NOBOUND [Software]
❝ Sorry but I think it should be NOBOUND
option with Proc MIXED
.
Absolutely right – I dabble in SAS. I just have the 6.3-Manual (a verbose 8,640 pages):
NOBOUND
requests the removal of boundary constraints on covariance parameters. For example, variance components have a default lower boundary constraint of 0, and the NOBOUND option allows their estimates to be negative.
Parameter Constraints
By default, some covariance parameters are assumed to satisfy certain boundary constraints during the Newton-Raphson algorithm. For example, variance components are constrained to be nonnegative […]. You can remove these constraints […] with the NOBOUND option in the PROC MIXED statement, but this can lead to estimates that produce an infinite likelihood. […]
For some data sets the final estimate of a parameter might equal one of its boundary constraints. This is usually not a cause for concern, but it might lead you to consider a different model. For instance, a variance component estimate can equal zero; in this case, you might want to drop the corresponding random effect from the model. However, be aware that changing the model in this fashion can affect degrees-of-freedom calculations.
Convergence Problems
- Using the NOPROFILE and NOBOUND options in the PROC MIXED statement might help convergence, although they can produce unusual results.
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Helmut Schütz
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Science Quotes
Complete thread:
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- Negative variance component zan 2014-01-31 00:16
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- Negative variance component yjlee168 2014-01-31 10:26
- Example data set Helmut 2014-02-01 16:03
- Example data set yjlee168 2014-02-01 17:40
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- Example data set yjlee168 2014-02-01 17:40
- Example data set Helmut 2014-02-01 16:03
- Negative variance component ElMaestro 2014-02-01 16:31
- Negative variance component yjlee168 2014-02-01 17:47
- Just thinking loud ElMaestro 2014-02-01 19:02
- All models are wrong… Helmut 2014-02-02 02:31
- another book for linear model yjlee168 2014-02-02 08:07
- All models are wrong… Helmut 2014-02-02 02:31
- Just thinking loud ElMaestro 2014-02-01 19:02
- References Helmut 2014-02-02 02:19
- References ElMaestro 2014-02-02 09:56
- Negative variance component yjlee168 2014-02-01 17:47
- Negative variance component – Chow/Liu d_labes 2014-02-03 09:02
- Negative variance component – Chow/Liu ElMaestro 2014-02-03 10:22
- Variance components – Proc mixed d_labes 2014-02-03 11:58
- Variance components – Proc mixed ElMaestro 2014-02-03 12:58
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- Variance components – Proc mixed d_labes 2014-02-03 11:58
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- lm() or lme() for 2x2x2 study design? yjlee168 2014-02-04 13:09
- bear for 2x2x2 study with negative variance components yjlee168 2014-02-05 19:12
- lm() or lme() for 2x2x2 study design? yjlee168 2014-02-04 13:09
- lm() or lme() for 2x2x2 study design? ElMaestro 2014-02-03 22:11
- Negative variance component – Chow/Liu ElMaestro 2014-02-03 10:22
- Negative variance component Helmut 2014-01-30 01:16