diff options
Diffstat (limited to 'stats/statsview.cpp')
| -rw-r--r-- | stats/statsview.cpp | 60 |
1 files changed, 28 insertions, 32 deletions
diff --git a/stats/statsview.cpp b/stats/statsview.cpp index 405677e56..63e9183b6 100644 --- a/stats/statsview.cpp +++ b/stats/statsview.cpp @@ -723,10 +723,10 @@ void StatsView::QuartileMarker::updatePosition() x + quartileMarkerSize / 2.0, y); } -StatsView::RegressionLine::RegressionLine(double a, double b, double width, QBrush brush, QGraphicsScene *scene, StatsAxis *xAxis, StatsAxis *yAxis) : +StatsView::RegressionLine::RegressionLine(const struct regression_data reg, QBrush brush, QGraphicsScene *scene, StatsAxis *xAxis, StatsAxis *yAxis) : item(createItemPtr<QGraphicsPolygonItem>(scene)), xAxis(xAxis), yAxis(yAxis), - a(a), b(b), width(width) + reg(reg) { item->setZValue(ZValues::chartFeatures); item->setPen(Qt::NoPen); @@ -740,12 +740,14 @@ void StatsView::RegressionLine::updatePosition() auto [minX, maxX] = xAxis->minMax(); auto [minY, maxY] = yAxis->minMax(); + // Draw the confidence interval according to http://www2.stat.duke.edu/~tjl13/s101/slides/unit6lec3H.pdf p.5 with t*=2 for 95% confidence QPolygonF poly; - poly << QPointF(xAxis->toScreen(minX), yAxis->toScreen(a * minX + b + width)) - << QPointF(xAxis->toScreen(maxX), yAxis->toScreen(a * maxX + b + width)) - << QPointF(xAxis->toScreen(maxX), yAxis->toScreen(a * maxX + b - width)) - << QPointF(xAxis->toScreen(minX), yAxis->toScreen(a * minX + b - width)) - << QPointF(xAxis->toScreen(minX), yAxis->toScreen(a * minX + b + width)); + for (double x = minX; x <= maxX + 1; x += (maxX - minX) / 100) + poly << QPointF(xAxis->toScreen(x), + yAxis->toScreen(reg.a * x + reg.b + 2.0 * sqrt(reg.res2 / (reg.n - 2) * (1.0 / reg.n + (x - reg.xavg) * (x - reg.xavg) / (reg.n - 1) * (reg.n -2) / reg.sx2)))); + for (double x = maxX; x >= minX - 1; x -= (maxX - minX) / 100) + poly << QPointF(xAxis->toScreen(x), + yAxis->toScreen(reg.a * x + reg.b - 2.0 * sqrt(reg.res2 / (reg.n - 2) * (1.0 / reg.n + (x - reg.xavg) * (x - reg.xavg) / (reg.n - 1) * (reg.n -2) / reg.sx2)))); QRectF box(QPoint(xAxis->toScreen(minX), yAxis->toScreen(minY)), QPoint(xAxis->toScreen(maxX), yAxis->toScreen(maxY))); item->setPolygon(poly.intersected(box)); @@ -780,15 +782,15 @@ void StatsView::addHistogramMarker(double pos, const QPen &pen, bool isHorizonta histogramMarkers.emplace_back(pos, isHorizontal, pen, &scene, xAxis, yAxis); } -void StatsView::addLinearRegression(double a, double b, double res2, double r2, double minX, double maxX, double minY, double maxY, StatsAxis *xAxis, StatsAxis *yAxis) +void StatsView::addLinearRegression(const struct regression_data reg, StatsAxis *xAxis, StatsAxis *yAxis) { QColor red = QColor(Qt::red); - red.setAlphaF(r2); + red.setAlphaF(reg.r2); QPen pen(red); QBrush brush(red); brush.setStyle(Qt::SolidPattern); - regressionLines.emplace_back(a, b, sqrt(res2), brush, &scene, xAxis, yAxis); + regressionLines.emplace_back(reg, brush, &scene, xAxis, yAxis); } // Yikes, we get our data in different kinds of (bin, value) pairs. @@ -1027,11 +1029,6 @@ static bool is_linear_regression(int sample_size, double cov, double sx2, double return true; // can't happen, as we tested for sample_size above. } -struct regression_data { - double a,b; - double res2, r2; -}; - // Returns the coefficients a,b of the line y = ax + b // as well as the variance of the residuals (averaged residual squared) as res2 // and r^2 = 1.0 - variance of data / res2 which is the fraction of the variance of @@ -1040,17 +1037,18 @@ struct regression_data { static struct regression_data linear_regression(const std::vector<StatsScatterItem> &v) { - if (v.size() < 2) - return { .a = NaN, .b = NaN, .res2 = 0.0, .r2 = 0.0}; - + struct regression_data ret = { .a = NaN, .b = NaN, .res2 = 0.0, .r2 = 0.0, .sx2 = 0.0, .xavg = 0.0}; + ret.n = v.size(); + if (ret.n < 2) + return ret; // First, calculate the x and y average double avg_x = 0.0, avg_y = 0.0; for (auto [x, y, d]: v) { avg_x += x; avg_y += y; } - avg_x /= (double)v.size(); - avg_y /= (double)v.size(); + avg_x /= ret.n; + avg_y /= ret.n; double cov = 0.0, sx2 = 0.0, sy2 = 0.0; for (auto [x, y, d]: v) { @@ -1062,15 +1060,16 @@ static struct regression_data linear_regression(const std::vector<StatsScatterIt bool is_linear = is_linear_regression((int)v.size(), cov, sx2, sy2); if (fabs(sx2) < 1e-10 || !is_linear) // If t is not statistically significant, do not plot the regression line. - return { .a = NaN, .b = NaN, .res2 = 0.0, .r2 = 0.0}; - double a = cov / sx2; - double b = avg_y - a * avg_x; + return ret; + ret.xavg = avg_x; + ret.sx2 = sx2; + ret.a = cov / sx2; + ret.b = avg_y - ret.a * avg_x; - double res2 = 0.0; for (auto [x, y, d]: v) - res2 += (y - a * x - b) * (y - a * x - b); - double r2 = sy2 > 0.0 ? 1.0 - res2 / sy2 : 1.0; - return { .a = a, .b = b, .res2 = res2 / v.size(), .r2 = r2 }; + ret.res2 += (y - ret.a * x - ret.b) * (y - ret.a * x - ret.b); + ret.r2 = sy2 > 0.0 ? 1.0 - ret.res2 / sy2 : 1.0; + return ret; } void StatsView::plotScatter(const std::vector<dive *> &dives, const StatsVariable *categoryVariable, const StatsVariable *valueVariable) @@ -1101,9 +1100,6 @@ void StatsView::plotScatter(const std::vector<dive *> &dives, const StatsVariabl // y = ax + b struct regression_data reg = linear_regression(points); - if (!std::isnan(reg.a)) { - auto [minx, maxx] = axisX->minMax(); - auto [miny, maxy] = axisY->minMax(); - addLinearRegression(reg.a, reg.b, reg.res2, reg.r2, minx, maxx, miny, maxy, xAxis, yAxis); - } + if (!std::isnan(reg.a)) + addLinearRegression(reg, xAxis, yAxis); } |