Re: [R] weighted likelihood for lme
--- Spencer Graves [EMAIL PROTECTED] wrote:
Thank you for providing such a marvelous example.
I wish I could
reward your dilligence with a simple, complete
answer. Unfortunately,
the best I can offer at the moment is a guess and a
reference. First, I
believe you are correct in that the weights
argument describes the
within-group heteroscedasticity structure. To
specify between-group
heterscadisticity, have you considered the
following:
foo - Orthodont
foo$w - c(rep(1, 5*4), rep(0.5, 22*4))
lme(distance ~ 1, random = ~ w-1|Subject,
+ method=ML, data = foo)
Linear mixed-effects model fit by maximum likelihood
Data: foo
Log-likelihood: -258.8586
Fixed: distance ~ 1
(Intercept)
23.8834
Random effects:
Formula: ~w - 1 | Subject
w Residual
StdDev: 3.370796 2.233126
Number of Observations: 108
Number of Groups: 27
Nice! I haven't considered this alternative. As you
said, it's not a complete answer, but, I say, it's a
smart start.
Second, have you consulted Pinheiro and Bates
(2000) Mixed-Effects
Models in S and S-Plus (Springer)? If you have not
already, I encourage
you to spend some quality time with that book. For
me, this book helped
transform lme from an inaequately documented and
unusable black box
into a simple, understandable, elegant tool. I may
have recommeded it
to more people than any other single work over the
past five years.
I know the book by Pinheiro and Bates and I do really
have to spend some time with it.
Thanks very much,
Marco
hope this helps.
spencer graves
Marco Geraci wrote:
Dear R users,
I'm trying to fit a simple random intercept
model with a fixed intercept.
Suppose I want to assign a weight w_i to the
i-th contribute to the log-likelihood, i.e.
w_i * logLik_i
where logLik_i is the log-likelihood for the
i-th subject.
I want to maximize the likelihood for N subjects
Sum_i {w_i * logLik_i}
Here is a simple example to reproduce
# require(nlme)
foo - Orthodont
lme(distance ~ 1, random = ~ 1|Subject,
method=ML, data = foo)
Linear mixed-effects model fit by maximum
likelihood
Data: foo
Log-likelihood: -257.7456
Fixed: distance ~ 1
(Intercept)
24.02315
Random effects:
Formula: ~1 | Subject
(Intercept) Residual
StdDev:1.888748 2.220312
Number of Observations: 108
Number of Groups: 27
Then I assign arbitrary weights, constant within
the group. I want to give weight 1 to the first 5
subjects, and weight 0.5 to the others 22 (4 is the
number of repeated measurements for each subject)
foo$w - c(rep(1, 5*4), rep(0.5, 22*4))
Maybe I am missing something, but I believe that
lme(distance ~ 1, random = ~ 1|Subject,
method=ML, data = foo, weight=~w)
does not maximize the likelihood Sum_i {w_i *
logLik_i},
since 'weight' describes the with-in
heteroscedasticity structure.
I think I need something like the option
'iweight'
(importance weight) for the command 'xtreg' of
Stata.
Any suggestion for R?
Thanks in advance,
Marco Geraci
sessionInfo()
R version 2.2.1, 2005-12-20, i386-pc-mingw32
attached base packages:
[1] methods stats graphics
grDevices utils
[6] datasets base
other attached packages:
nlme
3.1-66
-
What are the most popular cars? Find out at
Yahoo! Autos
[[alternative HTML version deleted]]
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Re: [R] weighted likelihood for lme
Thank you for providing such a marvelous example. I wish I could
reward your dilligence with a simple, complete answer. Unfortunately,
the best I can offer at the moment is a guess and a reference. First, I
believe you are correct in that the weights argument describes the
within-group heteroscedasticity structure. To specify between-group
heterscadisticity, have you considered the following:
foo - Orthodont
foo$w - c(rep(1, 5*4), rep(0.5, 22*4))
lme(distance ~ 1, random = ~ w-1|Subject,
+ method=ML, data = foo)
Linear mixed-effects model fit by maximum likelihood
Data: foo
Log-likelihood: -258.8586
Fixed: distance ~ 1
(Intercept)
23.8834
Random effects:
Formula: ~w - 1 | Subject
w Residual
StdDev: 3.370796 2.233126
Number of Observations: 108
Number of Groups: 27
Second, have you consulted Pinheiro and Bates (2000) Mixed-Effects
Models in S and S-Plus (Springer)? If you have not already, I encourage
you to spend some quality time with that book. For me, this book helped
transform lme from an inaequately documented and unusable black box
into a simple, understandable, elegant tool. I may have recommeded it
to more people than any other single work over the past five years.
hope this helps.
spencer graves
Marco Geraci wrote:
Dear R users,
I'm trying to fit a simple random intercept model with a fixed intercept.
Suppose I want to assign a weight w_i to the i-th contribute to the
log-likelihood, i.e.
w_i * logLik_i
where logLik_i is the log-likelihood for the i-th subject.
I want to maximize the likelihood for N subjects
Sum_i {w_i * logLik_i}
Here is a simple example to reproduce
# require(nlme)
foo - Orthodont
lme(distance ~ 1, random = ~ 1|Subject, method=ML, data = foo)
Linear mixed-effects model fit by maximum likelihood
Data: foo
Log-likelihood: -257.7456
Fixed: distance ~ 1
(Intercept)
24.02315
Random effects:
Formula: ~1 | Subject
(Intercept) Residual
StdDev:1.888748 2.220312
Number of Observations: 108
Number of Groups: 27
Then I assign arbitrary weights, constant within the group. I want to give
weight 1 to the first 5 subjects, and weight 0.5 to the others 22 (4 is the
number of repeated measurements for each subject)
foo$w - c(rep(1, 5*4), rep(0.5, 22*4))
Maybe I am missing something, but I believe that
lme(distance ~ 1, random = ~ 1|Subject, method=ML, data = foo,
weight=~w)
does not maximize the likelihood Sum_i {w_i * logLik_i},
since 'weight' describes the with-in heteroscedasticity structure.
I think I need something like the option 'iweight'
(importance weight) for the command 'xtreg' of Stata.
Any suggestion for R?
Thanks in advance,
Marco Geraci
sessionInfo()
R version 2.2.1, 2005-12-20, i386-pc-mingw32
attached base packages:
[1] methods stats graphics grDevices utils
[6] datasets base
other attached packages:
nlme
3.1-66
-
What are the most popular cars? Find out at Yahoo! Autos
[[alternative HTML version deleted]]
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
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R-help@stat.math.ethz.ch mailing list
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[R] weighted likelihood for lme
Dear R users,
I'm trying to fit a simple random intercept model with a fixed intercept.
Suppose I want to assign a weight w_i to the i-th contribute to the
log-likelihood, i.e.
w_i * logLik_i
where logLik_i is the log-likelihood for the i-th subject.
I want to maximize the likelihood for N subjects
Sum_i {w_i * logLik_i}
Here is a simple example to reproduce
# require(nlme)
foo - Orthodont
lme(distance ~ 1, random = ~ 1|Subject, method=ML, data = foo)
Linear mixed-effects model fit by maximum likelihood
Data: foo
Log-likelihood: -257.7456
Fixed: distance ~ 1
(Intercept)
24.02315
Random effects:
Formula: ~1 | Subject
(Intercept) Residual
StdDev:1.888748 2.220312
Number of Observations: 108
Number of Groups: 27
Then I assign arbitrary weights, constant within the group. I want to give
weight 1 to the first 5 subjects, and weight 0.5 to the others 22 (4 is the
number of repeated measurements for each subject)
foo$w - c(rep(1, 5*4), rep(0.5, 22*4))
Maybe I am missing something, but I believe that
lme(distance ~ 1, random = ~ 1|Subject, method=ML, data = foo, weight=~w)
does not maximize the likelihood Sum_i {w_i * logLik_i}, since 'weight'
describes the with-in heteroscedasticity structure.
I think I need something like the option 'iweight' (importance weight) for
the command 'xtreg' of Stata.
Any suggestion for R?
Thanks in advance,
Marco Geraci
sessionInfo()
R version 2.2.1, 2005-12-20, i386-pc-mingw32
attached base packages:
[1] methods stats graphics grDevices utils
[6] datasets base
other attached packages:
nlme
3.1-66
-
What are the most popular cars? Find out at Yahoo! Autos
[[alternative HTML version deleted]]
__
R-help@stat.math.ethz.ch mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
