How do you generate a Cumulative Histogram on R2014a?

I am trying to produce a Cummulative Histogram on MatLab so I can work out the 50% and 99% Percentiles in a set of data. I was shown and given a piece of code that will work on a newer version of MatLab but the only versions I have access to is MatLab R2014a which I have learned does not use the same commands as the newer versions. The section of code I am trying to use is shown below.

figure
    histogram(wSpd,'Normalization','cdf');  % plot the cumulative histogram

      y = quantile(wSpd, [0.5 0.99]); % extract the 50th and 99th quantiles (median and extreme)

As far as I know, hist is one of the options, but I have not been able to find any documentation for 2014a, only 2018a. Is there a way of doing what this section of code does in R2014a?

Answer 1

[Image Analyst]

You can get the counts with histcounts(), and then use cumsum(counts).

[Sam Harper-Barber]

And how would that be put into the code? Say by changing the first line to hist, removing the normalisation and cdf and then having a new line with cumsum which I assume can do the same thing?

[Image Analyst]

Did you look up those functions? You just do

[counts, bins] = histcounts(data);
cdf = cumsum(counts);

Luckily, Anton gave you a full demo below in his answer.

Answer 2

[Anton Semechko]

Here is an example of how to use 'histcounts' and estimate percentiles:

% Simulate data; N samples from standard Guassian PDF
N=1E4;
X=randn(N,1);

% Bin data into B bins of equal size
X_min=min(X);
X_max=max(X);
B=50;                           % # of bins between X_min and X_max
dB=(X_max-X_min)/B;             % bin size
BE=(X_min-dB):dB:(X_max+dB);    % bin edges; first and last edges are at X_min-dB and X_max+dB
H=histcounts(X,BE);             % histogram of X
MDF=H/N;                        % mass density function
Bc=(BE(2:end)+BE(1:(end-1)))/2; % bin centroids

% Cumulative mass function
CMF=cumsum(MDF);

% Esimate 50-th and 99-th percentiles
p=[50 99]/100;
Xp=zeros(size(p));
X_srt=sort(X);
CMF_raw=cumsum(ones(1,N))/N;
for i=1:numel(p)
    [Ya,id_a]=find(CMF_raw<=p(i),1,'last');
    [Yb,id_b]=find(CMF_raw>=p(i),1,'first');
    if (Ya-p(i))<eps || id_a==id_b
        Xp(i)=X_srt(id_a);
        continue
    end
    [Xa,Xb]=deal(X_srt(id_a),X_srt(id_b));    
    m=(Yb-Ya)/(Xb-Xa);
    if abs(m)<eps
        Xp(i)=(Xa+Xb)/2;
    else
        Xp(i)=(p(i)-Ya)/m+Xa;
    end
end

% Visualize MDF along with specified percentiles
figure('color','w')
ha=subplot(1,2,1);
h=bar(Bc,MDF);
set(h,'FaceColor',0.75*[1 1 1],'EdgeColor','k')
hold on 
YLim=get(ha,'YLim');
col=zeros(numel(p),3);
for i=1:numel(p)
    h=plot(Xp(i)*ones(2,1),YLim(:),'--','LineWidth',2);
    col(i,:)=get(h,'Color');
end
set(get(ha,'Title'),'String','Normalized Histogram','FontSize',20)

% Visualize CDF
ha=subplot(1,2,2);
h=bar(Bc,CMF);
set(h,'FaceColor',0.75*[1 1 1],'EdgeColor','k')
hold on 

XLim=get(ha,'XLim');
for i=1:numel(p)
    plot(Xp(i)*ones(2,1),[0;p(i)],'--','LineWidth',2,'Color',col(i,:))
    plot([XLim(1);Xp(i)],p(i)*ones(2,1),'--','LineWidth',2,'Color',col(i,:))
end
set(get(ha,'Title'),'String','Cumulative Mass Function','FontSize',20)