Lognormal probability density function Fitting to experimental data

Hello. I would appreciate so much if someone could give me a hand whit this, I've been trying to fit a Lognormal probability density function curve to an experimental data that comes from frequency info. I was able to reproduce some examples using "lognpdf", but I didn't succeed when I tryed to adjust them.

The experiemental data is:

rp = [0.125 0.175 0.200 0.250 0.300 0.375]*1e-6    ;           % um
fp = [6.160 19.63 61.19 10.96 1.370 0.690]/100          ;         

plot (rp,fp,'linestyle','--','color','b')
stem(rp*10^6,fp)
grid on;
ax = gca;
ax.GridLineStyle = '--';
ax.GridColor = 'r';
xlabel ('r (\mum)');
ylabel ('fp');

I need to fit it with the "lognpdf" if possible, it would also work with lognormal distributions, in both cases I need to calculate the statistic values (mean and standard deviation).

Thanks in advance.

[infinity]

Hello,

You might take a look at this

https://www.mathworks.com/help/stats/lognfit.html

Answer 1

[Jeff Miller]

I'm guessing that your rp values are the tops of various bins and that the fp values are the probabilities in each bin? If so, Cupid says that the minimum chi-square estimates of the lognormal mean & sd (multiplied by 10^6 as in your figure) are 0.18144 and 0.032248.

As far as I can see, lognfit will only work with the original data values, not with binned frequency data.

[Jhon MT]

Thanks for your answer, even with the original data I couldn't do it, could you check it.

rp = [0.125 0.175 0.200 0.250 0.300 0.375]*1e-6    ;           % um
fp = [7.261553565341827e+10 2.314030787137338e+11 7.213221796481597e+11 1.291990699288091e+11 1.614988374110114e+10 8.133883052087434e+09];         

Thanks in advance.

[Jeff Miller]

Hmmm...now I'm not sure what your rp and fp are. Your original fp values summed to 1, so I thought they were bin probabilities. These new fp values obviously aren't probabilities however.

If you have the original data, it is better to use those. What was the problem that made you say "even with the original data I couldn't do it"?