tech.tablesaw.plotly.api.TukeyMeanDifferencePlot Maven / Gradle / Ivy
package tech.tablesaw.plotly.api;
import com.google.common.base.Preconditions;
import java.util.Arrays;
import org.apache.commons.math3.stat.StatUtils;
import tech.tablesaw.api.NumericColumn;
import tech.tablesaw.api.Table;
import tech.tablesaw.plotly.components.Axis;
import tech.tablesaw.plotly.components.Figure;
import tech.tablesaw.plotly.components.Layout;
import tech.tablesaw.plotly.traces.ScatterTrace;
/**
* A Tukey Mean-Difference Plot (AKA a Bland-Altman plot) is a kind of scatter plot used frequently
* in medicine, biology and other fields, is used to visualize the differences between two
* quantitative measurements. In particular, it is often used to evaluate whether two tests produce
* 'the same' result.
*
* For two numeric arrays, a and b, the plot shows the mean for each pair of observations (a + b)
* / 2, as well as the differences between them: a - b.
*
*
For more information: https://en.wikipedia.org/wiki/Bland%E2%80%93Altman_plot
*/
public class TukeyMeanDifferencePlot {
/**
* Returns a figure containing a Tukey Mean-Difference Plot describing the differences between the
* data in two columns of interest
*
* @param title A title for the plot
* @param measure The measure being compared on the plot (e.g "inches" or "height in inches"
* @param table The table containing the columns of interest
* @param columnName1 The name of the first numeric column containing the data to plot
* @param columnName2 The name of the second numeric column containing the data to plot
* @return A quantile plot
*/
public static Figure create(
String title, String measure, Table table, String columnName1, String columnName2) {
NumericColumn xCol = table.nCol(columnName1);
NumericColumn yCol = table.nCol(columnName2);
return create(title, measure, xCol.asDoubleArray(), yCol.asDoubleArray());
}
/**
* Returns a figure containing a QQ Plot describing the differences between the distribution of
* values in the columns of interest
*
* @param title A title for the plot
* @param measure The measure being compared on the plot (e.g "inches" or "height in inches"
* @param xData The data to plot on the x Axis
* @param yData The data to plot on the y Axis
* @return A quantile plot
*/
public static Figure create(String title, String measure, double[] xData, double[] yData) {
Preconditions.checkArgument(xData.length != 0, "x Data array is empty");
Preconditions.checkArgument(yData.length != 0, "x Data array is empty");
if (xData.length != yData.length) {
double[] interpolatedData;
if (xData.length < yData.length) {
interpolatedData = interpolate(yData, xData.length);
yData = interpolatedData;
} else {
interpolatedData = interpolate(xData, yData.length);
xData = interpolatedData;
}
}
Arrays.sort(xData);
Arrays.sort(yData);
double[] averagePoints = new double[xData.length];
double[] differencePoints = new double[xData.length];
for (int i = 0; i < xData.length; i++) {
averagePoints[i] = (xData[i] + yData[i]) / 2.0;
differencePoints[i] = (xData[i] - yData[i]);
}
double xMin = StatUtils.min(xData);
double xMax = StatUtils.max(xData);
double[] zeroLineX = {xMin, xMax};
double[] zeroLineY = {0, 0};
// Draw the line indicating equal distributions (this is zero in this plot)
ScatterTrace trace1 =
ScatterTrace.builder(zeroLineX, zeroLineY)
.mode(ScatterTrace.Mode.LINE)
.name("y = x")
.build();
// Draw the actual data points
ScatterTrace trace2 =
ScatterTrace.builder(averagePoints, differencePoints).name("mean x difference").build();
Layout layout =
Layout.builder()
.title(title)
.xAxis(Axis.builder().title("mean (" + measure + ")").build())
.yAxis(Axis.builder().title("difference (" + measure + ")").build())
.height(700)
.width(900)
.build();
return new Figure(layout, trace1, trace2);
}
/**
* Returns a double array, whose values are quantiles from the given source, based on the given
* size. The idea is to produce size elements that represent the quantiles of source array
*
* @param source The array to whose quantiles are calculated
* @param size The size of the array to return
*/
private static double[] interpolate(double[] source, int size) {
double[] interpolatedData = new double[size];
for (int i = 0; i < size; i++) {
double value = ((i + .5) / (double) size) * 100;
interpolatedData[i] = StatUtils.percentile(source, value);
}
return interpolatedData;
}
}