Plot and fit exponential decay probability of population

Hello

I have a population

example=[1,1,2,4,NaN,NaN,10,200,50]

were each value is the time were the respective element of the population "dies" with "NaN" being elements, that never "die".

I am looking for a way to plot the propability that an element "dies" by the funtion of time converging to the relative amount ('NaN') of elements "surviving" as well as to fit this with a exponential decay curve (I know from e.g. histfit, that my data resembles this behaviour).

I already tried to use the hazard function or to plot 1-cdf.

Thanks for advance.

[Peter Mühlenbrock]

I got so far to fit the data without taking the amount of "NaN" into account.

figure(1)
ecdf(y,'function','survivor');
 h=gcf;
 dataObjs = findobj(gcf,'-property','YData');
 dmy3=dataObjs.XData;
 x=dmy3';
 dmy4=dataObjs.YData;
 y2=dmy4';

Up until now the survival function converges to 0. How can i make it converge to the amount of "NaN" in "y"?

Answer 1

[the cyclist]

You are correct to use the ecdf function. I think the piece you are missing is the use of the 'censoring' input. Take a look at the example Empirical Hazard Function of Right-Censored Data

[Peter Mühlenbrock]

Perfect, this is working now, thanks. How can I now extract the datapoints for further fitting including the bounds as a weighting factor for the fit? The way I did it above is giving me the upper bounds as yData

[the cyclist]

I'm not sure I fully understand your question. May I suggest you accept this answer, and then post a new question with a bit more detail about what you mean? I'll keep a lookout for it.