Hi there, We have got a valuable data set from a long-term experiment. The experimental design is following: Trt Time Y 1 1983 1 1986 1 1993 1 1998 1 2003 1 2010 2 1983 2 1986 2 1993 2 1998 2 2003 2 2010 3 1983 3 1986 3 1993 3 1998 3 2003 3 2010 Because the data was collected in a way of repeated measurement. we think it should be analyzed using linear mixed model. However, there is no group variable, and as you have noted, there is no replication for each treatment (Trt). So, we don't know how to deal with this data set. Any suggestions or comments will be really appreciated. Thanks in advance. Regards, Jinsong
repeated measurement using mixed model
3 messages · Jinsong Zhao, Christoph Scherber
Dear Jinsong Zhao, In this case, you may wish to use a generalized least squares model (gls), also to be found in the nlme library. You can start with an initial model containing only fixed effects, and then update it with an appropriate correlation structure. Try this first: library(nlme) ?gls ?corStruct All the best Christoph
on 18.11.2011 07:29, Jinsong Zhao wrote:
Hi there, We have got a valuable data set from a long-term experiment. The experimental design is following: Trt Time Y 1 1983 1 1986 1 1993 1 1998 1 2003 1 2010 2 1983 2 1986 2 1993 2 1998 2 2003 2 2010 3 1983 3 1986 3 1993 3 1998 3 2003 3 2010 Because the data was collected in a way of repeated measurement. we think it should be analyzed using linear mixed model. However, there is no group variable, and as you have noted, there is no replication for each treatment (Trt). So, we don't know how to deal with this data set. Any suggestions or comments will be really appreciated. Thanks in advance. Regards, Jinsong
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models .
Dr. Christoph Scherber Georg-August-University Goettingen Department of Crop Science Agroecology Grisebachstrasse 6 37077 Goettingen tel +49 (0)551 39 8807 fax +49 (0)551 39 8806 www.gwdg.de/~cscherb1
On 2011-11-18 21:32, Christoph Scherber wrote:
Dear Jinsong Zhao, In this case, you may wish to use a generalized least squares model (gls), also to be found in the nlme library. You can start with an initial model containing only fixed effects, and then update it with an appropriate correlation structure. Try this first: library(nlme) ?gls ?corStruct All the best Christoph
Dear Christoph, Thank you very much for the kindly reply. Dose gls() fit the linear model without random effects? Is corStruct used with correlation argument to explore the structure of residual variance? Another question, someone suggests that replications of response variable, e.g., determined concentration on 3 samples obtained from same treatment, should be include in the design. Then, the random effects could be estimated (I don't know how), and it will improve the power of statistical analysis. I don't think so, however, I can't find a way to explain that. Regards, Jinsong
on 18.11.2011 07:29, Jinsong Zhao wrote:
Hi there,
We have got a valuable data set from a long-term experiment. The
experimental design is following:
Trt Time Y
1 1983
1 1986
1 1993
1 1998
1 2003
1 2010
2 1983
2 1986
2 1993
2 1998
2 2003
2 2010
3 1983
3 1986
3 1993
3 1998
3 2003
3 2010
Because the data was collected in a way of repeated measurement. we
think it should be analyzed using linear mixed model. However, there is
no group variable, and as you have noted, there is no replication for
each treatment (Trt). So, we don't know how to deal with this data set.
Any suggestions or comments will be really appreciated. Thanks in advance.
Regards,
Jinsong
_______________________________________________ R-sig-mixed-models at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models .