Re: [R] unequal variance assumption for lme (mixed effect model)
gls() from the package nlme is similar to lm but is meant for models without random effects. Joris Spencer Graves [EMAIL PROTECTED] df.comTo Sent by: shirley zhang [EMAIL PROTECTED] [EMAIL PROTECTED] at.math.ethz.chcc R-help@stat.math.ethz.ch Subject 01/07/2007 23:30 Re: [R] unequal variance assumption for lme (mixed effect model) The 'weights' argument on 'lm' is assumed to identify a vector of the same length as the response, giving numbers that are inversely proportional to the variance for each observation. However, 'lm' provides no capability to estimate weights. If you want to do that, the varFunc capabilities in the 'nlme' package is the best tool I know for that purpose. If someone thinks there are better tools available for estimating heterscedasticity, I hope s/he will enlighten us both. Hope this helps. Spencer Graves shirley zhang wrote: Thanks for Spencer and Simon's help. I've got very interesting results based on your suggestions. One more question, how to handle unequal variance problme in lm()? Isn't the weights option also, which means weighted least squares, right? Can you give me an example of setting this parameter in lm() to account for different variance assumption in each group? Thanks again, Shirley On 6/29/07, Spencer Graves [EMAIL PROTECTED] wrote: comments in line shirley zhang wrote: Hi Simon, Thanks for your reply. Your reply reminds me that book. I've read it long time ago, but haven't try the weights option in my projects yet:) Is the heteroscedastic test always less powerful because we have to estimate the within group variance from the given data? SG: In general, I suspect we generally lose power when we estimate more parameters. SG: You can check this using the 'simulate.lme' function, whose use is illustrated in the seminal work reported in sect. 2.4 of Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). Should we check whether each group has equal variance before using weights=varIdent()? If we should, what is the function for linear mixed model? SG: The general advice I've seen is to avoid excessive overparameterization of heterscedasticity and correlations. However, parsimonious correlation had heterscedasticity models would likely be wise. Years ago, George Box expressed concern about people worrying too much about outliers, which are often fairly obvious and relatively easy to detect, while they worried too little, he thought, about dependence, especially serial dependence, which is generally more difficult to detect and creates bigger problems in inference than outliers. He wrote, Why worry about mice when there are tigers about? SG: Issues of this type can be fairly easily evaluated using 'simulate.lme'. Hope this helps. Spencer Graves Thanks, Shirley On 6/27/07, Simon Blomberg [EMAIL PROTECTED] wrote: The default settings for lme do assume equal variances within groups. You can change that by using the various varClasses. see ?varClasses. A simple example would be to allow unequal variances across groups. So if your call to lme was: lme(...,random=~1|group,...) then to allow each group to have its own variance, use: lme(...,random=~1|group, weights=varIdent(form=~1|group),...) You really really should read Pinheiro Bates (2000). It's all there. HTH, Simon. , On Wed, 2007-06-27 at 21:55 -0400, shirley zhang wrote: Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley __ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE
Re: [R] unequal variance assumption for lme (mixed effect model)
Thanks for Spencer and Simon's help. I've got very interesting results based on your suggestions. One more question, how to handle unequal variance problme in lm()? Isn't the weights option also, which means weighted least squares, right? Can you give me an example of setting this parameter in lm() to account for different variance assumption in each group? Thanks again, Shirley On 6/29/07, Spencer Graves [EMAIL PROTECTED] wrote: comments in line shirley zhang wrote: Hi Simon, Thanks for your reply. Your reply reminds me that book. I've read it long time ago, but haven't try the weights option in my projects yet:) Is the heteroscedastic test always less powerful because we have to estimate the within group variance from the given data? SG: In general, I suspect we generally lose power when we estimate more parameters. SG: You can check this using the 'simulate.lme' function, whose use is illustrated in the seminal work reported in sect. 2.4 of Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). Should we check whether each group has equal variance before using weights=varIdent()? If we should, what is the function for linear mixed model? SG: The general advice I've seen is to avoid excessive overparameterization of heterscedasticity and correlations. However, parsimonious correlation had heterscedasticity models would likely be wise. Years ago, George Box expressed concern about people worrying too much about outliers, which are often fairly obvious and relatively easy to detect, while they worried too little, he thought, about dependence, especially serial dependence, which is generally more difficult to detect and creates bigger problems in inference than outliers. He wrote, Why worry about mice when there are tigers about? SG: Issues of this type can be fairly easily evaluated using 'simulate.lme'. Hope this helps. Spencer Graves Thanks, Shirley On 6/27/07, Simon Blomberg [EMAIL PROTECTED] wrote: The default settings for lme do assume equal variances within groups. You can change that by using the various varClasses. see ?varClasses. A simple example would be to allow unequal variances across groups. So if your call to lme was: lme(...,random=~1|group,...) then to allow each group to have its own variance, use: lme(...,random=~1|group, weights=varIdent(form=~1|group),...) You really really should read Pinheiro Bates (2000). It's all there. HTH, Simon. , On Wed, 2007-06-27 at 21:55 -0400, shirley zhang wrote: Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley __ 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 and provide commented, minimal, self-contained, reproducible code. -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320, Goddard Building (8) T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. __ 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 and provide commented, minimal, self-contained, reproducible code. __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] unequal variance assumption for lme (mixed effect model)
The 'weights' argument on 'lm' is assumed to identify a vector of the same length as the response, giving numbers that are inversely proportional to the variance for each observation. However, 'lm' provides no capability to estimate weights. If you want to do that, the varFunc capabilities in the 'nlme' package is the best tool I know for that purpose. If someone thinks there are better tools available for estimating heterscedasticity, I hope s/he will enlighten us both. Hope this helps. Spencer Graves shirley zhang wrote: Thanks for Spencer and Simon's help. I've got very interesting results based on your suggestions. One more question, how to handle unequal variance problme in lm()? Isn't the weights option also, which means weighted least squares, right? Can you give me an example of setting this parameter in lm() to account for different variance assumption in each group? Thanks again, Shirley On 6/29/07, Spencer Graves [EMAIL PROTECTED] wrote: comments in line shirley zhang wrote: Hi Simon, Thanks for your reply. Your reply reminds me that book. I've read it long time ago, but haven't try the weights option in my projects yet:) Is the heteroscedastic test always less powerful because we have to estimate the within group variance from the given data? SG: In general, I suspect we generally lose power when we estimate more parameters. SG: You can check this using the 'simulate.lme' function, whose use is illustrated in the seminal work reported in sect. 2.4 of Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). Should we check whether each group has equal variance before using weights=varIdent()? If we should, what is the function for linear mixed model? SG: The general advice I've seen is to avoid excessive overparameterization of heterscedasticity and correlations. However, parsimonious correlation had heterscedasticity models would likely be wise. Years ago, George Box expressed concern about people worrying too much about outliers, which are often fairly obvious and relatively easy to detect, while they worried too little, he thought, about dependence, especially serial dependence, which is generally more difficult to detect and creates bigger problems in inference than outliers. He wrote, Why worry about mice when there are tigers about? SG: Issues of this type can be fairly easily evaluated using 'simulate.lme'. Hope this helps. Spencer Graves Thanks, Shirley On 6/27/07, Simon Blomberg [EMAIL PROTECTED] wrote: The default settings for lme do assume equal variances within groups. You can change that by using the various varClasses. see ?varClasses. A simple example would be to allow unequal variances across groups. So if your call to lme was: lme(...,random=~1|group,...) then to allow each group to have its own variance, use: lme(...,random=~1|group, weights=varIdent(form=~1|group),...) You really really should read Pinheiro Bates (2000). It's all there. HTH, Simon. , On Wed, 2007-06-27 at 21:55 -0400, shirley zhang wrote: Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley __ 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 and provide commented, minimal, self-contained, reproducible code. -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320, Goddard Building (8) T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. __ 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 and provide commented, minimal, self-contained, reproducible code. __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] unequal variance assumption for lme (mixed effect model)
comments in line shirley zhang wrote: Hi Simon, Thanks for your reply. Your reply reminds me that book. I've read it long time ago, but haven't try the weights option in my projects yet:) Is the heteroscedastic test always less powerful because we have to estimate the within group variance from the given data? SG: In general, I suspect we generally lose power when we estimate more parameters. SG: You can check this using the 'simulate.lme' function, whose use is illustrated in the seminal work reported in sect. 2.4 of Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). Should we check whether each group has equal variance before using weights=varIdent()? If we should, what is the function for linear mixed model? SG: The general advice I've seen is to avoid excessive overparameterization of heterscedasticity and correlations. However, parsimonious correlation had heterscedasticity models would likely be wise. Years ago, George Box expressed concern about people worrying too much about outliers, which are often fairly obvious and relatively easy to detect, while they worried too little, he thought, about dependence, especially serial dependence, which is generally more difficult to detect and creates bigger problems in inference than outliers. He wrote, Why worry about mice when there are tigers about? SG: Issues of this type can be fairly easily evaluated using 'simulate.lme'. Hope this helps. Spencer Graves Thanks, Shirley On 6/27/07, Simon Blomberg [EMAIL PROTECTED] wrote: The default settings for lme do assume equal variances within groups. You can change that by using the various varClasses. see ?varClasses. A simple example would be to allow unequal variances across groups. So if your call to lme was: lme(...,random=~1|group,...) then to allow each group to have its own variance, use: lme(...,random=~1|group, weights=varIdent(form=~1|group),...) You really really should read Pinheiro Bates (2000). It's all there. HTH, Simon. , On Wed, 2007-06-27 at 21:55 -0400, shirley zhang wrote: Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley __ 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 and provide commented, minimal, self-contained, reproducible code. -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320, Goddard Building (8) T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. __ 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 and provide commented, minimal, self-contained, reproducible code. __ 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 and provide commented, minimal, self-contained, reproducible code.
[R] unequal variance assumption for lme (mixed effect model)
Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] unequal variance assumption for lme (mixed effect model)
The default settings for lme do assume equal variances within groups. You can change that by using the various varClasses. see ?varClasses. A simple example would be to allow unequal variances across groups. So if your call to lme was: lme(...,random=~1|group,...) then to allow each group to have its own variance, use: lme(...,random=~1|group, weights=varIdent(form=~1|group),...) You really really should read Pinheiro Bates (2000). It's all there. HTH, Simon. , On Wed, 2007-06-27 at 21:55 -0400, shirley zhang wrote: Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley __ 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 and provide commented, minimal, self-contained, reproducible code. -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320, Goddard Building (8) T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. __ 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 and provide commented, minimal, self-contained, reproducible code.
Re: [R] unequal variance assumption for lme (mixed effect model)
Hi Simon, Thanks for your reply. Your reply reminds me that book. I've read it long time ago, but haven't try the weights option in my projects yet:) Is the heteroscedastic test always less powerful because we have to estimate the within group variance from the given data? Should we check whether each group has equal variance before using weights=varIdent()? If we should, what is the function for linear mixed model? Thanks, Shirley On 6/27/07, Simon Blomberg [EMAIL PROTECTED] wrote: The default settings for lme do assume equal variances within groups. You can change that by using the various varClasses. see ?varClasses. A simple example would be to allow unequal variances across groups. So if your call to lme was: lme(...,random=~1|group,...) then to allow each group to have its own variance, use: lme(...,random=~1|group, weights=varIdent(form=~1|group),...) You really really should read Pinheiro Bates (2000). It's all there. HTH, Simon. , On Wed, 2007-06-27 at 21:55 -0400, shirley zhang wrote: Dear Douglas and R-help, Does lme assume normal distribution AND equal variance among groups like anova() does? If it does, is there any method like unequal variance T-test (Welch T) in lme when each group has unequal variance in my data? Thanks, Shirley __ 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 and provide commented, minimal, self-contained, reproducible code. -- Simon Blomberg, BSc (Hons), PhD, MAppStat. Lecturer and Consultant Statistician Faculty of Biological and Chemical Sciences The University of Queensland St. Lucia Queensland 4072 Australia Room 320, Goddard Building (8) T: +61 7 3365 2506 email: S.Blomberg1_at_uq.edu.au The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. - John Tukey. __ 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 and provide commented, minimal, self-contained, reproducible code.
