Let me draw your attention to this very pedagogical GAUSS blog on the Kalman filter. It links very well the theory and the code.
Please find below the code for plotting the results:
/**** Plotting Section ***/
// Layout
plotOpenWindow();
// Set plot canvas to be 640 by 480 pixels
plotCanvasSize("px", 640 | 480);
// Plot control structure
struct plotControl myPlot;
myPlot = plotGetDefaults("XY");
// Turn grid off
plotSetGrid(&myPlot, "off");
// Font settings
font_name = "Calibri";
font_color = "#3e3e3e";
title_size = 18;
label_size = 14;
legend_size = 12;
/*** Predicted state ***/
// Legend
plotSetLegend(&myPlot, "Predicted"$|"Observed");
plotSetLegendFont(&myPlot, font_name, legend_size, font_color);
// Change color of axes numbers
plotSetTicLabelFont(&myPlot, font_name, legend_size, font_color);
// Title
plotSetTitle(&myPlot, "Kalman Filter Predicted State: AR(2) Model", font_name, title_size, font_color);
// Set up to add plot of filtered data
// Turn line symbol off
plotSetLineSymbol(&myPlot, -1); x = seqa(1, 1, cols(y));
// XY plot of filtered data
plotXY(myPlot, x, rslt.predicted_state[2, 2:cols(y)+1]');
/*** Observed Data ***/
// Plot scatter of observed data
plotAddScatter(x, y');
// Save the plot
// Save the graph as a 640 wide by 480 tall PNG file
plotSave("kalman.png", 640 | 480, "px");
It produces the following result:
