normalmixEM gives widely divergent results.
On Wed, 27 Jan 2016 11:51:07 -0500 John Sorkin <JSorkin at grecc.umaryland.edu> wrote:
I am running normalmixEM: mixmdlscaled <- normalmixEM(data$FCWg) summary(mixmdlscaled) plot(mixmdlscaled,which=2) If I run the program multiple times, I get widely different results:
mixmdlscaled <- normalmixEM(data$FCWg)
number of iterations= 41
summary(mixmdlscaled)
summary of normalmixEM object:
comp 1 comp 2
lambda 0.0818928 0.918107
mu 0.6575938 0.740870
sigma 0.0070562 0.178410
loglik at estimate: 56.87445
plot(mixmdlscaled,which=2) mixmdlscaled <- normalmixEM(data$FCWg)
number of iterations= 357
summary(mixmdlscaled)
summary of normalmixEM object:
comp 1 comp 2
lambda 0.959912 0.0400879
mu 0.722022 1.0220719
sigma 0.165454 0.0131391
loglik at estimate: 53.66051
plot(mixmdlscaled,which=2)
I understand that when run without specifying various parameters (e.g. mu, or sigma) values are chosen randomly from a normal distribution with center(s) determined from binning the data.
I don't know what this means or what the mechanics are.
Despite this, would not one expect the results to be similar? If one is not to expect similar results, how can I get a solution in which I can have confidence? Should I run the program multiple times and take the average of the results? Should I look for the solution with the best log likelihood?
But if a likelihood has several local maxima wrt its parameters, isn't this what you would expect? I don't know how familiar you are with statistics so maybe I am repeating something that you already know, but a MLE (note the indefinite article) is what is found by the EM or any iterative/root-finding method in the vicinity of its initialization. Your best best is to use a package such as EMCluster. If you want to use the above package, you should make several runs and then choose the one which gives a stable solution and the highest loglikelihood value. EMCluster does it for you. HTH! Best wishes, Ranjan
Thank you,
John
John David Sorkin M.D., Ph.D.
Professor of Medicine
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology and Geriatric Medicine
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