plotLineHist: when summary statistics are not enough.

Today I am going to show one of my function that I am using quite extensively in my work. It's plotLineHist and that's what it produces:

Well, what the heck is that? PlotLineHist take a cell array C, a matrix M, a function handle F, and several optional arguments. The cell array C is a nxm. You have to think of each row as being an experimental condition for one factor, and each column as the experimental condition for a second (optional) factor. The matrix M specifies the values for the row's factor ( λ in the figure). In the figure, each column's factor is represented by different colours.

PlotLineHist execute the function F (for example, @mean) for each cell of the cell array, plotting the resulting value and connecting the row's  (continuous lines in the figure). It also calculates the standard error for each cell (vertical line on each marker).

However, the novelty is that it also plot a frequency distribution itself for each condition, and aligns it vertically on each row's factor. The distribution's value corresponds now to the vertical axis of the  figure.

Each row distribution has a different colour. The first row's distribution is plotted on the left side, the others on the right (of course, having more than 3 row's condition will make the plot difficult to read, but it may still be useful in those cases).

If this sounds convoluted, I am sure you will get a better idea by a simple example.

Let's say that we are measuring the effect of a drug on cortisol level on a sample of 8 participants. We have just 1 factor, the horizontal one, which is drug dose: let's say 100mg, 200mg and 300mg. The cortisol level is found to be:

Since we have only one factor, we need to put all the data in the first row of the cell array feed into the plotLineHist function:

p{1,2}=[15 16 14 17 30 15 14 16];
p{1,3}=[20 23 15 20 21 19 23 23];
plotLineHist(p, drugDose,fun);

The blue line with the markers indicates the mean response for each condition (with standard error). But then we also have a nice plot on the distribution for each condition! This allows us to spot some clear outliers in the first two distributions.

Now, let's say that we have two factor. For example, sometime the drug is taken together with another substance, and sometime is not.The drug is still given in 3 different dosages, 100, 200 and 300mg.

%example 2
%without substance A
p{1,1}=[10 12 13 20 50 12 14 12];
p{1,2}=[15 16 14 17 30 15 14 16];
p{1,3}=[20 23 15 20 21 19 23 23];
%with substance A
p{2,1}=[12 12 12 21 60 13 11 15];
p{2,2}=[16 16 14 18 31 19 18 17];
p{2,3}=[22 23 17 20 21 20 23 23];
drugDose=[100 200 300];
fun=@nanmean;
plotLineHist(p, drugDose,fun)

You can see how the second row condition distribution is positioned on the right side, so that the two distributions can be easily compared. Note how must of the nitty-gritty details are automatically calculated by the functions, such as bin width, scaling factors, etc. However, you have the chance to change it manually by using the optional arguments.

For me, this has been a very useful plot to summarize different types of information at the same time, without having a lot of figures.

Try it out and let me know!

DOWNLOAD PLOTLINEHIST HERE

scaleSubplot, to organize your subplot nicely

Download scaleSubplot

In psychology we often deal with data from several subjects, each subjet having several datapoints across more than one condition. Showing the results of an experiment for individual subjects in order to get a meaningful insight understandment of the data could be quite tricky. A classic way of organizing the data consists of plotting each subject's result in a subplot, in order to have all the subject's data in a single figure. MATLAB function subplot is quite good for the task, even if more and more often I find myself using the tight_subplot function. Anyway, one problem is that the subplot axes does not have the same scale by default, and this decrease the redeability of the results.

MATLAB allows you to link different axes (namely different subplots) so that you can scale all of them at the same time (linkaxes).  However, once that all the subplots have the same scale, showing the axes label for each subplot seems a little bit redundant. It would be sufficient just to show the axes of the bottom and left most panels. I coded a function that does exactly that.

scaleSubplot(fig, varargin) allows you to scale at the same time all the subplots of a figure. You can specify a custom scale or let the code decide a nice unique scale for all the subplots. You can decide to scale at the same time the horizontal and vertical axes, or only one of them.  Let's look at some example.

The easiest way of using the function is: scaleSubplot(gcf). In this case you will scale both x and y axes, and the script will find the best xlimit and ylimit for all the subplots. The code will make sure that all the data will be visualized in the subplots.

Note how the redundant x/ylabels have been suppressed:

We can have more control on the image by specifying some optional arguments. For example, if we want to have the same scale only the x axis, we specify 'sameScale',{'x'}. Notice how the x labels on the intermediate subplots are suppressed, but all the y labels are still there, since they do not have the same scale. In the figure below we also specify a custom xlimit. If you decide to specify a custom limit, make sure that all the datapoints in all the subplot fall within you limits.

We can also specify only one of the boundaries for the axes limit, and leave the code decide for the other one. For example we can write scaleSubplot(gcf,'xlimit',[-Inf 40]) or scaleSubplot(gcf,'ylimit',[0 Inf]).

My function is expecially useful when we want to have a tight subplot. In this case, eliminating the intermediate axis can make the subplot much more readable. For example, I generate the image on the left side with tight_subplot(2,4,0.05), where 0.05 is the gap between subplots. However, with all the axes labels, the image is a mess. After a simple scaleSubplot(gcf) the figure looks much better.

Hope you enjoy it.

Download scaleSubplot