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RegressionDialog.cxx
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1/* -*- Mode: C++; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- */
2/*
3 * This file is part of the LibreOffice project.
4 *
5 * This Source Code Form is subject to the terms of the Mozilla Public
6 * License, v. 2.0. If a copy of the MPL was not distributed with this
7 * file, You can obtain one at http://mozilla.org/MPL/2.0/.
8 *
9 */
10
11#include <sal/config.h>
12
13#include <string_view>
14
15#include <document.hxx>
16#include <reffact.hxx>
18#include <RegressionDialog.hxx>
19#include <scresid.hxx>
20#include <strings.hrc>
21
22/*
23 Some regression basics
24 ----------------------
25
26 1. Linear regression fits using data, a linear function between the dependent variable and the independent variable(s).
27 The basic form of this function is :-
28
29 y = b + m_1*x_1 + m_2*x_2 + ... + m_k*x_k
30
31 where y is the dependent variable
32 x_1, x_2, ..., x_k are the k independent variables
33 b is the intercept
34 m_1, m_2, ..., m_k are the slopes corresponding to the variables x_1, x_2, ..., x_k respectively.
35
36
37 This equation for n observations can be compactly written using matrices as :-
38
39 y = X*A
40
41 where y is the n dimensional column vector containing dependent variable observations.
42 where X is matrix of shape n*(k+1) where a row looks like [ 1 x_1 x_2 ... x_k ]
43 A is the k+1 dimensional column vector [ b m_1 m_2 ... m_k ]
44
45 Calc formula LINEST(Y_array ; X_array) can be used to compute all entries in "A" along with many other statistics.
46
47
48 2. Logarithmic regression is basically used to find a linear function between the dependent variable and
49 the natural logarithm of the independent variable(s).
50 So the basic form of this functions is :-
51
52 y = b + m_1*ln(x_1) + m_2*ln(x_2) + ... + m_k*ln(x_k)
53
54 This can be again written in a compact matrix form for n observations.
55
56 y = ln(X)*A
57
58 where y is the n dimensional column vector containing dependent variable observations.
59 where X is matrix of shape n*(k+1) where a row looks like [ e x_1 x_2 ... x_k ]
60 A is the k+1 dimensional column vector [ b m_1 m_2 ... m_k ]
61
62 To estimate A, we use the formula =LINEST(Y_array ; LN(X_array))
63
64
65 3. Power regression is used to fit the following model :-
66
67 y = b * (x_1 ^ m_1) * (x_2 ^ m_2) * ... * (x_k ^ m_k)
68
69 To reduce this to a linear function(so that we can still use LINEST()), we take natural logarithm on both sides
70
71 ln(y) = c + m_1*ln(x_1) + m_2*ln(x_2) + ... + m_k*ln(x_k) ; where c = ln(b)
72
73
74 This again can be written compactly in matrix form as :-
75
76 ln(y) = ln(X)*A
77
78 where y is the n dimensional column vector containing dependent variable observations.
79 where X is matrix of shape n*(k+1) where a row looks like [ e x_1 x_2 ... x_k ]
80 A is the k+1 dimensional column vector [ c m_1 m_2 ... m_k ]
81
82 To estimate A, we use the formula =LINEST(LN(Y_array) ; LN(X_array))
83
84 Once we get A, to get back y from x's we use the formula :-
85
86 y = exp( ln(X)*A )
87
88
89
90 Some references for computing confidence interval for the regression coefficients :-
91
92 [1] https://en.wikipedia.org/wiki/Student%27s_t-test#Slope_of_a_regression_line
93 [2] https://en.wikipedia.org/wiki/Simple_linear_regression#Normality_assumption
94 [3] https://onlinecourses.science.psu.edu/stat414/node/280
95
96 */
97
98namespace
99{
100 enum class ScRegType {
101 LINEAR,
102 LOGARITHMIC,
103 POWER
104 };
105
106 const TranslateId constRegressionModel[] =
107 {
108 STR_LABEL_LINEAR,
109 STR_LABEL_LOGARITHMIC,
110 STR_LABEL_POWER
111 };
112
113 OUString constTemplateLINEST[] =
114 {
115 "=LINEST(%VARIABLE2_RANGE% ; %VARIABLE1_RANGE% ; %CALC_INTERCEPT% ; TRUE)",
116 "=LINEST(%VARIABLE2_RANGE% ; LN(%VARIABLE1_RANGE%) ; %CALC_INTERCEPT% ; TRUE)",
117 "=LINEST(LN(%VARIABLE2_RANGE%) ; LN(%VARIABLE1_RANGE%) ; %CALC_INTERCEPT% ; TRUE)"
118 };
119
120 OUString constRegressionFormula[] =
121 {
122 "=MMULT(%XDATAMATRIX_RANGE% ; %SLOPES_RANGE%) + %INTERCEPT_ADDR%",
123 "=MMULT(LN(%XDATAMATRIX_RANGE%) ; %SLOPES_RANGE%) + %INTERCEPT_ADDR%",
124 "=EXP(MMULT(LN(%XDATAMATRIX_RANGE%) ; %SLOPES_RANGE%) + %INTERCEPT_ADDR%)"
125 };
126
127} // end anonymous namespace
128
129static size_t lcl_GetNumRowsColsInRange(const ScRange& rRange, bool bRows)
130{
131 if (bRows)
132 return rRange.aEnd.Row() - rRange.aStart.Row() + 1;
133
134 return rRange.aEnd.Col() - rRange.aStart.Col() + 1;
135}
136
138 SfxBindings* pSfxBindings, SfxChildWindow* pChildWindow,
139 weld::Window* pParent, ScViewData& rViewData )
141 pSfxBindings, pChildWindow, pParent, rViewData,
142 "modules/scalc/ui/regressiondialog.ui", "RegressionDialog")
143 , mbUnivariate(true)
144 , mnNumIndependentVars(1)
145 , mnNumObservations(0)
147 , mbCalcIntercept(true)
148 , mxWithLabelsCheckBox(m_xBuilder->weld_check_button("withlabels-check"))
151 , mxErrorMessage(m_xBuilder->weld_label("error-message"))
152 , mxConfidenceLevelField(m_xBuilder->weld_spin_button("confidencelevel-spin"))
153 , mxCalcResidualsCheckBox(m_xBuilder->weld_check_button("calcresiduals-check"))
154 , mxNoInterceptCheckBox(m_xBuilder->weld_check_button("nointercept-check"))
155{
158}
159
161{
162}
163
165{
167}
168
170{
171 return STR_REGRESSION_UNDO_NAME;
172}
173
175{
178 FormulaTemplate aTemplate(&mDocument);
180 mbCalcIntercept = !mxNoInterceptCheckBox->get_active();
181
182 // max col of our output should account for
183 // 1. constant term column,
184 // 2. mnNumIndependentVars columns
185 // 3. Actual Y column
186 // 4. Predicted Y column
187 // 5. Residual Column
188 SCCOL nOutputMaxCol = mOutputAddress.Col() + mnNumIndependentVars + 3;
189
192
193 aTemplate.autoReplaceRange("%VARIABLE1_RANGE%", aXDataRange);
194 aTemplate.autoReplaceRange("%VARIABLE2_RANGE%", aYDataRange);
195 size_t nRegressionIndex = GetRegressionTypeIndex();
196 ScRegType eRegType = static_cast<ScRegType>(nRegressionIndex);
197 bool bTakeLogX = eRegType == ScRegType::LOGARITHMIC || eRegType == ScRegType::POWER;
198
199 WriteRawRegressionResults(aOutput, aTemplate, nRegressionIndex);
200 WriteRegressionStatistics(aOutput, aTemplate);
201 WriteRegressionANOVAResults(aOutput, aTemplate);
202 WriteRegressionEstimatesWithCI(aOutput, aTemplate, bTakeLogX);
203 if (mxCalcResidualsCheckBox->get_active())
204 WritePredictionsWithResiduals(aOutput, aTemplate, nRegressionIndex);
205
209}
210
212{
214 {
215 mxErrorMessage->set_label(ScResId(STR_MESSAGE_XINVALID_RANGE));
216 return false;
217 }
218
220 {
221 mxErrorMessage->set_label(ScResId(STR_MESSAGE_YINVALID_RANGE));
222 return false;
223 }
224
226 {
228 return false;
229 }
230
231 {
232 double fConfidenceLevel = mxConfidenceLevelField->get_value();
233 if ( fConfidenceLevel <= 0.0 || fConfidenceLevel >= 100.0 )
234 {
235 mxErrorMessage->set_label(ScResId(STR_MESSAGE_INVALID_CONFIDENCE_LEVEL));
236 return false;
237 }
238 }
239
242
243 bool bGroupedByColumn = mGroupedBy == BY_COLUMN;
244
245 bool bYHasSingleDim = (
246 (bGroupedByColumn &&
248 (!bGroupedByColumn &&
250
251 if (!bYHasSingleDim)
252 {
253 if (bGroupedByColumn)
254 mxErrorMessage->set_label(ScResId(STR_MESSAGE_YVARIABLE_MULTI_COLUMN));
255 else
256 mxErrorMessage->set_label(ScResId(STR_MESSAGE_YVARIABLE_MULTI_ROW));
257 return false;
258 }
259
260 bool bWithLabels = mxWithLabelsCheckBox->get_active();
261
262 size_t nYObs = lcl_GetNumRowsColsInRange(mVariable2Range, bGroupedByColumn);
263 size_t nNumXVars = lcl_GetNumRowsColsInRange(mVariable1Range, !bGroupedByColumn);
264 mbUnivariate = nNumXVars == 1;
265 // Observation count mismatch check
266 if (lcl_GetNumRowsColsInRange(mVariable1Range, bGroupedByColumn) != nYObs)
267 {
268 if (mbUnivariate)
269 mxErrorMessage->set_label(ScResId(STR_MESSAGE_UNIVARIATE_NUMOBS_MISMATCH));
270 else
271 mxErrorMessage->set_label(ScResId(STR_MESSAGE_MULTIVARIATE_NUMOBS_MISMATCH));
272 return false;
273 }
274
275 mnNumIndependentVars = nNumXVars;
276 mnNumObservations = bWithLabels ? nYObs - 1 : nYObs;
277
280
281 mxErrorMessage->set_label("");
282
283 return true;
284}
285
287{
289 return 0;
291 return 1;
292 return 2;
293}
294
296{
297 if (!mxWithLabelsCheckBox->get_active())
298 return rRange;
299
301 if (mGroupedBy == BY_COLUMN)
303 else
305
307}
308
309OUString ScRegressionDialog::GetVariableNameFormula(bool bXVar, size_t nIndex, bool bWithLog)
310{
311 if (bXVar && nIndex == 0)
312 return "=\"" + ScResId(STR_LABEL_INTERCEPT) + "\"";
313
314 if (mxWithLabelsCheckBox->get_active())
315 {
317 if (mGroupedBy == BY_COLUMN)
319 else
321
323 return bWithLog ? OUString("=CONCAT(\"LN(\";" +
326 }
327
329
330 if (bXVar)
331 aDefaultVarName = "X" + OUString::number(nIndex);
332 else
334
335 return bWithLog ? OUString("=\"LN(" + aDefaultVarName + ")\"") :
336 OUString("=\"" + aDefaultVarName + "\"");
337}
338
339OUString ScRegressionDialog::GetXVariableNameFormula(size_t nIndex, bool bWithLog)
340{
341 assert(nIndex <= mnNumIndependentVars);
342 return GetVariableNameFormula(true, nIndex, bWithLog);
343}
344
346{
347 return GetVariableNameFormula(false, 1, bWithLog);
348}
349
351 size_t nRegressionIndex)
352{
353 rOutput.writeBoldString(ScResId(STR_REGRESSION));
354 rOutput.newLine();
355 // REGRESSION MODEL
356 rOutput.writeString(ScResId(STR_LABEL_REGRESSION_MODEL));
357 rOutput.nextColumn();
358 rOutput.writeString(ScResId(constRegressionModel[nRegressionIndex]));
359 rOutput.newLine();
360 rOutput.newLine();
361
362 rOutput.writeString(ScResId(STR_LINEST_RAW_OUTPUT_TITLE));
363 rOutput.newLine();
364 rOutput.push();
365
366 rTemplate.setTemplate(constTemplateLINEST[nRegressionIndex].
367 replaceFirst("%CALC_INTERCEPT%",
368 mbCalcIntercept ? std::u16string_view(u"TRUE") : std::u16string_view(u"FALSE")));
369 rOutput.writeMatrixFormula(rTemplate.getTemplate(), 1 + mnNumIndependentVars, 5);
370 // Add LINEST result components to template
371 // 1. Add ranges for coefficients and standard errors for indep. vars and the intercept.
372 // Note that these two are in the reverse order(m_n, m_n-1, ..., m_1, b) w.r.t what we expect.
373 rTemplate.autoReplaceRange("%COEFFICIENTS_REV_RANGE%", ScRange(rOutput.current(), rOutput.current(mnNumIndependentVars)));
374 rTemplate.autoReplaceRange("%SERRORSX_REV_RANGE%", ScRange(rOutput.current(0, 1), rOutput.current(mnNumIndependentVars, 1)));
375
376 // 2. Add R-squared and standard error for y estimate.
379
380 // 3. Add F statistic and degrees of freedom
383
384 // 4. Add regression sum of squares and residual sum of squares
387
388 rOutput.push(0, 4);
389 rOutput.newLine();
390}
391
393{
394 rOutput.newLine();
395 rOutput.writeString(ScResId(STR_LABEL_REGRESSION_STATISTICS));
396 rOutput.newLine();
397
398 const TranslateId aMeasureNames[] =
399 {
400 STR_LABEL_RSQUARED,
401 STRID_CALC_STD_ERROR,
402 STR_LABEL_XVARIABLES_COUNT,
403 STR_OBSERVATIONS_LABEL,
405 };
406
407 OUString aMeasureFormulas[] =
408 {
411 "=" + OUString::number(mnNumIndependentVars),
412 "=" + OUString::number(mnNumObservations),
413 OUString::Concat(
415 (mbCalcIntercept ? std::u16string_view(u" - 1)") : std::u16string_view(u")"))
416 };
417
420
421 for (size_t nIdx = 0; nIdx < SAL_N_ELEMENTS(aMeasureNames); ++nIdx)
422 {
423 rOutput.writeString(ScResId(aMeasureNames[nIdx]));
424 rOutput.nextColumn();
425 rTemplate.setTemplate(aMeasureFormulas[nIdx]);
426 rOutput.writeFormula(rTemplate.getTemplate());
427 rOutput.newLine();
428 }
429}
430
432{
433 rOutput.newLine();
434 rOutput.writeString(ScResId(STR_LABEL_ANOVA));
435 rOutput.newLine();
436
437 const size_t nColsInTable = 6;
438 const size_t nRowsInTable = 4;
439 OUString aTable[nRowsInTable][nColsInTable] =
440 {
441 {
442 "",
443 ScResId(STR_ANOVA_LABEL_DF),
444 ScResId(STR_ANOVA_LABEL_SS),
445 ScResId(STR_ANOVA_LABEL_MS),
446 ScResId(STR_ANOVA_LABEL_F),
447 ScResId(STR_ANOVA_LABEL_SIGNIFICANCE_F)
448 },
449 {
450 ScResId(STR_REGRESSION),
456 },
457 {
458 ScResId(STR_LABEL_RESIDUAL),
462 "",
463 ""
464 },
465 {
466 ScResId(STR_ANOVA_LABEL_TOTAL),
469 "",
470 "",
471 ""
472 }
473 };
474
476
477 // Cell getter lambda
478 std::function<CellValueGetter> aCellGetterFunc = [&aTable](size_t nRowIdx, size_t nColIdx) -> const OUString&
479 {
480 return aTable[nRowIdx][nColIdx];
481 };
482
483 // Cell writer lambda
484 std::function<CellWriter> aCellWriterFunc = [&rOutput, &rTemplate]
485 (const OUString& rContent, size_t /*nRowIdx*/, size_t /*nColIdx*/)
486 {
487 if (!rContent.isEmpty())
488 {
489 if (rContent.startsWith("="))
490 {
491 rTemplate.setTemplate(rContent);
492 rOutput.writeFormula(rTemplate.getTemplate());
493 }
494 else
495 rOutput.writeString(rContent);
496 }
497 };
498
499 WriteTable(aCellGetterFunc, nRowsInTable, nColsInTable, rOutput, aCellWriterFunc);
500
501 // User given confidence level
502 rOutput.newLine();
503 rOutput.writeString(ScResId(STR_LABEL_CONFIDENCE_LEVEL));
504 rOutput.nextColumn();
505 rOutput.writeValue(mxConfidenceLevelField->get_value() / 100.0);
507 rOutput.newLine();
508}
509
510// Write slopes, intercept, their standard errors, t-statistics, p-value, confidence intervals
512 bool bTakeLogX)
513{
514 rOutput.newLine();
515 ScAddress aEnd( rOutput.current(0, 1 + mnNumIndependentVars));
517 aEnd.IncCol();
519 aEnd.IncCol();
521
522 // Coefficients & Std.Errors ranges (column vectors) in this table (yet to populate).
523 rTemplate.autoReplaceRange("%COEFFICIENTS_RANGE%",
524 ScRange(rOutput.current(1, 1),
525 rOutput.current(1, 1 + mnNumIndependentVars)));
526 rTemplate.autoReplaceRange("%SLOPES_RANGE%", // Excludes the intercept
527 ScRange(rOutput.current(1, 2),
528 rOutput.current(1, 1 + mnNumIndependentVars)));
530 rTemplate.autoReplaceRange("%SERRORSX_RANGE%",
531 ScRange(rOutput.current(2, 1),
532 rOutput.current(2, 1 + mnNumIndependentVars)));
533 // t-Statistics range in this table (yet to populate)
534 rTemplate.autoReplaceRange("%TSTAT_RANGE%",
535 ScRange(rOutput.current(3, 1),
536 rOutput.current(3, 1 + mnNumIndependentVars)));
537
538 const size_t nColsInTable = 7;
539 const size_t nRowsInTable = 2;
540 OUString aTable[nRowsInTable][nColsInTable] =
541 {
542 {
543 "",
544 ScResId(STR_LABEL_COEFFICIENTS),
545 ScResId(STRID_CALC_STD_ERROR),
546 ScResId(STR_LABEL_TSTATISTIC),
547 ScResId(STR_P_VALUE_LABEL),
548
549 "=CONCAT(\"" + ScResId(STR_LABEL_LOWER) +
550 " \" ; INT(%CONFIDENCE_LEVEL_ADDR% * 100) ; \"%\")",
551
552 "=CONCAT(\"" + ScResId(STR_LABEL_UPPER) +
553 " \" ; INT(%CONFIDENCE_LEVEL_ADDR% * 100) ; \"%\")",
554 },
555
556 // Following are matrix formulas of size numcols = 1, numrows = (mnNumIndependentVars + 1)
557 {
558 "",
559 // This puts the coefficients in the reverse order compared to that in LINEST output.
560 "=INDEX(%COEFFICIENTS_REV_RANGE%; 1 ; ROW(" + aCoeffAddr + ")+1 - ROW())",
561 // This puts the standard errors in the reverse order compared to that in LINEST output.
562 "=INDEX(%SERRORSX_REV_RANGE%; 1 ; ROW(" + aStErrAddr + ")+1 - ROW())",
563 // t-Statistic
564 "=%COEFFICIENTS_RANGE% / %SERRORSX_RANGE%",
565 // p-Value
566 "=TDIST(ABS(%TSTAT_RANGE%) ; %DoFRESID_ADDR% ; 2 )",
567 // Lower limit of confidence interval
568 "=%COEFFICIENTS_RANGE% - %SERRORSX_RANGE% * "
570 // Upper limit of confidence interval
571 "=%COEFFICIENTS_RANGE% + %SERRORSX_RANGE% * "
573 }
574 };
575
576 // Cell getter lambda
577 std::function<CellValueGetter> aCellGetterFunc = [&aTable](size_t nRowIdx, size_t nColIdx) -> const OUString&
578 {
579 return aTable[nRowIdx][nColIdx];
580 };
581
582 // Cell writer lambda
583 size_t nNumIndependentVars = mnNumIndependentVars;
584 std::function<CellWriter> aCellWriterFunc = [&rOutput, &rTemplate, nNumIndependentVars]
585 (const OUString& rContent, size_t nRowIdx, size_t /*nColIdx*/)
586 {
587 if (!rContent.isEmpty())
588 {
589 if (rContent.startsWith("="))
590 {
591 rTemplate.setTemplate(rContent);
592 if (nRowIdx == 0)
593 rOutput.writeFormula(rTemplate.getTemplate());
594 else
595 rOutput.writeMatrixFormula(rTemplate.getTemplate(), 1, 1 + nNumIndependentVars);
596 }
597 else
598 rOutput.writeString(rContent);
599 }
600 };
601
602 WriteTable(aCellGetterFunc, nRowsInTable, nColsInTable, rOutput, aCellWriterFunc);
603
604 // Go back to the second row and first column of the table to
605 // fill the names of variables + intercept
606 rOutput.push(0, -1);
607
608 for (size_t nXvarIdx = 0; nXvarIdx <= mnNumIndependentVars; ++nXvarIdx)
609 {
610 rOutput.writeFormula(GetXVariableNameFormula(nXvarIdx, bTakeLogX));
611 rOutput.newLine();
612 }
613
614}
615
616// Re-write all observations in group-by column mode with predictions and residuals
618 size_t nRegressionIndex)
619{
620 bool bGroupedByColumn = mGroupedBy == BY_COLUMN;
621 rOutput.newLine();
622 rOutput.push();
623
624 // Range of X variables with rows as observations and columns as variables.
625 ScRange aDataMatrixRange(rOutput.current(0, 1), rOutput.current(mnNumIndependentVars - 1, mnNumObservations));
627
628 // Write X variable names
629 for (size_t nXvarIdx = 1; nXvarIdx <= mnNumIndependentVars; ++nXvarIdx)
630 {
631 // Here we write the X variables without any transformation(LN)
632 rOutput.writeFormula(GetXVariableNameFormula(nXvarIdx, false));
633 rOutput.nextColumn();
634 }
635 rOutput.reset();
636
637 // Write the X data matrix
638 rOutput.nextRow();
639 OUString aDataMatrixFormula = bGroupedByColumn ? OUString("=%VARIABLE1_RANGE%") : OUString("=TRANSPOSE(%VARIABLE1_RANGE%)");
642
643 // Write predicted values
644 rOutput.push(mnNumIndependentVars, -1);
645 rOutput.writeString(ScResId(STR_LABEL_PREDICTEDY));
646 rOutput.nextRow();
647 rTemplate.setTemplate(constRegressionFormula[nRegressionIndex]);
648 rOutput.writeMatrixFormula(rTemplate.getTemplate(), 1, mnNumObservations);
649 rTemplate.autoReplaceRange("%PREDICTEDY_RANGE%", ScRange(rOutput.current(), rOutput.current(0, mnNumObservations - 1)));
650
651 // Write actual Y
652 rOutput.push(1, -1);
654 rOutput.nextRow();
655 OUString aYVectorFormula = bGroupedByColumn ? OUString("=%VARIABLE2_RANGE%") : OUString("=TRANSPOSE(%VARIABLE2_RANGE%)");
656 rTemplate.setTemplate(aYVectorFormula);
657 rOutput.writeMatrixFormula(rTemplate.getTemplate(), 1, mnNumObservations);
658 rTemplate.autoReplaceRange("%ACTUALY_RANGE%", ScRange(rOutput.current(), rOutput.current(0, mnNumObservations - 1)));
659
660 // Write residual
661 rOutput.push(1, -1);
662 rOutput.writeString(ScResId(STR_LABEL_RESIDUAL));
663 rOutput.nextRow();
664 rTemplate.setTemplate("=%ACTUALY_RANGE% - %PREDICTEDY_RANGE%");
665 rOutput.writeMatrixFormula(rTemplate.getTemplate(), 1, mnNumObservations);
666}
667
668// Generic table writer
669void ScRegressionDialog::WriteTable(const std::function<CellValueGetter>& rCellGetter,
670 size_t nRowsInTable, size_t nColsInTable,
672 const std::function<CellWriter>& rFunc)
673{
674 for (size_t nRowIdx = 0; nRowIdx < nRowsInTable; ++nRowIdx)
675 {
676 for (size_t nColIdx = 0; nColIdx < nColsInTable; ++nColIdx)
677 {
678 rFunc(rCellGetter(nRowIdx, nColIdx), nRowIdx, nColIdx);
679 rOutput.nextColumn();
680 }
681 rOutput.newLine();
682 }
683}
684
686{
688}
689
691{
693}
694
695/* vim:set shiftwidth=4 softtabstop=4 expandtab: */
static size_t lcl_GetNumRowsColsInRange(const ScRange &rRange, bool bRows)
ScRefFlags
void writeMatrixFormula(const OUString &aFormula, SCCOL nCols=1, SCROW nRows=1)
void writeFormula(const OUString &aFormula)
void writeBoldString(const OUString &aString)
void writeString(const OUString &aString)
ScAddress current(SCCOL aRelativeCol=0, SCROW aRelativeRow=0, SCTAB aRelativeTab=0)
void push(SCCOL aRelativeCol=0, SCROW aRelativeRow=0, SCTAB aRelativeTab=0)
void setTemplate(const OUString &aTemplate)
void autoReplaceRange(const OUString &aVariable, const ScRange &rRange)
SCTAB Tab() const
void SetCol(SCCOL nColP)
SC_DLLPUBLIC void Format(OStringBuffer &r, ScRefFlags nFlags, const ScDocument *pDocument=nullptr, const Details &rDetails=detailsOOOa1) const
void IncCol(SCCOL nDelta=1)
bool IsValid() const
SCROW Row() const
void IncRow(SCROW nDelta=1)
SCCOL Col() const
Definition: documen3.cxx:492
void PutInOrder()
bool IsValid() const
bool DoClose(sal_uInt16 nId)
Definition: anyrefdg.cxx:714
virtual void Close() override
virtual TranslateId GetUndoNameId() override
void WritePredictionsWithResiduals(AddressWalkerWriter &rOutput, FormulaTemplate &rTemplate, size_t nRegressionIndex)
ScRegressionDialog(SfxBindings *pB, SfxChildWindow *pCW, weld::Window *pParent, ScViewData &rViewData)
void WriteRegressionEstimatesWithCI(AddressWalkerWriter &rOutput, FormulaTemplate &rTemplate, bool bTakeLogX)
std::unique_ptr< weld::SpinButton > mxConfidenceLevelField
size_t GetRegressionTypeIndex() const
virtual ~ScRegressionDialog() override
OUString GetXVariableNameFormula(size_t nIndex, bool bWithLog)
static void WriteTable(const std::function< CellValueGetter > &rCellGetter, size_t nRowsInTable, size_t nColsInTable, AddressWalkerWriter &rOutput, const std::function< CellWriter > &rFunc)
virtual bool InputRangesValid() override
std::unique_ptr< weld::CheckButton > mxCalcResidualsCheckBox
ScRange GetDataRange(const ScRange &rRange)
std::unique_ptr< weld::Label > mxErrorMessage
void WriteRawRegressionResults(AddressWalkerWriter &rOutput, FormulaTemplate &rTemplate, size_t nRegressionIndex)
OUString GetVariableNameFormula(bool bXVar, size_t nIndex, bool bWithLog)
std::unique_ptr< weld::CheckButton > mxNoInterceptCheckBox
virtual ScRange ApplyOutput(ScDocShell *pDocShell) override
OUString GetYVariableNameFormula(bool bWithLog)
std::unique_ptr< weld::CheckButton > mxWithLabelsCheckBox
static Grammar mergeToGrammar(const Grammar eGrammar, const AddressConvention eConv)
LINEAR
float u
sal_Int32 nIndex
#define SAL_N_ELEMENTS(arr)
OUString ScResId(TranslateId aId)
Definition: scdll.cxx:90