Scipy: lognormal fitting

Question

There have been quite a few posts on handling the lognorm distribution with Scipy but i still dont get the hang of it.

The 2 parameter lognormal is usua

Answer 1

I made the same observations: a free fit of all parameters fails most of the time. You can help by providing a better initial guess, fixing the parameter is not necessary.

samp = stats.lognorm(0.5,loc=0,scale=1).rvs(size=2000)

# this is where the fit gets it initial guess from
print stats.lognorm._fitstart(samp)

(1.0, 0.66628696413404565, 0.28031095750445462)

print stats.lognorm.fit(samp)
# note that the fit failed completely as the parameters did not change at all

(1.0, 0.66628696413404565, 0.28031095750445462)

# fit again with a better initial guess for loc
print stats.lognorm.fit(samp, loc=0)

(0.50146296628099118, 0.0011019321419653122, 0.99361128537912125)

You can also make up your own function to calculate the initial guess, e.g.:

def your_func(sample):
    # do some magic here
    return guess

stats.lognorm._fitstart = your_func