Dear all,

I'm trying to estimate the parameters of a lognormal distribution fitted
from some data.

The tricky thing is that my data represent the time at which I recorded
certain events. However, in many cases I don't really know when the event
happened. I' only know the time at which I recorded it as already happened.

Therefore I want to fit the lognormal from the cumulative distribution
function (cdf) rather than from the probability distribution function (pdf).

My understanding is that methods based on Maximum Likelihood (e.g. fitdistr
{MASS}) are based on the pdf. Nonlinear least-squares methods seem to be
based on the cdf... however I was unable to use nls{stat} for lognormal.

I found a website that explains how to fit univariate distribution functions
based on cumulative probabilities, including a lognormal example, in Matlab:
http://www.mathworks.com/products/statistics/demos.html?file=/products/demos/shipping/stats/cdffitdemo.html

and other programs like TableCurve 2D seem to do this too.

There must be a straightforward method in R which I have overlooked. Any
suggestion on how can I estimate these parameters in R or helpful references
are very much appreciated.

(not sure if it helps but) here is an example of my type of data:

treat.1 <- c(21.67, 21.67, 43.38, 35.50, 32.08, 32.08, 21.67, 21.67, 41.33,
        41.33, 41.33, 32.08, 21.67, 22.48, 23.25, 30.00, 26.00, 19.37, 26.00
,
        32.08, 21.67, 26.00, 26.00, 43.38, 26.00, 21.67, 22.48, 35.50, 38.30,

        32.08)

treat.2 <- c(35.92, 12.08, 12.08, 30.00, 33.73, 35.92, 12.08, 30.00, 56.00,
        30.00, 35.92, 33.73, 12.08, 26.00, 54.00, 12.08, 12.08, 35.92, 35.92
,
        12.08, 33.73, 35.92, 63.20, 30.00, 26.00, 33.73, 23.50, 30.00, 35.92
,
        30.00)

Thank you very much!

Ahimsa


-- 
ahimsa campos-arceiz
www.camposarceiz.com

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