Hi Peter,
>> See e.g. Hedeker and Gibbons, Longitudinal Data Analysis, which
>> repeatedly stresses that
>> mixed models provide good estimates if the data are missing at random.
This may be true. However, one of the real strengths of LME is that it
handles unbalanced designs, which is a differ
Mixed models based on likelihood methods can often handle missing
observations within subjects, but they not do well with missing
individual elements in the design matrices (think unit nonresponse vs
item nonresponse in the survey world). Continuing with the example I
recently sent to you
set.see
I wrote
>>> I am puzzled by the performance of LME in situations where there are
>>> missing data. As I
>>> understand it, one of the strengths of this sort of model is how well it
>>> deals with missing
>>> data, yet lme requires nonmissing data.
>
Mark Difford replied
>You are confusing mi
Peter Flom wrote:
>> I am puzzled by the performance of LME in situations where there are
>> missing data. As I
>> understand it, one of the strengths of this sort of model is how well it
>> deals with missing
>> data, yet lme requires nonmissing data.
You are confusing missing data with an
Hello
Running R2.9.2 on Windows XP
I am puzzled by the performance of LME in situations where there are missing
data. As I understand it, one of the strengths of this sort of model is how
well it deals with missing data, yet lme requires nonmissing data.
Thus,
m1.mod1 <- lme(fixed = math_
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