;
00140
00141 ninputs = get_ninputs(learnsample);
00142 nobs = get_nobs(learnsample);
00143 responses = GET_SLOT(learnsample, PL2_responsesSym);
00144 inputs = GET_SLOT(learnsample, PL2_inputsSym);
00145 dweights = REAL(weights);
00146
00147
00148 y = get_test_trafo(responses);
00149
00150 expcovinf = GET_SLOT(fitmem, PL2_expcovinfSym);
00151 C_ExpectCovarInfluence(REAL(y), ncol(y), REAL(weights),
00152 nobs, expcovinf);
00153
00154 if (REAL(GET_SLOT(expcovinf, PL2_sumweightsSym))[0] < minsplit) {
00155 for (j = 0; j < ninputs; j++) {
00156 ans_teststat[j] = 0.0;
00157 ans_criterion[j] = 0.0;
00158 }
00159 } else {
00160
00161 dontuse = INTEGER(get_dontuse(fitmem));
00162 dontusetmp = INTEGER(get_dontusetmp(fitmem));
00163
00164 for (j = 0; j < ninputs; j++) dontusetmp[j] = !dontuse[j];
00165
00166
00167 RANDOM = get_randomsplits(gtctrl);
00168 mtry = get_mtry(gtctrl);
00169 if (RANDOM & (mtry > ninputs)) {
00170 warning("mtry is larger than ninputs, using mtry = inputs");
00171 mtry = ninputs;
00172 RANDOM = 0;
00173 }
00174 if (RANDOM) {
00175 index = Calloc(ninputs, int);
00176 randomvar = Calloc(mtry, int);
00177 C_SampleNoReplace(index, ninputs, mtry, randomvar);
00178 j = 0;
00179 for (k = 0; k < mtry; k++) {
00180 j = randomvar[k];
00181 while(dontuse[j] && j < ninputs) j++;
00182 if (j == ninputs)
00183 error("not enough variables to sample from");
00184 dontusetmp[j] = 0;
00185 }
00186 Free(index);
00187 Free(randomvar);
00188 }
00189
00190 for (j = 1; j <= ninputs; j++) {
00191
00192 if ((RANDOM && dontusetmp[j - 1]) || dontuse[j - 1]) {
00193 ans_teststat[j - 1] = 0.0;
00194 ans_criterion[j - 1] = 0.0;
00195 continue;
00196 }
00197
00198 x = get_transformation(inputs, j);
00199
00200 xmem = get_varmemory(fitmem, j);
00201 if (!has_missings(inputs, j)) {
00202 C_LinStatExpCov(REAL(x), ncol(x), REAL(y), ncol(y),
00203 REAL(weights), nrow(x), !RECALC, expcovinf,
00204 xmem);
00205 } else {
00206 thisweights = C_tempweights(j, weights, fitmem, inputs);
00207
00208
00209
00210
00211
00212
00213 stweights = 0.0;
00214 for (i = 0; i < nobs; i++) stweights += thisweights[i];
00215 if (stweights < minsplit) {
00216 ans_teststat[j - 1] = 0.0;
00217 ans_criterion[j - 1] = 0.0;
00218 continue;
00219 }
00220
00221 C_LinStatExpCov(REAL(x), ncol(x), REAL(y), ncol(y),
00222 thisweights, nrow(x), RECALC,
00223 GET_SLOT(xmem, PL2_expcovinfSym),
00224 xmem);
00225 }
00226
00227 if (get_teststat(varctrl) == 2)
00228 C_LinStatExpCovMPinv(xmem, get_tol(varctrl));
00229 C_TeststatCriterion(xmem, varctrl, &ans_teststat[j - 1],
00230 &ans_criterion[j - 1]);
00231 }
00232
00233 type = get_testtype(gtctrl);
00234 switch(type) {
00235
00236 case BONFERRONI:
00237 for (j = 0; j < ninputs; j++)
00238 ans_criterion[j] = R_pow_di(ans_criterion[j], ninputs);
00239 break;
00240
00241 case MONTECARLO:
00242 pvaltmp = Calloc(ninputs, double);
00243 C_MonteCarlo(ans_criterion, learnsample, weights, fitmem,
00244 varctrl, gtctrl, pvaltmp);
00245 for (j = 0; j < ninputs; j++)
00246 ans_criterion[j] = 1 - pvaltmp[j];
00247 Free(pvaltmp);
00248 break;
00249
00250 case AGGREGATED:
00251 error("C_GlobalTest: aggregated global test not yet implemented");
00252 break;
00253
00254 case UNIVARIATE: break;
00255 case TESTSTATISTIC: break;
00256 default: error("C_GlobalTest: undefined value for type argument");
00257 break;
00258 }
00259 }
00260 }
00261
00262
00272 SEXP R_GlobalTest(SEXP learnsample, SEXP weights, SEXP fitmem,
00273 SEXP varctrl, SEXP gtctrl) {
00274
00275 SEXP ans, teststat, criterion;
00276
00277 GetRNGstate();
00278
00279 PROTECT(ans = allocVector(VECSXP, 2));
00280 SET_VECTOR_ELT(ans, 0,
00281 teststat = allocVector(REALSXP, get_ninputs(learnsample)));
00282 SET_VECTOR_ELT(ans, 1,
00283 criterion = allocVector(REALSXP, get_ninputs(learnsample)));
00284
00285 C_GlobalTest(learnsample, weights, fitmem, varctrl, gtctrl, 0,
00286 REAL(teststat), REAL(criterion));
00287
00288 PutRNGstate();
00289
00290 UNPROTECT(1);
00291 return(ans);
00292 }