Differential D22496 Diff 61870 src/backend/hypothesisTest/HypothesisTest.cpp

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src/backend/hypothesisTest/HypothesisTest.cpp

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47#include <gsl/gsl_cdf.h> 47​#include <gsl/gsl_cdf.h>
48#include <gsl/gsl_math.h> 48​#include <gsl/gsl_math.h>
49 49​
50#include <math.h> ​
51 ​
52extern "C" { 50​extern "C" {
53#include "backend/nsl/nsl_stats.h" 51​#include "backend/nsl/nsl_stats.h"
54} 52​}
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106 104​
107void HypothesisTest::performTest(Test test, bool categoricalVariable, bool equalVariance) { 105​void HypothesisTest::performTest(Test test, bool categoricalVariable, bool equalVariance) {
108 d->tailType = test.tail; 106​ d->tailType = test.tail;
107​ d->pValue.clear();
108​ d->statisticValue.clear();
109​ d->statsTable = "";
110​ for (int i = 0; i < 10; i++)
111​ d->resultLine[i]->clear();
112​
109 switch (test.subtype) { 113​ switch (test.subtype) {
110 case HypothesisTest::Test::SubType::TwoSampleIndependent: { 114​ case HypothesisTest::Test::SubType::TwoSampleIndependent: {
111 d->currTestName = "<h2>" + i18n("Two Sample Independent Test") + "</h2>"; 115​ d->currTestName = "<h2>" + i18n("Two Sample Independent Test") + "</h2>";
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141void HypothesisTest::performLeveneTest(bool categoricalVariable) { 145​void HypothesisTest::performLeveneTest(bool categoricalVariable) {
142 d->currTestName = "<h2>" + i18n("Levene Test for Equality of Variance") + "</h2>"; 146​ d->currTestName = "<h2>" + i18n("Levene Test for Equality of Variance") + "</h2>";
143 d->performLeveneTest(categoricalVariable); 147​ d->performLeveneTest(categoricalVariable);
144 ​
145 emit changed(); 148​ emit changed();
146} 149​}
147 150​
148double HypothesisTest::statisticValue() { 151​QList<double> HypothesisTest::statisticValue() {
149 return d->statisticValue; 152​ return d->statisticValue;
150} 153​}
151 154​
152double HypothesisTest::pValue() { 155​QList<double> HypothesisTest::pValue() {
153 return d->pValue; 156​ return d->pValue;
154} 157​}
155 158​
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203/**************************Two Sample Independent *************************************/ 206​/**************************Two Sample Independent *************************************/
204 207​
205void HypothesisTestPrivate::performTwoSampleIndependentTest(HypothesisTest::Test::Type test, bool categoricalVariable, bool equalVariance) { 208​void HypothesisTestPrivate::performTwoSampleIndependentTest(HypothesisTest::Test::Type test, bool categoricalVariable, bool equalVariance) {
206 clearTestView(); ​
207 ​
208 if (columns.size() != 2) { 209​ if (columns.size() != 2) {
209 printError("Inappropriate number of columns selected"); 210​ printError("Inappropriate number of columns selected");
210 return; 211​ return;
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300 301​
301 sp = qSqrt(((n[0]-1) * gsl_pow_2(std[0]) + 302​ sp = qSqrt(((n[0]-1) * gsl_pow_2(std[0]) +
302 (n[1]-1) * gsl_pow_2(std[1]) ) / df ); 303​ (n[1]-1) * gsl_pow_2(std[1]) ) / df );
303 statisticValue = (mean[0] - mean[1]) / (sp * qSqrt(1.0/n[0] + 1.0/n[1])); 304​ statisticValue.append((mean[0] - mean[1]) / (sp * qSqrt(1.0/n[0] + 1.0/n[1])));
sgerlachUnsubmitted
Done

I would suggest to use sqrt() from cmath instead of qSqrt(). What do you think?

sgerlach: I would suggest to use sqrt() from cmath instead of qSqrt(). What do you think?
devanshuagarwalAuthorUnsubmitted
Done

Yes you are right.

devanshuagarwal: Yes you are right.
304 printLine(9, "<b>Assumption:</b> Equal Variance b/w both population means"); 305​ printLine(9, "<b>Assumption:</b> Equal Variance b/w both population means");
305 } else { 306​ } else {
306 double temp_val; 307​ double temp_val;
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309 (gsl_pow_2( (gsl_pow_2(std[1]) / n[1]) ) / (n[1]-1))); 310​ (gsl_pow_2( (gsl_pow_2(std[1]) / n[1]) ) / (n[1]-1)));
310 df = qRound(temp_val); 311​ df = qRound(temp_val);
311 312​
312 statisticValue = (mean[0] - mean[1]) / (qSqrt( (gsl_pow_2(std[0])/n[0]) + 313​ statisticValue.append((mean[0] - mean[1]) / (qSqrt( (gsl_pow_2(std[0])/n[0]) +
313 (gsl_pow_2(std[1])/n[1]))); 314​ (gsl_pow_2(std[1])/n[1]))));
314 printLine(9, "<b>Assumption:</b> UnEqual Variance b/w both population means"); 315​ printLine(9, "<b>Assumption:</b> UnEqual Variance b/w both population means");
315 } 316​ }
316 317​
Context not available.
320 case HypothesisTest::Test::Type::ZTest: { 321​ case HypothesisTest::Test::Type::ZTest: {
321 testName = "Z"; 322​ testName = "Z";
322 sp = qSqrt( ((n[0]-1) * gsl_pow_2(std[0]) + (n[1]-1) * gsl_pow_2(std[1])) / df); 323​ sp = qSqrt( ((n[0]-1) * gsl_pow_2(std[0]) + (n[1]-1) * gsl_pow_2(std[1])) / df);
323 statisticValue = (mean[0] - mean[1]) / (sp * qSqrt( 1.0 / n[0] + 1.0 / n[1])); 324​ statisticValue.append((mean[0] - mean[1]) / (sp * qSqrt( 1.0 / n[0] + 1.0 / n[1])));
324 pValue = gsl_cdf_gaussian_P(statisticValue, sp); 325​// pValue.append(gsl_cdf_gaussian_P(statisticValue, sp));
325 break; 326​ break;
326 } 327​ }
327 case HypothesisTest::Test::Type::Anova: 328​ case HypothesisTest::Test::Type::Anova:
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330 } 331​ }
331 332​
332 currTestName = "<h2>" + i18n("Two Sample Independent %1 Test for %2 vs %3", testName, col1Name, col2Name) + "</h2>"; 333​ currTestName = "<h2>" + i18n("Two Sample Independent %1 Test for %2 vs %3", testName, col1Name, col2Name) + "</h2>";
333 pValue = getPValue(test, statisticValue, col1Name, col2Name, (mean[0] - mean[1]), sp, df); 334​ pValue.append(getPValue(test, statisticValue[0], col1Name, col2Name, (mean[0] - mean[1]), sp, df));
334 335​
335 printLine(2, i18n("Significance level is %1", round(significanceLevel)), "blue"); 336​ printLine(2, i18n("Significance level is %1", round(significanceLevel)), "blue");
336 337​
337 printLine(4, i18n("%1 Value is %2 ", testName, round(statisticValue)), "green"); 338​ printLine(4, i18n("%1 Value is %2 ", testName, round(statisticValue[0])), "green");
338 printTooltip(4, i18n("More is the |%1-value|, more safely we can reject the null hypothesis", testName)); 339​ printTooltip(4, i18n("More is the |%1-value|, more safely we can reject the null hypothesis", testName));
339 340​
340 printLine(5, i18n("P Value is %1 ", pValue), "green"); 341​ printLine(5, i18n("P Value is %1 ", pValue[0]), "green");
341 342​
342 printLine(6, i18n("Degree of Freedom is %1", df), "green"); 343​ printLine(6, i18n("Degree of Freedom is %1", df), "green");
343 printTooltip(6, i18n("Number of independent Pieces of information that went into calculating the estimate")); 344​ printTooltip(6, i18n("Number of independent Pieces of information that went into calculating the estimate"));
344 345​
345 if (pValue <= significanceLevel) 346​ if (pValue[0] <= significanceLevel)
346 printTooltip(5, i18n("We can safely reject Null Hypothesis for significance level %1", round(significanceLevel))); 347​ printTooltip(5, i18n("We can safely reject Null Hypothesis for significance level %1", round(significanceLevel)));
347 else 348​ else
348 printTooltip(5, i18n("There is a plausibility for Null Hypothesis to be true")); 349​ printTooltip(5, i18n("There is a plausibility for Null Hypothesis to be true"));
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352/********************************Two Sample Paired ***************************************/ 353​/********************************Two Sample Paired ***************************************/
353 354​
354void HypothesisTestPrivate::performTwoSamplePairedTest(HypothesisTest::Test::Type test) { 355​void HypothesisTestPrivate::performTwoSamplePairedTest(HypothesisTest::Test::Type test) {
355 clearTestView(); ​
356 ​
357 if (columns.size() != 2) { 356​ if (columns.size() != 2) {
358 printError("Inappropriate number of columns selected"); 357​ printError("Inappropriate number of columns selected");
359 358​
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403 402​
404 switch (test) { 403​ switch (test) {
405 case HypothesisTest::Test::Type::TTest: { 404​ case HypothesisTest::Test::Type::TTest: {
406 statisticValue = mean / (std / qSqrt(n)); 405​ statisticValue[0] = mean / (std / qSqrt(n));
407 df = n - 1; 406​ df = n - 1;
408 testName = "T"; 407​ testName = "T";
409 printLine(6, i18n("Degree of Freedom is %1</p", df), "green"); 408​ printLine(6, i18n("Degree of Freedom is %1</p", df), "green");
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411 } 410​ }
412 case HypothesisTest::Test::Type::ZTest: { 411​ case HypothesisTest::Test::Type::ZTest: {
413 testName = "Z"; 412​ testName = "Z";
414 statisticValue = mean / (std / qSqrt(n)); 413​ statisticValue[0] = mean / (std / qSqrt(n));
415 df = n - 1; 414​ df = n - 1;
416 break; 415​ break;
417 } 416​ }
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422 421​
423 } 422​ }
424 423​
425 pValue = getPValue(test, statisticValue, columns[0]->name(), i18n("%1", populationMean), mean, std, df); 424​ pValue.append(getPValue(test, statisticValue[0], columns[0]->name(), i18n("%1", populationMean), mean, std, df));
426 currTestName = "<h2>" + i18n("One Sample %1 Test for %2 vs %3", testName, columns[0]->name(), columns[1]->name()) + "</h2>"; 425​ currTestName = "<h2>" + i18n("One Sample %1 Test for %2 vs %3", testName, columns[0]->name(), columns[1]->name()) + "</h2>";
427 426​
428 printLine(2, i18n("Significance level is %1 ", round(significanceLevel)), "blue"); 427​ printLine(2, i18n("Significance level is %1 ", round(significanceLevel)), "blue");
429 printLine(4, i18n("%1 Value is %2 ", testName, round(statisticValue)), "green"); 428​ printLine(4, i18n("%1 Value is %2 ", testName, round(statisticValue[0])), "green");
430 printLine(5, i18n("P Value is %1 ", pValue), "green"); 429​ printLine(5, i18n("P Value is %1 ", pValue[0]), "green");
431 430​
432 if (pValue <= significanceLevel) 431​ if (pValue[0] <= significanceLevel)
433 printTooltip(5, i18n("We can safely reject Null Hypothesis for significance level %1", significanceLevel)); 432​ printTooltip(5, i18n("We can safely reject Null Hypothesis for significance level %1", significanceLevel));
434 else 433​ else
435 printTooltip(5, i18n("There is a plausibility for Null Hypothesis to be true")); 434​ printTooltip(5, i18n("There is a plausibility for Null Hypothesis to be true"));
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440/******************************** One Sample ***************************************/ 439​/******************************** One Sample ***************************************/
441 440​
442void HypothesisTestPrivate::performOneSampleTest(HypothesisTest::Test::Type test) { 441​void HypothesisTestPrivate::performOneSampleTest(HypothesisTest::Test::Type test) {
443 clearTestView(); ​
444 ​
445 if (columns.size() != 1) { 442​ if (columns.size() != 1) {
446 printError("Inappropriate number of columns selected"); 443​ printError("Inappropriate number of columns selected");
447 444​
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488 switch (test) { 485​ switch (test) {
489 case HypothesisTest::Test::Type::TTest: { 486​ case HypothesisTest::Test::Type::TTest: {
490 testName = "T"; 487​ testName = "T";
491 statisticValue = (mean - populationMean) / (std / qSqrt(n)); 488​ statisticValue.append((mean - populationMean) / (std / qSqrt(n)));
492 df = n - 1; 489​ df = n - 1;
493 printLine(6, i18n("Degree of Freedom is %1", df), "blue"); 490​ printLine(6, i18n("Degree of Freedom is %1", df), "blue");
494 break; 491​ break;
Context not available.
496 case HypothesisTest::Test::Type::ZTest: { 493​ case HypothesisTest::Test::Type::ZTest: {
497 testName = "Z"; 494​ testName = "Z";
498 df = 0; 495​ df = 0;
499 statisticValue = (mean - populationMean) / (std / qSqrt(n)); 496​ statisticValue.append((mean - populationMean) / (std / qSqrt(n)));
500 break; 497​ break;
501 } 498​ }
502 case HypothesisTest::Test::Type::Anova: 499​ case HypothesisTest::Test::Type::Anova:
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504 break; 501​ break;
505 } 502​ }
506 503​
507 pValue = getPValue(test, statisticValue, columns[0]->name(), i18n("%1",populationMean), mean - populationMean, std, df); 504​ pValue.append(getPValue(test, statisticValue[0], columns[0]->name(), i18n("%1",populationMean), mean - populationMean, std, df));
508 currTestName = "<h2>" + i18n("One Sample %1 Test for %2", testName, columns[0]->name()) + "</h2>"; 505​ currTestName = "<h2>" + i18n("One Sample %1 Test for %2", testName, columns[0]->name()) + "</h2>";
509 506​
510 printLine(2, i18n("Significance level is %1", round(significanceLevel)), "blue"); 507​ printLine(2, i18n("Significance level is %1", round(significanceLevel)), "blue");
511 printLine(4, i18n("%1 Value is %2", testName, round(statisticValue)), "green"); 508​ printLine(4, i18n("%1 Value is %2", testName, round(statisticValue[0])), "green");
512 printLine(5, i18n("P Value is %1", pValue), "green"); 509​ printLine(5, i18n("P Value is %1", pValue[0]), "green");
513 510​
514 if (pValue <= significanceLevel) 511​ if (pValue[0] <= significanceLevel)
515 printTooltip(5, i18n("We can safely reject Null Hypothesis for significance level %1", significanceLevel)); 512​ printTooltip(5, i18n("We can safely reject Null Hypothesis for significance level %1", significanceLevel));
516 else 513​ else
517 printTooltip(5, i18n("There is a plausibility for Null Hypothesis to be true")); 514​ printTooltip(5, i18n("There is a plausibility for Null Hypothesis to be true"));
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526// w stands for within groups 523​// w stands for within groups
527// np is number of partition i.e., number of classes 524​// np is number of partition i.e., number of classes
528void HypothesisTestPrivate::performOneWayAnova() { 525​void HypothesisTestPrivate::performOneWayAnova() {
529 clearTestView(); 526​ int np, totalRows;
530 int np, totalRows; ​
531 countPartitions(columns[0], np, totalRows); 527​ countPartitions(columns[0], np, totalRows);
532 528​
533 int* ni = new int[np]; 529​ int* ni = new int[np];
Context not available.
551 double sW = 0; // sum of squares of (value - mean of group) within the groups 547​ double sW = 0; // sum of squares of (value - mean of group) within the groups
552 int fW = 0; // degree of freedom within the group 548​ int fW = 0; // degree of freedom within the group
553 double msW = 0; // mean sum of squares within the groups 549​ double msW = 0; // mean sum of squares within the groups
554 double fValue = 0; ​
555 ​
556 550​
557 // now finding mean of each group; 551​ // now finding mean of each group;
558 552​
Context not available.
573 msB = sB / fB; 567​ msB = sB / fB;
574 568​
575 msW = sW / fW; 569​ msW = sW / fW;
576 fValue = msB / msW; 570​ statisticValue.append(msB / msW);
577 571​
578 572​
579 pValue = nsl_stats_fdist_p(fValue, static_cast<size_t>(np-1), fW); 573​ pValue.append(nsl_stats_fdist_p(statisticValue[0], static_cast<size_t>(np-1), fW));
580 574​
581 QMapIterator<QString, int> i(classnameToIndex); 575​ QMapIterator<QString, int> i(classnameToIndex);
582 while (i.hasNext()) { 576​ while (i.hasNext()) {
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643 delete[] std; 637​ delete[] std;
644 delete[] colNames; 638​ delete[] colNames;
645 639​
646 printLine(1, i18n("F Value is %1", round(fValue)), "green"); 640​ printLine(1, i18n("F Value is %1", round(statisticValue[0])), "green");
647 printLine(2, i18n("P Value is %1 ", pValue), "green"); 641​ printLine(2, i18n("P Value is %1 ", pValue[0]), "green");
648 642​
649 if (pValue <= significanceLevel) 643​ if (pValue[0] <= significanceLevel)
650 printTooltip(2, i18n("We can safely reject Null Hypothesis for significance level %1", significanceLevel)); 644​ printTooltip(2, i18n("We can safely reject Null Hypothesis for significance level %1", significanceLevel));
651 else 645​ else
652 printTooltip(2, i18n("There is a plausibility for Null Hypothesis to be true")); 646​ printTooltip(2, i18n("There is a plausibility for Null Hypothesis to be true"));
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659// all formulas and symbols are taken from: http://statweb.stanford.edu/~susan/courses/s141/exanova.pdf 653​// all formulas and symbols are taken from: http://statweb.stanford.edu/~susan/courses/s141/exanova.pdf
660 654​
661//TODO: suppress warning of variable length array are a C99 feature. 655​//TODO: suppress warning of variable length array are a C99 feature.
662//TODO: changed int mean to double mean; 656​//TODO: add assumptions verification option
657​//TODO: add tail option (if needed)
663void HypothesisTestPrivate::performTwoWayAnova() { 658​void HypothesisTestPrivate::performTwoWayAnova() {
664 clearTestView(); ​
665 int np_a, totalRows_a; 659​ int np_a, totalRows_a;
666 int np_b, totalRows_b; 660​ int np_b, totalRows_b;
667 countPartitions(columns[0], np_a, totalRows_a); 661​ countPartitions(columns[0], np_a, totalRows_a);
Context not available.
720 groupMean[i][j] /= replicates[i][j]; 714​ groupMean[i][j] /= replicates[i][j];
721 } 715​ }
722 716​
723 for (int i = 0; i < np_a; i++) 717​// for (int i = 0; i < np_a; i++)
724 for (int j = 0; j < np_b; j++) 718​// for (int j = 0; j < np_b; j++)
725 groupMean[i][j] = int(groupMean[i][j]); 719​// groupMean[i][j] = int(groupMean[i][j]);
726 720​
727 double ss_within = 0; 721​ double ss_within = 0;
728 for (int i = 0; i < totalRows_a; i++) { 722​ for (int i = 0; i < totalRows_a; i++) {
Context not available.
749 for (int i = 0; i < np_a; i++) 743​ for (int i = 0; i < np_a; i++)
750 mean += mean_a[i] / np_a; 744​ mean += mean_a[i] / np_a;
751 745​
752 QDEBUG("ss_within is " << ss_within); ​
753 QDEBUG("df_within is " << df_within); ​
754 QDEBUG("ms_within is " << ms_within); ​
755 746​
747​ double ss_a = 0;
sgerlachUnsubmitted
Done

you are doing a lot of basic statistical calculations. Can you check if any GSL function can be used (see https://www.gnu.org/software/gsl/doc/html/statistics.html)?

sgerlach: you are doing a lot of basic statistical calculations. Can you check if any GSL function can be…
devanshuagarwalAuthorUnsubmitted
Done

The reason that I am doing so many basic calculations is to increase performance. If I will use functions then I have to call separate functions for mean, std deviation and other statistical calculations. Though asymptotically it will also end up in O(n) but for practical purposes, it will turn out to be slower as here I am transversing through the whole data only twice and there it will be f times (where f >= number of statistical calculations)

devanshuagarwal: The reason that I am doing so many basic calculations is to increase performance. If I will use…
sgerlachUnsubmitted
Done

I'm not sure if i understand your argument. You don't have to calculate it f times when you save the values. Also the performance should not be a problem. I would really recommend to use GSL function to improve readability and maintainability. Of course only if it fits to the workflow. Just think about it.

sgerlach: I'm not sure if i understand your argument. You don't have to calculate it f times when you…
devanshuagarwalAuthorUnsubmitted
Done

Yes, you are right. it will improve readability and maintainability. Will use gsl functions.

devanshuagarwal: Yes, you are right. it will improve readability and maintainability. Will use gsl functions.
756 for (int i = 0; i < np_a; i++) 748​ for (int i = 0; i < np_a; i++)
757 QDEBUG("mean_a is " << mean_a[i]); 749​ ss_a += gsl_pow_2(mean_a[i] - mean);
750​ ss_a *= replicate * np_b;
751​
752​ int df_a = np_a - 1;
753​ double ms_a = ss_a / df_a;
754​
755​ double ss_b = 0;
758 for (int i = 0; i < np_b; i++) 756​ for (int i = 0; i < np_b; i++)
759 QDEBUG("mean_b is " << mean_b[i]); 757​ ss_b += gsl_pow_2(mean_b[i] - mean);
758​ ss_b *= replicate * np_a;
759​
760​ int df_b = np_b - 1;
761​ double ms_b = ss_b / df_b;
762​
763​ double ss_interaction = 0;
764​
765​ for (int i = 0; i < np_a; i++)
766​ for (int j = 0; j < np_b; j++)
767​ ss_interaction += gsl_pow_2(groupMean[i][j] - mean_a[i] - mean_b[j] + mean);
760 768​
769​ ss_interaction *= replicate;
770​ int df_interaction = (np_a - 1) * (np_b - 1);
771​ double ms_interaction = ss_interaction / df_interaction;
761 772​
762 QString partitionNames_a[np_a]; 773​ QString partitionNames_a[np_a];
763 QString partitionNames_b[np_b]; 774​ QString partitionNames_b[np_b];
Context not available.
791 for (int i = 0; i < np_a; i++) { 802​ for (int i = 0; i < np_a; i++) {
792 rowMajor.append(new Cell(partitionNames_a[i], level, true)); 803​ rowMajor.append(new Cell(partitionNames_a[i], level, true));
793 for (int j = 0; j < np_b; j++) { 804​ for (int j = 0; j < np_b; j++) {
794 rowMajor.append(new Cell(groupMean[i][j], level)); 805​ rowMajor.append(new Cell(round(groupMean[i][j]), level));
795 rowMajor.append(new Cell(replicates[i][j], level)); 806​ rowMajor.append(new Cell(replicates[i][j], level));
796 } 807​ }
797 rowMajor.append(new Cell(mean_a[i], level)); 808​ rowMajor.append(new Cell(round(mean_a[i]), level));
798 level++; 809​ level++;
799 } 810​ }
800 811​
801 rowMajor.append(new Cell("Mean", level, true)); 812​ rowMajor.append(new Cell("Mean", level, true));
802 for (int i = 0; i < np_b; i++) 813​ for (int i = 0; i < np_b; i++)
803 rowMajor.append(new Cell(mean_b[i], level, false, 1, 2)); 814​ rowMajor.append(new Cell(round(mean_b[i]), level, false, 1, 2));
804 rowMajor.append(new Cell(mean, level)); 815​ rowMajor.append(new Cell(round(mean), level));
805 816​
806 statsTable = "<h3>" + i18n("Contingency Table") + "</h3>"; 817​ statsTable = "<h3>" + i18n("Contingency Table") + "</h3>";
807 statsTable += getHtmlTable3(rowMajor); 818​ statsTable += getHtmlTable3(rowMajor);
808 819​
809// QDEBUG(""); 820​ statsTable += "</br>";
810// QDEBUG(""); 821​ statsTable += "<h3>" + i18n("results table") + "</h3>";
811// QDEBUG(statsTable); 822​
823​ rowMajor.clear();
824​ level = 0;
825​ rowMajor.append(new Cell("", level, true));
826​ rowMajor.append(new Cell("SS", level, true));
827​ rowMajor.append(new Cell("DF", level, true));
828​ rowMajor.append(new Cell("MS", level, true));
829​
830​ level++;
831​ rowMajor.append(new Cell(columns[0]->name(), level, true));
832​ rowMajor.append(new Cell(round(ss_a), level));
833​ rowMajor.append(new Cell(df_a, level));
834​ rowMajor.append(new Cell(round(ms_a), level));
835​
836​ level++;
837​ rowMajor.append(new Cell(columns[1]->name(), level, true));
838​ rowMajor.append(new Cell(round(ss_b), level));
839​ rowMajor.append(new Cell(df_b, level));
840​ rowMajor.append(new Cell(round(ms_b), level));
841​
842​ level++;
843​ rowMajor.append(new Cell("Interaction", level, true));
sgerlachUnsubmitted
Done

translation?

sgerlach: translation?
devanshuagarwalAuthorUnsubmitted
Done

the translation is automatic in getHtmlTable3 function

devanshuagarwal: the translation is automatic in getHtmlTable3 function
sgerlachUnsubmitted
Done

ok

sgerlach: ok
844​ rowMajor.append(new Cell(round(ss_interaction), level));
845​ rowMajor.append(new Cell(df_interaction, level));
846​ rowMajor.append(new Cell(round(ms_interaction), level));
847​
848​ level++;
849​ rowMajor.append(new Cell("Within", level, true));
sgerlachUnsubmitted
Done

translation?

sgerlach: translation?
devanshuagarwalAuthorUnsubmitted
Done

It is there in getHtmlTable3 method.

devanshuagarwal: It is there in getHtmlTable3 method.
sgerlachUnsubmitted
Done

ok

sgerlach: ok
850​ rowMajor.append(new Cell(round(ss_within), level));
851​ rowMajor.append(new Cell(df_within, level));
852​ rowMajor.append(new Cell(round(ms_within), level));
853​
854​ statsTable += getHtmlTable3(rowMajor);
855​
856​ double fValue_a = ms_a / ms_within;
857​ double fValue_b = ms_b / ms_within;
858​ double fValue_interaction = ms_interaction / ms_within;
859​
860​ double pValue_a = nsl_stats_fdist_p(fValue_a, static_cast<size_t>(np_a - 1), df_a);
861​ double pValue_b = nsl_stats_fdist_p(fValue_b, static_cast<size_t>(np_b - 1), df_b);
862​
863​ printLine(0, "F(df<sub>" + columns[0]->name() + "</sub>, df<sub>within</sub>) is " + round(fValue_a), "blue");
864​ printLine(1, "F(df<sub>" + columns[1]->name() + "</sub>, df<sub>within</sub>) is " + round(fValue_b), "blue");
865​ printLine(2, "F(df<sub>interaction</sub>, df<sub>within</sub>) is " + round(fValue_interaction), "blue");
866​
867​ printLine(4, "P(df<sub>" + columns[0]->name() + "</sub>, df<sub>within</sub>) is " + round(pValue_a), "blue");
868​ printLine(5, "P(df<sub>" + columns[1]->name() + "</sub>, df<sub>within</sub>) is " + round(pValue_b), "blue");
869​// printLine(2, "P(df<sub>interaction</sub>, df<sub>within</sub>) is " + round(fValue_interaction), "blue");
870​
871​ statisticValue.append(fValue_a);
872​ statisticValue.append(fValue_b);
873​ statisticValue.append(fValue_interaction);
874​
875​ pValue.append(pValue_a);
876​ pValue.append(pValue_b);
877​
812 return; 878​ return;
813} 879​}
814 880​
Context not available.
825// ziBarBar = mean for all zij 891​// ziBarBar = mean for all zij
826// ni = number of elements in group i 892​// ni = number of elements in group i
827void HypothesisTestPrivate::performLeveneTest(bool categoricalVariable) { 893​void HypothesisTestPrivate::performLeveneTest(bool categoricalVariable) {
828 clearTestView(); ​
829 ​
830 if (columns.size() != 2) { 894​ if (columns.size() != 2) {
831 printError("Inappropriate number of columns selected"); 895​ printError("Inappropriate number of columns selected");
832 return; 896​ return;
Context not available.
1045 delete[] ziBar; 1109​ delete[] ziBar;
1046 delete[] ni; 1110​ delete[] ni;
1047 1111​
1048 pValue = nsl_stats_fdist_p(fValue, static_cast<size_t>(np-1), df); 1112​ pValue.append(nsl_stats_fdist_p(fValue, static_cast<size_t>(np-1), df));
1049 1113​
1050 printLine(0, "Null Hypothesis: Variance is equal between all classes", "blue"); 1114​ printLine(0, "Null Hypothesis: Variance is equal between all classes", "blue");
1051 printLine(1, "Alternate Hypothesis: Variance is not equal in at-least one pair of classes", "blue"); 1115​ printLine(1, "Alternate Hypothesis: Variance is not equal in at-least one pair of classes", "blue");
1052 printLine(2, i18n("Significance level is %1", round(significanceLevel)), "blue"); 1116​ printLine(2, i18n("Significance level is %1", round(significanceLevel)), "blue");
1053 printLine(4, i18n("F Value is %1 ", round(fValue)), "green"); 1117​ printLine(4, i18n("F Value is %1 ", round(fValue)), "green");
1054 printLine(5, i18n("P Value is %1 ", pValue), "green"); 1118​ printLine(5, i18n("P Value is %1 ", pValue[0]), "green");
1055 printLine(6, i18n("Degree of Freedom is %1", df), "green"); 1119​ printLine(6, i18n("Degree of Freedom is %1", df), "green");
1056 1120​
1057 if (pValue <= significanceLevel) { 1121​ if (pValue[0] <= significanceLevel) {
1058 printTooltip(5, i18n("We can safely reject Null Hypothesis for significance level %1", significanceLevel)); 1122​ printTooltip(5, i18n("We can safely reject Null Hypothesis for significance level %1", significanceLevel));
1059 printLine(8, "Requirement for homogeneity is not met", "red"); 1123​ printLine(8, "Requirement for homogeneity is not met", "red");
1060 } else { 1124​ } else {
Context not available.
1062 printLine(8, "Requirement for homogeneity is met", "green"); 1126​ printLine(8, "Requirement for homogeneity is met", "green");
1063 } 1127​ }
1064 1128​
1129​ statisticValue.append(fValue);
1065 return; 1130​ return;
1066} 1131​}
1067 1132​
Context not available.
1247 1312​
1248//TODO change ("⋖") symbol to ("<"), currently macro UTF8_QSTRING is not working properly if used "<" symbol; 1313​//TODO change ("⋖") symbol to ("<"), currently macro UTF8_QSTRING is not working properly if used "<" symbol;
1249// TODO: check for correctness between: for TestZ with TailTwo 1314​// TODO: check for correctness between: for TestZ with TailTwo
1250// pValue = 2*gsl_cdf_tdist_P(value, df) v/s 1315​// pValue.append(2*gsl_cdf_tdist_P(value, df) v/s
1251// pValue = gsl_cdf_tdis_P(value, df) + gsl_cdf_tdis_P(-value, df); 1316​// pValue.append(gsl_cdf_tdis_P(value, df) + gsl_cdf_tdis_P(-value, df);
1252double HypothesisTestPrivate::getPValue(const HypothesisTest::Test::Type& test, double& value, const QString& col1Name, const QString& col2Name, const double mean, const double sp, const int df) { 1317​double HypothesisTestPrivate::getPValue(const HypothesisTest::Test::Type& test, double& value, const QString& col1Name, const QString& col2Name, const double mean, const double sp, const int df) {
1253 1318​
1254 switch (test) { 1319​ switch (test) {
1255 case HypothesisTest::Test::Type::TTest: { 1320​ case HypothesisTest::Test::Type::TTest: {
1256 switch (tailType) { 1321​ switch (tailType) {
1257 case HypothesisTest::Test::Tail::Negative: { 1322​ case HypothesisTest::Test::Tail::Negative: {
1258 pValue = gsl_cdf_tdist_P(value, df); 1323​ pValue.append(gsl_cdf_tdist_P(value, df));
1259 printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("≥"), col2Name), "blue"); 1324​ printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("≥"), col2Name), "blue");
1260 printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("⋖"), col2Name), "blue"); 1325​ printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("⋖"), col2Name), "blue");
1261 break; 1326​ break;
1262 } 1327​ }
1263 case HypothesisTest::Test::Tail::Positive: { 1328​ case HypothesisTest::Test::Tail::Positive: {
1264 value *= -1; 1329​ value *= -1;
1265 pValue = gsl_cdf_tdist_P(value, df); 1330​ pValue.append(gsl_cdf_tdist_P(value, df));
1266 printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("≤"), col2Name), "blue"); 1331​ printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("≤"), col2Name), "blue");
1267 printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING(">"), col2Name), "blue"); 1332​ printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING(">"), col2Name), "blue");
1268 break; 1333​ break;
1269 } 1334​ }
1270 case HypothesisTest::Test::Tail::Two: { 1335​ case HypothesisTest::Test::Tail::Two: {
1271 pValue = 2.*gsl_cdf_tdist_P(-fabs(value), df); 1336​ pValue.append(2.*gsl_cdf_tdist_P(-fabs(value), df));
1272 1337​
1273 printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("="), col2Name), "blue"); 1338​ printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("="), col2Name), "blue");
1274 printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("≠"), col2Name), "blue"); 1339​ printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1Name, UTF8_QSTRING("≠"), col2Name), "blue");
Context not available.
1280 case HypothesisTest::Test::Type::ZTest: { 1345​ case HypothesisTest::Test::Type::ZTest: {
1281 switch (tailType) { 1346​ switch (tailType) {
1282 case HypothesisTest::Test::Tail::Negative: { 1347​ case HypothesisTest::Test::Tail::Negative: {
1283 pValue = gsl_cdf_gaussian_P(value - mean, sp); 1348​ pValue.append(gsl_cdf_gaussian_P(value - mean, sp));
1284 printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("≥"), col2Name), "blue"); 1349​ printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("≥"), col2Name), "blue");
1285 printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("⋖"), col2Name), "blue"); 1350​ printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("⋖"), col2Name), "blue");
1286 break; 1351​ break;
1287 } 1352​ }
1288 case HypothesisTest::Test::Tail::Positive: { 1353​ case HypothesisTest::Test::Tail::Positive: {
1289 value *= -1; 1354​ value *= -1;
1290 pValue = nsl_stats_tdist_p(value - mean, sp); 1355​ pValue.append(nsl_stats_tdist_p(value - mean, sp));
1291 printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("≤"), col2Name), "blue"); 1356​ printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("≤"), col2Name), "blue");
1292 printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING(">"), col2Name), "blue"); 1357​ printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING(">"), col2Name), "blue");
1293 break; 1358​ break;
1294 } 1359​ }
1295 case HypothesisTest::Test::Tail::Two: { 1360​ case HypothesisTest::Test::Tail::Two: {
1296 pValue = 2.*gsl_cdf_gaussian_P(value - mean, sp); 1361​ pValue.append(2.*gsl_cdf_gaussian_P(value - mean, sp));
1297 printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("="), col2Name), "blue"); 1362​ printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("="), col2Name), "blue");
1298 printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("≠"), col2Name), "blue"); 1363​ printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1Name, UTF8_QSTRING("≠"), col2Name), "blue");
1299 break; 1364​ break;
Context not available.
1306 break; 1371​ break;
1307 } 1372​ }
1308 1373​
1309 if (pValue > 1) 1374​ if (pValue[0] > 1)
1310 return 1; 1375​ return 1;
sgerlachUnsubmitted
Done

check indentation

sgerlach: check indentation
devanshuagarwalAuthorUnsubmitted
Done

It is indented in the source file. I don't know why it is not shown in the differnce file.

devanshuagarwal: It is indented in the source file. I don't know why it is not shown in the differnce file.
sgerlachUnsubmitted
Done

ok. This seems to happen sometimes.

sgerlach: ok. This seems to happen sometimes.
1311 return pValue; 1376​ return pValue[0];
1312} 1377​}
1313 1378​
1314int HypothesisTestPrivate::setSpanValues(HypothesisTestPrivate::Node* root, int& totalLevels) { 1379​int HypothesisTestPrivate::setSpanValues(HypothesisTestPrivate::Node* root, int& totalLevels) {
Context not available.
1473 ".tg {border-collapse:collapse;border: 1px solid black;}" 1538​ ".tg {border-collapse:collapse;border: 1px solid black;}"
1474 ".tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border: 1px solid black;overflow:hidden;word-break:normal;color:#333;background-color:#fff;}" 1539​ ".tg td{font-family:Arial, sans-serif;font-size:14px;padding:10px 5px;border: 1px solid black;overflow:hidden;word-break:normal;color:#333;background-color:#fff;}"
1475 ".tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border: 1px solid black;overflow:hidden;word-break:normal;color:#333;background-color:#f0f0f0;}" 1540​ ".tg th{font-family:Arial, sans-serif;font-size:14px;font-weight:normal;padding:10px 5px;border: 1px solid black;overflow:hidden;word-break:normal;color:#333;background-color:#f0f0f0;}"
1476 "</style><table class=tg>"; 1541​ "</style>";
1542​
1543​ table += "<table class=tg>";
1477 1544​
1478 table += " <tr>"; 1545​ table += " <tr>";
1479 int prevLevel = 0; 1546​ int prevLevel = 0;
Context not available.
1501 cellEndTag; 1568​ cellEndTag;
1502 } 1569​ }
1503 table += " <tr>"; 1570​ table += " <tr>";
1571​ table += "</table>";
1504 return table; 1572​ return table;
1505} 1573​}
1506 1574​
Context not available.
1527 printLine(0, errorMsg, "red"); 1595​ printLine(0, errorMsg, "red");
1528} 1596​}
1529 1597​
1530void HypothesisTestPrivate::clearSummaryLayout() { ​
1531 for (int i = 0; i < 10; i++) ​
1532 resultLine[i]->clear(); ​
1533} ​
1534 ​
1535void HypothesisTestPrivate::clearTestView() { ​
1536 statsTable = ""; ​
1537 clearSummaryLayout(); ​
1538} ​
1539 ​
1540 1598​
1541/********************************************************************************** 1599​/**********************************************************************************
1542 * virtual functions implementations 1600​ * virtual functions implementations
Context not available.