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Tom Marshall
public
Commits
4f549eb3
Commit
4f549eb3
authored
Jun 04, 2019
by
Tom
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Update rm_raincloud.m
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8049eebd
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raincloud_plots/rm_raincloud.m
raincloud_plots/rm_raincloud.m
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raincloud_plots/rm_raincloud.m
View file @
4f549eb3
%% Raincloud lineplot for repeated-measures data
% it might be that 'patch' is a much better plotting function to plot the
% densities than 'area'; it seems to handle over-plotting of different
% areas with different zero lines very well. This would avoid the horrible
% gymnastics that the repeated measures raincloudplot currently has to deal
% with.
% This is a work-in-progress example inspired by this post on Matlab
% answers https://uk.mathworks.com/matlabcentral/answers/1387-area-plot-with-gradient
% Use like: h = rm_raincloud(data, colours)
% Use like: h = rm_raincloud(data, colours, plot_top_to_bottom, density_type, bandwidth)
% Where 'data' is an M x N cell array of N data series and M measurements
% And 'colours' is an N x 3 array defining the colour to plot each data series
% h is a cell array containing handles of the various figure parts
% plot_top_to_bottom: Default plots left-to-right, set to 1 to rotate.
% density_type: 'ks' (default) or 'RASH'. 'ks' uses matlab's inbuilt 'ksdensity' to
% determine the shape of the rainclouds. 'RASH' will use the 'rst_RASH'
% method from Cyril Pernet's Robust Stats toolbox, if that function is on
% your matlab path.
% bandwidth: If density_type == 'ks', determines bandwidth of density estimate
% h is a cell array containing handles of the various figure parts:
% h.p{i,j} is the handle to the density plot from data{i,j}
% h.s{i,j} is the handle to the 'raindrops' (individual datapoints) from data{i,j}
% h.m(i,j) is the handle to the single, large dot that represents mean(data{i,j})
% h.l(i,j) is the handle for the line connecting h.m(i,j) and h.m(i+1,j)
%% TO-DO:
% Patch can create colour gradients using the 'interp' option to 'FaceColor'. Allow this?
...
...
@@ -51,6 +51,9 @@ for i = 1:nper
case
'ks'
[
ks
{
i
,
j
},
x
{
i
,
j
}]
=
ksdensity
(
data
{
i
,
j
},
'NumPoints'
,
nbins
(
i
,
j
),
'bandwidth'
,
bandwidth
);
case
'rash'
% check for rst_RASH function (from Robust stats toolbox) in path, fail if not found
assert
(
exist
(
'rst_RASH'
,
'file'
)
==
2
,
'Could not compute density using RASH method. Do you have the Robust Stats toolbox on your path?'
);
[
x
{
i
,
j
},
ks
{
i
,
j
}]
=
rst_RASH
(
data
{
i
,
j
});
% override default 'nbins' as rst_RASH determines number of bins
nbins
(
i
,
j
)
=
size
(
ks
{
i
,
j
},
2
);
...
...
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