    Michael Haenlein wrote
    Dear all,

    I would like to use predict.lm to obtain a set of predicted values
    based on
    a regression model I estimated.

    When I apply predict.lm to two vectors that have the same values, the
    predicted values will be identical. I know that my regression model
    is not
    perfect and I would like to take account of the error inherent in
    the model
    within my predictions. So, while I understand that the expected
    value of
    both vectors should be the same (since they have the same value), I
    would
    like to have different predictions to take account of the error
    inherent in
    my model.

    I assume I can probably use se.fit to achieve my objective of including
    "random error" in my predictions but I don't really know how. Could
    anybody
    give me a pointer on how this can be done?

    Thanks,

    Michael

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I guess that, given the fact that you know how good/bad your models are, 
you can specify an additional error therm  (a random variable with given 
mean and standard deviation). That variable will "cause" the predicted 
values to be different every time you predict the outcome. The model you 
have fitted are a linear model after all (You just need to add the error).

Hope it helps,

Marko



-- 
Marko Tonc(ic'
Assistant Researcher
University of Rijeka
Faculty of Humanities and Social Sciences
Department of Psychology
Sveu?ilis(na Avenija 4, 51000 Rijeka, Croatia

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