Dear Ted,
sorry for being unclear. Let me try again.
I indeed have no knowledge about the value of the response variable for
any object.
Instead, I have a data frames of explanatory variables for
each object. For example,
x1 x2 x3
1 4.409974 2.348745 1.9845313
2 3.809249 2.281260 1.9170466
3 4.229544 2.610347 0.9127431
4 4.259644 1.866025 1.5982859
5 4.001306 2.225069 1.2551570
...
, and I want to model a regression model of the form y ~ x1 + x2 + x3.
From prior information I know that all coefficients are approximately
Gaussian distributed around one and the same for the intercept around
-10. Now I think there must be a package which estimates the
coefficients more precisely by fitting the logistic regression function
to the data without knowledge of the response variable (similar to
fitting Gaussians in a mixture model where the class labels are unknown).
I looked at the flexmix package but this seems to "only" find
dependencies in the data assuming the presence of some training data.
I also found some evidence In Magder1997 (see below) that such an
algorithm exists, however from the documented math I can't apply the
method to my problem.
Thanks in advance,
Best Regards
Robin
Magder, L. S. & Hughes, J. P. Logistic Regression When the Outcome Is
Measured with Uncertainty American Journal of Epidemiology, 1997, 146,
195-203
On 01/04/2011 12:36 AM, (Ted Harding) wrote:
On 03-Jan-11 14:02:21, Robin Aly wrote:
Hi all,
is there any package which can do an EM algorithm fitting of
logistic regression coefficients given only the explanatory
variables? I tried to realize this using the Design package,
but I didn't find a way.
Thanks a lot& Kind regards
Robin Aly
As written, this is a strange question! You imply that you
do not have data on the response (0/1) variable at all,
only on the explanatory variables. In that case there is
no possible estimate, because that would require data on
at least some of the values of the response variable.
I think you should explain more clearly and explicitly what
the information is that you have for all the variables.
Ted.
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Date: 03-Jan-11 Time: 23:36:56
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