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Commit 741fef54 authored by dandreea's avatar dandreea
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Added spm and made options and subjects general.

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Toolboxes/PlottingFunctions/README.md 0 → 100755
View file @ 741fef54
# notBoxPlot
<p align="center">
<img src="https://raw.githubusercontent.com/raacampbell/notBoxPlot/gh-pages/images/nbp_example.png" height=550px/>
</p>
notBoxPlot is a MATLAB data visualisation function.
Whilst box plots have their place, it's sometimes nicer to see all the data, rather than hiding them with summary statistics such as the inter-quartile range. This function (with a tongue in cheek name) addresses this problem. The use of the mean instead of the median and the SEM and SD instead of quartiles and whiskers are deliberate.
Jittered raw data are plotted for each group. Also shown are the mean, and 95% confidence intervals for the mean. This plotting style is designed to be used alongside parametric tests such as ANOVA and the t-test. Comparing the jittered data to the error bars provides a visual indication of whether the normality assumptions of the statistical tests are being violated. Furthermore, it allows one to eyeball the data to look for significant differences between means (non-overlapping confidence intervals indicate a significant difference at the chosen p-value, which here is 5%). Also see: http://jcb.rupress.org/cgi/content/abstract/177/1/7 Finally, 1 SD is also shown. Note that if data are not normally distributed then these statistics will be less meaningful.
The function has several examples and there are various visualization possibilities in addition to those shown in the above screenshot. For instance, the coloured areas can be replaced by lines.
Although it's worked well for situations I've needed it, I will be happy to modify the function if users come up against problems.
## Features
- Directly plot LinearModel objects from `fitlm` [NEW]
- Easily mix a variety of plot styles on one figure
- Easy post-hoc modification of plot parameters via returned function handles
- Statistics (mean, SD, etc) optionally returned
- Optional plotting of median in addition to mean
- Option to plot either a 95% confidence interval for the mean or a 95% t-interval
## FAQ
Q: "How can I modify the plot to look like..."
<br />
A: Most modifications can be done using the function handles. See the function help and the example function, NBP_example
## Included functions
- notBoxPlot.m - generates plots as shown in screenshot
- NBP.SEM_calc.m - calculate standard error of the mean. Provided as a separate function file so that it can be used for other purposes.
- NBP.tInterval_calc.m - calculate a t-interval. For small sample sizes, the t-interval is larger than the SEM. Provided as a separate function file so that it can be used for other purposes.
- NBP.example - makes a nice example plot
## Installation
Add the ``code`` directory to your MATLAB path. Some operations (such as t-interval calculation) depend on the Stats Toolbox.
## Changelog
**v1.31 (28-11-17)**
* Bugfix to SEM and t-interval calc functions that caused errors to be pooled when fed a matrix rather than a vector
* Jitter is no "violin-like" so it scales with point density in Y. Looks neater.
**v1.3 (11-03-17)**
* Remove legacy calls
* Allow passing of a table, which automatically labels the axes
* Pass a LinearModel, which automatically labels the axes and uses the model errors
* Examples are now in separate files and doc text is neater
* User can now optionally do `notBoxPlot(y,'jitter',0.5)` instead of `notBoxPlot(y,[],'jitter',0.5)`
**v1.2 (28-08-16)**
* Add median to plots
* Select SEM or t-interval from command line
* Return stats as second output
* Move to parameter/value pairs by default and warn user if they aren't doing this.
* Add unit tests.
Toolboxes/PlottingFunctions/Violin.m 0 → 100644
View file @ 741fef54
classdef Violin < handle
% Violin creates violin plots for some data
% A violin plot is an easy to read substitute for a box plot
% that replaces the box shape with a kernel density estimate of
% the data, and optionally overlays the data points itself.
%
% Additional constructor parameters include the width of the
% plot, the bandwidth of the kernel density estimation, and the
% X-axis position of the violin plot.
%
% Use <a href="matlab:help('violinplot')">violinplot</a> for a
% <a href="matlab:help('boxplot')">boxplot</a>-like wrapper for
% interactive plotting.
%
% See for more information on Violin Plots:
% J. L. Hintze and R. D. Nelson, "Violin plots: a box
% plot-density trace synergism," The American Statistician, vol.
% 52, no. 2, pp. 181-184, 1998.
%
% Violin Properties:
% ViolinColor - Fill color of the violin area and data points.
% Defaults to the next default color cycle.
% ViolinAlpha - Transparency of the ciolin area and data points.
% Defaults to 0.3.
% EdgeColor - Color of the violin area outline.
% Defaults to [0.5 0.5 0.5]
% BoxColor - Color of the box, whiskers, and the outlines of
% the median point and the notch indicators.
% Defaults to [0.5 0.5 0.5]
% MedianColor - Fill color of the median and notch indicators.
% Defaults to [1 1 1]
% ShowData - Whether to show data points.
% Defaults to true
% ShowNotches - Whether to show notch indicators.
% Defaults to false
% ShowMean - Whether to show mean indicator.
% Defaults to false
%
% Violin Children:
% ScatterPlot - <a href="matlab:help('scatter')">scatter</a> plot of the data points
% ViolinPlot - <a href="matlab:help('fill')">fill</a> plot of the kernel density estimate
% BoxPlot - <a href="matlab:help('fill')">fill</a> plot of the box between the quartiles
% WhiskerPlot - line <a href="matlab:help('plot')">plot</a> between the whisker ends
% MedianPlot - <a href="matlab:help('scatter')">scatter</a> plot of the median (one point)
% NotchPlots - <a href="matlab:help('scatter')">scatter</a> plots for the notch indicators
% MeanPlot - line <a href="matlab:help('plot')">plot</a> at mean value
% Copyright (c) 2016, Bastian Bechtold
% This code is released under the terms of the BSD 3-clause license
properties
ScatterPlot % scatter plot of the data points
ViolinPlot % fill plot of the kernel density estimate
BoxPlot % fill plot of the box between the quartiles
WhiskerPlot % line plot between the whisker ends
MedianPlot % scatter plot of the median (one point)
NotchPlots % scatter plots for the notch indicators
MeanPlot % line plot of the mean (horizontal line)
end
properties (Dependent=true)
ViolinColor % fill color of the violin area and data points
ViolinAlpha % transparency of the violin area and data points
EdgeColor % color of the violin area outline
BoxColor % color of box, whiskers, and median/notch edges
MedianColor % fill color of median and notches
ShowData % whether to show data points
ShowNotches % whether to show notch indicators
ShowMean % whether to show mean indicator
end
methods
function obj = Violin(data, pos, varargin)
%Violin plots a violin plot of some data at pos
% VIOLIN(DATA, POS) plots a violin at x-position POS for
% a vector of DATA points.
%
% VIOLIN(..., 'PARAM1', val1, 'PARAM2', val2, ...)
% specifies optional name/value pairs:
% 'Width' Width of the violin in axis space.
% Defaults to 0.3
% 'Bandwidth' Bandwidth of the kernel density
% estimate. Should be between 10% and
% 40% of the data range.
% 'ViolinColor' Fill color of the violin area and
% data points. Defaults to the next
% default color cycle.
% 'ViolinAlpha' Transparency of the violin area and
% data points. Defaults to 0.3.
% 'EdgeColor' Color of the violin area outline.
% Defaults to [0.5 0.5 0.5]
% 'BoxColor' Color of the box, whiskers, and the
% outlines of the median point and the
% notch indicators. Defaults to
% [0.5 0.5 0.5]
% 'MedianColor' Fill color of the median and notch
% indicators. Defaults to [1 1 1]
% 'ShowData' Whether to show data points.
% Defaults to true
% 'ShowNotches' Whether to show notch indicators.
% Defaults to false
% 'ShowMean' Whether to show mean indicator.
% Defaults to false
args = obj.checkInputs(data, pos, varargin{:});
data = data(not(isnan(data)));
if numel(data) == 1
obj.MedianPlot = scatter(pos, data, 'filled');
obj.MedianColor = args.MedianColor;
obj.MedianPlot.MarkerEdgeColor = args.EdgeColor;
return
end
hold('on');
% calculate kernel density estimation for the violin
[density, value] = ksdensity(data, 'bandwidth', args.Bandwidth);
density = density(value >= min(data) & value <= max(data));
value = value(value >= min(data) & value <= max(data));
value(1) = min(data);
value(end) = max(data);
% all data is identical
if min(data) == max(data)
density = 1;
end
if isempty(args.Width)
width = 0.3/max(density);
else
width = args.Width/max(density);
end
% plot the data points within the violin area
if length(density) > 1
jitterstrength = interp1(value, density*width, data);
else % all data is identical:
jitterstrength = density*width;
end
jitter = 2*(rand(size(data))-0.5);
obj.ScatterPlot = ...
scatter(pos + jitter.*jitterstrength, data, 'filled');
% plot the data mean
meanValue = mean(value);
if length(density) > 1
meanDensity = interp1(value, density, meanValue);
else % all data is identical:
meanDensity = density;
end
obj.MeanPlot = plot([pos-meanDensity*width pos+meanDensity*width], ...
[meanValue meanValue]);
obj.MeanPlot.LineWidth = 0.75;
% plot the violin
obj.ViolinPlot = ... % plot color will be overwritten later
fill([pos+density*width pos-density(end:-1:1)*width], ...
[value value(end:-1:1)], [1 1 1]);
% plot the mini-boxplot within the violin
quartiles = quantile(data, [0.25, 0.5, 0.75]);
obj.BoxPlot = ... % plot color will be overwritten later
fill([pos-0.01 pos+0.01 pos+0.01 pos-0.01], ...
[quartiles(1) quartiles(1) quartiles(3) quartiles(3)], ...
[1 1 1]);
IQR = quartiles(3) - quartiles(1);
lowhisker = quartiles(1) - 1.5*IQR;
lowhisker = max(lowhisker, min(data(data > lowhisker)));
hiwhisker = quartiles(3) + 1.5*IQR;
hiwhisker = min(hiwhisker, max(data(data < hiwhisker)));
if ~isempty(lowhisker) && ~isempty(hiwhisker)
obj.WhiskerPlot = plot([pos pos], [lowhisker hiwhisker]);
end
obj.MedianPlot = scatter(pos, quartiles(2), [], [1 1 1], 'filled');
obj.NotchPlots = ...
scatter(pos, quartiles(2)-1.57*IQR/sqrt(length(data)), ...
[], [1 1 1], 'filled', '^');
obj.NotchPlots(2) = ...
scatter(pos, quartiles(2)+1.57*IQR/sqrt(length(data)), ...
[], [1 1 1], 'filled', 'v');
obj.EdgeColor = args.EdgeColor;
obj.BoxColor = args.BoxColor;
obj.MedianColor = args.MedianColor;
if not(isempty(args.ViolinColor))
obj.ViolinColor = args.ViolinColor;
else
obj.ViolinColor = obj.ScatterPlot.CData;
end
obj.ViolinAlpha = args.ViolinAlpha;
obj.ShowData = args.ShowData;
obj.ShowNotches = args.ShowNotches;
obj.ShowMean = args.ShowMean;
end
function set.EdgeColor(obj, color)
obj.ViolinPlot.EdgeColor = color;
end
function color = get.EdgeColor(obj)
color = obj.ViolinPlot.EdgeColor;
end
function set.MedianColor(obj, color)
obj.MedianPlot.MarkerFaceColor = color;
if not(isempty(obj.NotchPlots))
obj.NotchPlots(1).MarkerFaceColor = color;
obj.NotchPlots(2).MarkerFaceColor = color;
end
end
function color = get.MedianColor(obj)
color = obj.MedianPlot.MarkerFaceColor;
end
function set.BoxColor(obj, color)
obj.BoxPlot.FaceColor = color;
obj.BoxPlot.EdgeColor = color;
obj.WhiskerPlot.Color = color<