diff --git a/src/backend/hypothesis_test/HypothesisTest.cpp b/src/backend/hypothesis_test/HypothesisTest.cpp index 3659c2afd..a6f0430fb 100644 --- a/src/backend/hypothesis_test/HypothesisTest.cpp +++ b/src/backend/hypothesis_test/HypothesisTest.cpp @@ -1,1038 +1,1035 @@ /*************************************************************************** File : HypothesisTest.cpp Project : LabPlot Description : Doing Hypothesis-Test on data provided -------------------------------------------------------------------- Copyright : (C) 2019 Devanshu Agarwal(agarwaldevanshu8@gmail.com) ***************************************************************************/ /*************************************************************************** * * * This program is free software; you can redistribute it and/or modify * * it under the terms of the GNU General Public License as published by * * the Free Software Foundation; either version 2 of the License, or * * (at your option) any later version. * * * * This program is distributed in the hope that it will be useful, * * but WITHOUT ANY WARRANTY; without even the implied warranty of * * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the * * GNU General Public License for more details. * * * * You should have received a copy of the GNU General Public License * * along with this program; if not, write to the Free Software * * Foundation, Inc., 51 Franklin Street, Fifth Floor, * * Boston, MA 02110-1301 USA * * * ***************************************************************************/ #include "HypothesisTest.h" #include "HypothesisTestPrivate.h" #include "kdefrontend/hypothesis_test/HypothesisTestView.h" #include "backend/spreadsheet/Spreadsheet.h" #include "backend/core/column/Column.h" #include "backend/lib/macros.h" #include "QDebug" extern "C" { #include "backend/nsl/nsl_stats.h" } #include #include #include #include #include #include #include #include HypothesisTest::HypothesisTest(const QString &name) : AbstractPart(name), d(new HypothesisTestPrivate(this)) { } HypothesisTest::~HypothesisTest() { delete d; } void HypothesisTest::setDataSourceType(DataSourceType type) { if (type != d->dataSourceType) { d->dataSourceType = type; } } HypothesisTest::DataSourceType HypothesisTest::dataSourceType() const { return d->dataSourceType; } void HypothesisTest::setDataSourceSpreadsheet(Spreadsheet *spreadsheet) { if (spreadsheet != d->dataSourceSpreadsheet) d->setDataSourceSpreadsheet(spreadsheet); } void HypothesisTest::setColumns(QVector cols) { d->m_columns = cols; } void HypothesisTest::setColumns(QStringList cols) { return d->setColumns(cols); } QStringList HypothesisTest::allColumns() { return d->all_columns; } void HypothesisTest::setTailType(HypothesisTest::TailType tailType) { d->tail_type = tailType; } HypothesisTest::TailType HypothesisTest::tailType() { return d->tail_type; } void HypothesisTest::setPopulationMean(QVariant populationMean) { d->m_population_mean = populationMean.toDouble(); } void HypothesisTest::setSignificanceLevel(QVariant alpha) { d->m_significance_level = alpha.toDouble(); } QString HypothesisTest::testName() { return d->m_currTestName; } QString HypothesisTest::statsTable() { return d->m_stats_table; } void HypothesisTest::performTwoSampleIndependentTTest(bool categorical_variable, bool equal_variance) { d->m_currTestName = "

Two Sample Independent T Test

"; d->performTwoSampleIndependentTest(HypothesisTestPrivate::TestT, categorical_variable, equal_variance); } void HypothesisTest::performTwoSamplePairedTTest() { d->m_currTestName = "

Two Sample Paried T Test

"; d->performTwoSamplePairedTest(HypothesisTestPrivate::TestT); } void HypothesisTest::performOneSampleTTest() { d->m_currTestName = "

One Sample T Test

"; d->performOneSampleTest(HypothesisTestPrivate::TestT); } void HypothesisTest::performTwoSampleIndependentZTest() { d->m_currTestName = "

Two Sample Independent Z Test

"; d->performTwoSampleIndependentTest(HypothesisTestPrivate::TestZ); } void HypothesisTest::performTwoSamplePairedZTest() { d->m_currTestName = "

Two Sample Paired Z Test

"; d->performTwoSamplePairedTest(HypothesisTestPrivate::TestZ); } void HypothesisTest::performOneSampleZTest() { d->m_currTestName = "

One Sample Z Test

"; d->performOneSampleTest(HypothesisTestPrivate::TestZ); } void HypothesisTest::performLeveneTest(bool categorical_variable) { d->m_currTestName = "

Levene Test for Equality of Variance

"; d->performLeveneTest(categorical_variable); } /****************************************************************************** * Private Implementations * ****************************************************************************/ HypothesisTestPrivate::HypothesisTestPrivate(HypothesisTest* owner) : q(owner) { } HypothesisTestPrivate::~HypothesisTestPrivate() { } void HypothesisTestPrivate::setDataSourceSpreadsheet(Spreadsheet *spreadsheet) { dataSourceSpreadsheet = spreadsheet; //setting rows and columns count; m_rowCount = dataSourceSpreadsheet->rowCount(); m_columnCount = dataSourceSpreadsheet->columnCount(); for (auto* col : dataSourceSpreadsheet->children()) { all_columns << col->name(); } } void HypothesisTestPrivate::setColumns(QStringList cols) { m_columns.clear(); Column* column = new Column("column"); for (QString col : cols) { if (!cols.isEmpty()) { column = dataSourceSpreadsheet->column(col); m_columns.append(column); } } } /**************************Two Sample Independent *************************************/ void HypothesisTestPrivate::performTwoSampleIndependentTest(TestType test,bool categorical_variable, bool equal_variance) { QString test_name; double value; int df = 0; double p_value = 0; clearGlobalVariables(); if (m_columns.size() != 2) { printError("Inappropriate number of columns selected"); emit q->changed(); return; } int n[2]; double sum[2], mean[2], std[2]; QString col1_name = m_columns[0]->name(); QString col2_name = m_columns[1]->name(); if (!categorical_variable && (m_columns[0]->columnMode() == AbstractColumn::Integer || m_columns[0]->columnMode() == AbstractColumn::Numeric)) { for (int i = 0; i < 2; i++) { findStats(m_columns[i], n[i], sum[i], mean[i], std[i]); if (n[i] < 1) { printError("At least one of selected column is empty"); emit q->changed(); return; } } } else { QMap col_name; int np; int total_rows; countPartitions(m_columns[0], np, total_rows); if (np != 2) { printError( i18n("Number of Categorical Variable in Column %1 is not equal to 2", m_columns[0]->name())); emit q->changed(); return; } ErrorType error_code = findStatsCategorical(m_columns[0], m_columns[1], n, sum, mean, std, col_name, np, total_rows); switch (error_code) { case ErrorUnqualSize: { printError( i18n("Unequal size between Column %1 and Column %2", m_columns[0]->name(), m_columns[1]->name())); emit q->changed(); return; }case ErrorEmptyColumn: { printError("At least one of selected column is empty"); emit q->changed(); return; } case NoError: break; } QMapIterator i(col_name); while (i.hasNext()) { i.next(); if (i.value() == 1) col1_name = i.key(); else col2_name = i.key(); } } QVariant row_major[] = {"", "N", "Sum", "Mean", "Std", col1_name, n[0], sum[0], mean[0], std[0], col2_name, n[1], sum[1], mean[1], std[1]}; m_stats_table = getHtmlTable(3, 5, row_major); switch (test) { case TestT: { test_name = "T"; if (equal_variance) { df = n[0] + n[1] - 2; double sp = qSqrt( ((n[0]-1)*qPow(std[0],2) + (n[1]-1)*qPow(std[1],2))/df); value = (mean[0] - mean[1])/(sp*qSqrt(1.0/n[0] + 1.0/n[1])); printLine(9, "Assumption: Equal Variance b/w both population means"); } else { double temp_val; temp_val = qPow( qPow(std[0], 2)/n[0] + qPow(std[1], 2)/n[1], 2); temp_val = temp_val / ( (qPow( (qPow(std[0], 2)/n[0]), 2)/(n[0]-1)) + (qPow( (qPow(std[1], 2)/n[1]), 2)/(n[1]-1))); df = qRound(temp_val); value = (mean[0] - mean[1]) / (qSqrt( (qPow(std[0], 2)/n[0]) + (qPow(std[1], 2)/n[1]))); printLine(9, "Assumption: UnEqual Variance b/w both population means"); } break; } case TestZ: { test_name = "Z"; df = n[0] + n[1] - 2; double sp = qSqrt( ((n[0]-1)*qPow(std[0],2) + (n[1]-1)*qPow(std[1],2))/df); value = (mean[0] - mean[1])/(sp*qSqrt(1.0/n[0] + 1.0/n[1])); } } m_currTestName = i18n("

Two Sample Independent %1 Test for %2 vs %3

", test_name, col1_name, col2_name); p_value = getPValue(test, value, col1_name, col2_name, df); printLine(2, i18n("Significance level is %1", m_significance_level), "blue"); printLine(4, i18n("%1 Value is %2 ", test_name, value), "green"); printLine(5, i18n("P Value is %1 ", p_value), "green"); printLine(6, i18n("Degree of Freedom is %1", df), "green"); if (p_value <= m_significance_level) q->m_view->setResultLine(5, i18n("We can safely reject Null Hypothesis for significance level %1", m_significance_level), Qt::ToolTipRole); else q->m_view->setResultLine(5, i18n("There is a plausibility for Null Hypothesis to be true"), Qt::ToolTipRole); emit q->changed(); return; } /********************************Two Sample Paired ***************************************/ void HypothesisTestPrivate::performTwoSamplePairedTest(TestType test) { QString test_name; int n; double sum, mean, std; double value; int df = 0; double p_value = 0; clearGlobalVariables(); if (m_columns.size() != 2) { printError("Inappropriate number of columns selected"); emit q->changed(); return; } for (int i = 0; i < 2; i++) { if (!(m_columns[i]->columnMode() == AbstractColumn::Numeric || m_columns[i]->columnMode() == AbstractColumn::Integer)) { printError("select only columns with numbers"); emit q->changed(); return; } } ErrorType error_code = findStatsPaired(m_columns[0], m_columns[1], n, sum, mean, std); switch (error_code) { case ErrorUnqualSize: { printError("both columns are having different sizes"); emit q->changed(); return; } case ErrorEmptyColumn: { printError("columns are empty"); emit q->changed(); return; } case NoError: break; - default: - emit q->changed(); - return; } if (n == -1) { printError("both columns are having different sizes"); emit q->changed(); return; } if (n < 1) { printError("columns are empty"); emit q->changed(); return; } QVariant row_major[] = {"", "N", "Sum", "Mean", "Std", "difference", n, sum, mean, std}; m_stats_table = getHtmlTable(2, 5, row_major); switch (test) { case TestT: { value = mean / (std/qSqrt(n)); df = n - 1; test_name = "T"; printLine(6, i18n("Degree of Freedom is %1name(), i18n("%1",m_population_mean), df); m_currTestName = i18n("

One Sample %1 Test for %2 vs %3

", test_name, m_columns[0]->name(), m_columns[1]->name()); printLine(2, i18n("Significance level is %1 ", m_significance_level), "blue"); printLine(4, i18n("%1 Value is %2 ", test_name, value), "green"); printLine(5, i18n("P Value is %1 ", p_value), "green"); if (p_value <= m_significance_level) q->m_view->setResultLine(5, i18n("We can safely reject Null Hypothesis for significance level %1", m_significance_level), Qt::ToolTipRole); else q->m_view->setResultLine(5, i18n("There is a plausibility for Null Hypothesis to be true"), Qt::ToolTipRole); emit q->changed(); return; } /******************************** One Sample ***************************************/ void HypothesisTestPrivate::performOneSampleTest(TestType test) { QString test_name; double value; int df = 0; double p_value = 0; clearGlobalVariables(); if (m_columns.size() != 1) { printError("Inappropriate number of columns selected"); emit q->changed(); return; } if ( !(m_columns[0]->columnMode() == AbstractColumn::Numeric || m_columns[0]->columnMode() == AbstractColumn::Integer)) { printError("select only columns with numbers"); emit q->changed(); return; } int n; double sum, mean, std; ErrorType error_code = findStats(m_columns[0], n, sum, mean, std); switch (error_code) { case ErrorUnqualSize: { printError("column is empty"); emit q->changed(); return; } case NoError: break; - default: { + case ErrorEmptyColumn: { emit q->changed(); return; } } QVariant row_major[] = {"", "N", "Sum", "Mean", "Std", m_columns[0]->name(), n, sum, mean, std}; m_stats_table = getHtmlTable(2, 5, row_major); switch (test) { case TestT: { test_name = "T"; value = (mean - m_population_mean) / (std/qSqrt(n)); df = n - 1; printLine(6, i18n("Degree of Freedom is %1", df), "blue"); break; } case TestZ: { test_name = "Z"; df = 0; value = (mean - m_population_mean) / (std/qSqrt(n)); }} p_value = getPValue(test, value, m_columns[0]->name(), i18n("%1",m_population_mean), df); m_currTestName = i18n("

One Sample %1 Test for %2

", test_name, m_columns[0]->name()); printLine(2, i18n("Significance level is %1", m_significance_level), "blue"); printLine(4, i18n("%1 Value is %2", test_name, value), "green"); printLine(5, i18n("P Value is %1", p_value), "green"); if (p_value <= m_significance_level) q->m_view->setResultLine(5, i18n("We can safely reject Null Hypothesis for significance level %1", m_significance_level), Qt::ToolTipRole); else q->m_view->setResultLine(5, i18n("There is a plausibility for Null Hypothesis to be true"), Qt::ToolTipRole); emit q->changed(); return; } /**************************************Levene Test****************************************/ void HypothesisTestPrivate::performLeveneTest(bool categorical_variable) { QString test_name; double f_value; int df = 0; // degree of freedom double p_value = 0; int np = 0; // number of partitions int n = 0; int total_rows = 0; clearGlobalVariables(); if (m_columns.size() != 2) { printError("Inappropriate number of columns selected"); emit q->changed(); return; } if (!categorical_variable && (m_columns[0]->columnMode() == AbstractColumn::Integer || m_columns[0]->columnMode() == AbstractColumn::Numeric)) np = m_columns.size(); else countPartitions(m_columns[0], np, n); if (np < 2) { printError("select atleast two columns/ classes"); emit q->changed(); return; } double* yi_bar = new double[np]; double* zi_bar = new double[np]; double zi_bar_bar = 0; double* ni = new double[np]; for (int i = 0; i < np; i++) { yi_bar[i] = 0; zi_bar[i] = 0; ni[i] = 0; } QString* col_names = new QString[np]; if (!categorical_variable && (m_columns[0]->columnMode() == AbstractColumn::Integer || m_columns[0]->columnMode() == AbstractColumn::Numeric)) { total_rows = m_columns[0]->rowCount(); double value = 0; for (int j = 0; j < total_rows; j++) { int number_nan_cols = 0; for (int i = 0; i < np; i++) { value = m_columns[i]->valueAt(j); if (std::isnan(value)) { number_nan_cols++; continue; } yi_bar[i] += value; ni[i]++; n++; } if (number_nan_cols == np) { total_rows = j; break; } } for (int i = 0; i < np; i++) { if (ni[i] > 0) yi_bar[i] = yi_bar[i] / ni[i]; } for (int j = 0; j < total_rows; j++) { for (int i = 0; i < np; i++) { value = m_columns[i]->valueAt(j); if (!(std::isnan(value))) zi_bar[i] += abs(value - yi_bar[i]); } } for (int i = 0; i < np; i++) { zi_bar_bar += zi_bar[i]; if (ni[i] > 0) zi_bar[i] = zi_bar[i] / ni[i]; } zi_bar_bar = zi_bar_bar / n; double numerator_value = 0; double denominator_value = 0; for (int j = 0; j < total_rows; j++) { for (int i = 0; i < np; i++) { value = m_columns[i]->valueAt(j); if (!(std::isnan(value))) { double zij = abs(value - yi_bar[i]); denominator_value += qPow( (zij - zi_bar[i]), 2); } } } for (int i = 0; i < np; i++) { col_names[i] = m_columns[i]->name(); numerator_value += ni[i]*qPow( (zi_bar[i]-zi_bar_bar), 2); } f_value = ((n - np) / (np - 1)) * (numerator_value / denominator_value); // qDebug() << "n is " << n; // qDebug() << "fvalue is " << f_value; // qDebug() << "numerator is " << numerator_value; // qDebug() << "denominator is " << denominator_value; } else { QMap classname_to_index; AbstractColumn::ColumnMode original_col_mode = m_columns[0]->columnMode(); m_columns[0]->setColumnMode(AbstractColumn::Text); int partition_number = 1; QString name; double value; int class_index; for (int j = 0; j < n; j++) { name = m_columns[0]->textAt(j); value = m_columns[1]->valueAt(j); if (std::isnan(value)) { n = j; break; } if (classname_to_index[name] == 0) { classname_to_index[name] = partition_number; partition_number++; } class_index = classname_to_index[name]-1; ni[class_index]++; yi_bar[class_index] += value; } for (int i = 0; i < np; i++) { if (ni[i] > 0) yi_bar[i] = yi_bar[i] / ni[i]; } for (int j = 0; j < n; j++) { name = m_columns[0]->textAt(j); value = m_columns[1]->valueAt(j); class_index = classname_to_index[name] - 1; zi_bar[class_index] += abs(value - yi_bar[class_index]); } for (int i = 0; i < np; i++) { zi_bar_bar += zi_bar[i]; zi_bar[i] = zi_bar[i] / ni[i]; } zi_bar_bar = zi_bar_bar / n; double numerator_value = 0; double denominator_value = 0; for (int j = 0; j < n; j++) { name = m_columns[0]->textAt(j); value = m_columns[1]->valueAt(j); class_index = classname_to_index[name] - 1; double zij = abs(value - yi_bar[class_index]); denominator_value += qPow( (zij - zi_bar[class_index]), 2); } for (int i = 0; i < np; i++) numerator_value += ni[i]*qPow( (zi_bar[i]-zi_bar_bar), 2); f_value = ((n - np) / (np - 1)) * (numerator_value / denominator_value); // qDebug() << "n is " << n; // qDebug() << "fvalue is " << f_value; // qDebug() << "numerator is " << numerator_value; // qDebug() << "denominator is " << denominator_value; QMapIterator i(classname_to_index); while (i.hasNext()) { i.next(); col_names[i.value()-1] = i.key(); } m_columns[0]->setColumnMode(original_col_mode); } df = n - np; int row_count = np+1; int column_count = 4; QVariant* row_major = new QVariant[row_count*column_count]; // header data; row_major[0] = ""; row_major[1] = "Ni"; row_major[2] = "Yi_bar"; row_major[3] = "Zi_bar"; // table data for (int row_i = 1; row_i < row_count; row_i++) { row_major[row_i*column_count] = col_names[row_i-1]; row_major[row_i*column_count + 1] = ni[row_i-1]; row_major[row_i*column_count + 2] = yi_bar[row_i-1]; row_major[row_i*column_count + 3] = zi_bar[row_i-1]; } m_stats_table = getHtmlTable(row_count, column_count, row_major); p_value = nsl_stats_fdist_p(f_value, static_cast(np-1), df); printLine(0, "Null Hypothesis: Variance is equal between all classes", "blue"); printLine(1, "Alternate Hypothesis: Variance is not equal in at-least one pair of classes", "blue"); printLine(2, i18n("Significance level is %1", m_significance_level), "blue"); printLine(4, i18n("F Value is %1 ", f_value), "green"); printLine(5, i18n("P Value is %1 ", p_value), "green"); printLine(6, i18n("Degree of Freedom is %1", df), "green"); if (p_value <= m_significance_level) { q->m_view->setResultLine(5, i18n("We can safely reject Null Hypothesis for significance level %1", m_significance_level), Qt::ToolTipRole); printLine(8, "Requirement for homogeneity is not met", "red"); } else { q->m_view->setResultLine(5, i18n("There is a plausibility for Null Hypothesis to be true"), Qt::ToolTipRole); printLine(8, "Requirement for homogeneity is met", "green"); } emit q->changed(); return; } /***************************************Helper Functions*************************************/ HypothesisTestPrivate::ErrorType HypothesisTestPrivate::findStats(const Column* column, int &count, double &sum, double &mean, double &std) { sum = 0; mean = 0; std = 0; count = column->rowCount(); for (int i = 0; i < count; i++) { double row = column->valueAt(i); if ( std::isnan(row)) { count = i; break; } sum += row; } if (count < 1) return HypothesisTestPrivate::ErrorEmptyColumn; mean = sum/count; for (int i = 0; i < count; i++) { double row = column->valueAt(i); std += qPow( (row - mean), 2); } if (count > 1) std = std / (count-1); std = qSqrt(std); return HypothesisTestPrivate::NoError; } HypothesisTestPrivate::ErrorType HypothesisTestPrivate::findStatsPaired(const Column* column1, const Column* column2, int &count, double &sum, double &mean, double &std) { sum = 0; mean = 0; std = 0; int count1 = column1->rowCount(); int count2 = column2->rowCount(); count = qMin(count1, count2); double cell1, cell2; for (int i = 0; i < count; i++) { cell1 = column1->valueAt(i); cell2 = column2->valueAt(i); if (std::isnan(cell1) || std::isnan(cell2)) { if (std::isnan(cell1) && std::isnan(cell2)) count = i; else return HypothesisTestPrivate::ErrorUnqualSize; break; } sum += cell1 - cell2; } if (count < 1) return HypothesisTestPrivate::ErrorEmptyColumn; mean = sum/count; double row; for (int i = 0; i < count; i++) { cell1 = column1->valueAt(i); cell2 = column2->valueAt(i); row = cell1 - cell2; std += qPow( (row - mean), 2); } if (count > 1) std = std / (count-1); std = qSqrt(std); return HypothesisTestPrivate::NoError; } void HypothesisTestPrivate::countPartitions(Column *column, int &np, int &total_rows) { total_rows = column->rowCount(); np = 0; QString cell_value; QMap discovered_categorical_var; AbstractColumn::ColumnMode original_col_mode = column->columnMode(); column->setColumnMode(AbstractColumn::Text); for (int i = 0; i < total_rows; i++) { cell_value = column->textAt(i); if (cell_value.isEmpty()) { total_rows = i; break; } if (discovered_categorical_var[cell_value]) continue; discovered_categorical_var[cell_value] = true; np++; } column->setColumnMode(original_col_mode); } HypothesisTestPrivate::ErrorType HypothesisTestPrivate::findStatsCategorical(Column *column1, Column *column2, int n[], double sum[], double mean[], double std[], QMap &col_name, const int &np, const int &total_rows) { Column* columns[] = {column1, column2}; for (int i = 0; i < np; i++) { n[i] = 0; sum[i] = 0; mean[i] = 0; std[i] = 0; } AbstractColumn::ColumnMode original_col_mode = columns[0]->columnMode(); columns[0]->setColumnMode(AbstractColumn::Text); int partition_number = 1; for (int i = 0; i < total_rows; i++) { QString name = columns[0]->textAt(i); name = columns[0]->textAt(i); double value = columns[1]->valueAt(i); if (std::isnan(value)) { columns[0]->setColumnMode(original_col_mode); return HypothesisTestPrivate::ErrorUnqualSize; } if (col_name[name] == 0) { col_name[name] = partition_number; partition_number++; } n[col_name[name]-1]++; sum[col_name[name]-1] += value; } for (int i = 0; i < np; i++) mean[i] = sum[i] / n[i]; for (int i = 0; i < total_rows; i++) { QString name = columns[0]->textAt(i); double value = columns[1]->valueAt(i); std[col_name[name]-1] += qPow( (value - mean[col_name[name]-1]), 2); } for (int i = 0; i < np; i++) { if (n[i] > 1) std[i] = std[i] / (n[i] - 1); std[i] = qSqrt(std[i]); } columns[0]->setColumnMode(original_col_mode); return HypothesisTestPrivate::NoError; } double HypothesisTestPrivate::getPValue(const HypothesisTestPrivate::TestType &test, double &value, const QString &col1_name, const QString &col2_name, const int df) { double p_value = 0; //TODO change ("⋖") symbol to ("<"), currently macro UTF8_QSTRING is not working properly if used "<" symbol; switch (test) { case TestT: { switch (tail_type) { case HypothesisTest::TailNegative: p_value = nsl_stats_tdist_p(value, df); printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1_name, UTF8_QSTRING("≥"), col2_name), "blue"); printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1_name, UTF8_QSTRING("⋖"), col2_name), "blue"); break; case HypothesisTest::TailPositive: value *= -1; p_value = nsl_stats_tdist_p(value, df); printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1_name, UTF8_QSTRING("≤"), col2_name), "blue"); printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1_name, UTF8_QSTRING(">"), col2_name), "blue"); break; case HypothesisTest::TailTwo: p_value = nsl_stats_tdist_p(value, df) + nsl_stats_tdist_p(-1*value, df); printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3", col1_name, UTF8_QSTRING("="), col2_name), "blue"); printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3", col1_name, UTF8_QSTRING("≠"), col2_name), "blue"); break; } break; } case TestZ: { switch (tail_type) { case HypothesisTest::TailNegative: p_value = nsl_stats_tdist_p(value, df); printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1_name, UTF8_QSTRING("≥"), col2_name), "blue"); printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1_name, UTF8_QSTRING("⋖"), col2_name), "blue"); break; case HypothesisTest::TailPositive: value *= -1; p_value = nsl_stats_tdist_p(value, df); printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1_name, UTF8_QSTRING("≤"), col2_name), "blue"); printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1_name, UTF8_QSTRING(">"), col2_name), "blue"); break; case HypothesisTest::TailTwo: p_value = nsl_stats_tdist_p(value, df) + nsl_stats_tdist_p(-1*value, df); printLine(0, i18n("Null Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1_name, UTF8_QSTRING("="), col2_name), "blue"); printLine(1, i18n("Alternate Hypothesis: Population mean of %1 %2 Population mean of %3 ", col1_name, UTF8_QSTRING("≠"), col2_name), "blue"); break; } break; } } if (p_value > 1) return 1; return p_value; } QString HypothesisTestPrivate::getHtmlTable(int row, int column, QVariant *row_major) { if (row < 1 || column < 1) return QString(); QString table = ""; table = "" "" " "; QString bg = "tg-0pky"; bool pky = true; QString element; table += " "; for (int j = 0; j < column; j++) { element = row_major[j].toString(); table += i18n(" ", bg, element); } table += " "; if (pky) bg = "tg-0pky"; else bg = "tg-btxf"; pky = !pky; for (int i = 1; i < row; i++) { table += " "; QString element = row_major[i*column].toString(); table += i18n(" ", bg, element); for (int j = 1; j < column; j++) { QString element = row_major[i*column+j].toString(); table += i18n(" ", bg, element); } table += " "; if (pky) bg = "tg-0pky"; else bg = "tg-btxf"; pky = !pky; } table += "
%2
%2%2
"; return table; } void HypothesisTestPrivate::printLine(const int &index, const QString &msg, const QString &color) { q->m_view->setResultLine(index, i18n("

%2

", color, msg)); return; } void HypothesisTestPrivate::printError(const QString &error_msg) { printLine(0, error_msg, "red"); emit q->changed(); } void HypothesisTestPrivate::clearGlobalVariables() { m_stats_table = ""; q->m_view->clearResult(); } /********************************************************************************** * virtual functions implementations * ********************************************************************************/ /*! Saves as XML. */ void HypothesisTest::save(QXmlStreamWriter* writer) const { writer->writeStartElement("hypothesisTest"); writeBasicAttributes(writer); writeCommentElement(writer); //TODO: writer->writeEndElement(); } /*! Loads from XML. */ bool HypothesisTest::load(XmlStreamReader* reader, bool preview) { Q_UNUSED(preview); if (!readBasicAttributes(reader)) return false; //TODO: return !reader->hasError(); } Spreadsheet *HypothesisTest::dataSourceSpreadsheet() const { return d->dataSourceSpreadsheet; } bool HypothesisTest::exportView() const { return true; } bool HypothesisTest::printView() { return true; } bool HypothesisTest::printPreview() const { return true; } /*! Constructs a primary view on me. This method may be called multiple times during the life time of an Aspect, or it might not get called at all. Aspects must not depend on the existence of a view for their operation. */ QWidget* HypothesisTest::view() const { if (!m_partView) { m_view = new HypothesisTestView(const_cast(this)); m_partView = m_view; } return m_partView; } /*! Returns a new context menu. The caller takes ownership of the menu. */ QMenu* HypothesisTest::createContextMenu() { QMenu* menu = AbstractPart::createContextMenu(); // Q_ASSERT(menu); // emit requestProjectContextMenu(menu); return menu; }