CODEWITHSUNDEEP
rstatisticssas
Julio47
Julio47

Reputation: 37

Maximum likelihood lognormal R and SAS

I am converting SAS codes to R and there is a feature of using lognormal distribution in the SAS univariate procedure using histograms and midpoints. The result is a table containing the following variables,

EXPPCT - estimated percent of population in histogram interval determined from optional fitted distribution (here it is lognormal)

OBSPCT - percent of variable values in histogram interval

VAR - variable name

MIDPT - midpoint of histogram interval

There is an option in SAS to consider the MLE of the zeta, theta and sigma parameters while applying the distribution.

Now I was able to figure out the way to do this in R. My only problem arises in the likelihood estimation, when the three parameters are being estimated in SAS. R gives me different values.

I am using the following for MLE in R.

library(fitdistrplus)
set.seed(0)
cd <- rlnorm(40,4)
pars <- coef(fitdist(cd, "lnorm"))

  meanlog     sdlog 
4.0549354 0.8620153

I am using the following for MLE in SAS. (the est option)

proc univariate data = testing;
histogram cd /lognormal (theta = est zeta=est sigma=est)
midpoints = 1 to &maxx. by 100
outhistogram = this;
run;

&maxx denotes the maximum of the input. The results of the run from SAS can be found here.

I am new to statistics and unable to find the method used for the MLE in SAS and have no clue as to how to estimate the same in R.

Thanks in advance.

Upvotes: 0

Views: 613

Answers (1)

Julio47
Julio47

Reputation: 37

I found these packages EnvStats and FAdist that let me estimate the threshold parameter and use these parameters to fit the 3 parameter lognormal distribution. Backlin was right about the parameters. Right now, the parameters are not an exact match but the end result is the same as SAS. Thank you vey much.

Upvotes: 0

Related Questions