MATLAB: Normal distribution problem using histfit

histogramplottingstatistics

Hi MatLab-friends,

I have a question about histfit. My dataset only contains positive (or 0s) values but when I make my histfit, the normal distribution goes from negative values. How can I make the distribution line only consider positive values?

Attaching figure. How I want the distribution to look is seen with the black line.

Thank you in advance.

P.S.

The code I use now is:

F=d_f_t(:,2) %

n_d=histfit(F)

Best Answer

If @dpb had made his comment as an answer, I would have upvoted it.

Your data are clearly not normally distributed. Is there a particular reason that you wanted to fit them with a normal?

You can use the histfit function with a different underlying distribution. dpb's suggestion of a lognormal looks like an excellent choice, from a purely empirical point of view (but you may know something about the underlying process that makes some other distribution a better choice). See the documentation for details, but for example ...

n_d = histfit(F,[ ],'lognormal')

should give you something much better than you have now.

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Are they correlated or uncorrelated? You could use a copula method.

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doc normcdf
doc normpdf

When you know what you want but not sure the name, try something like

>> lookfor normal
realmin                        - Smallest positive normalized floating point number.
randn                          - Normally distributed pseudorandom numbers.
sprandn                        - Sparse normally distributed random matrix.
surfnorm                       - Surface normals.
isonormals                     - Isosurface normals.
cde                            - cd elliptic function with normalized complex argument.
sne                            - sn elliptic function with normalized complex argument.
addfreqcsmenu                  - Add a cs menu to switch between linear and normalized frequency
convertfrequnits               - converts between Normalized, Hz, kHz, etc
histfit                        - Histogram with superimposed fitted normal density.
jbtest                         - Jarque-Bera hypothesis test of composite normality.
lhsnorm                        - Generate a latin hypercube sample with a normal distribution
logncdf                        - Lognormal cumulative distribution function (cdf).
lognfit                        - Parameter estimates and confidence intervals for lognormal data.
logninv                        - Inverse of the lognormal cumulative distribution function (cdf).
lognlike                       - Negative log-likelihood for the lognormal distribution.
lognpdf                        - Lognormal probability density function (pdf).
lognrnd                        - Random arrays from the lognormal distribution.
lognstat                       - Mean and variance for the lognormal distribution.
mvncdf                         - Multivariate normal cumulative distribution function (cdf).
mvnpdf                         - Multivariate normal probability density function (pdf).
mvnrnd                         - Random vectors from the multivariate normal distribution.
normcdf                        - Normal cumulative distribution function (cdf).
normfit                        - Parameter estimates and confidence intervals for normal data.
norminv                        - Inverse of the normal cumulative distribution function (cdf).
normlike                       - Negative log-likelihood for the normal distribution.
normpdf                        - Normal probability density function (pdf).
normplot                       - Displays a normal probability plot.
normrnd                        - Random arrays from the normal distribution.
normspec                       - Plots normal density between specification limits.
normstat                       - Mean and variance for the normal distribution.
logn3fit                       - Fit a 3-param lognormal dist'n using cumulative probabilities.
wgtnormfit                     - Fitting example for a weighted normal distribution.
wgtnormfit2                    - Fitting example for a weighted normal distribution (log(sigma) parameterization).
>>

Judicious search terms help but seeing the list of things related to "normal" lets you find the two functions of interest (plus a lot more depending upon which toolboxes are available, maybe) that might be of use/interest...

Best Answer

Related Solutions

MATLAB: How to generate or simulate multivariate observations that are not multivariate normal

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