Re: [R] what does it mean when my main effect 'disappears' when using lme4?
Hi all, Thanks for the replies (including off list). I have since resolved the discrepant results. I believe it has to do with R's scoping rules - I had an object called 'labs' and a variable in the dataset (DATA) called 'labs', and apparently (to my surprise), when I called this: lmer(Y~X + (1|labs),dataset=DATA) lmer was using the object 'labs' rather than the object 'DATA$labs'. Is this expected behavior?? This would have been fine, except I had reordered DATA in the meantime! Best, JJ On Tue, Aug 17, 2010 at 7:17 PM, Mitchell Maltenfort mmal...@gmail.comwrote: One difference is that the random effect in lmer is assumed -- implicitly constrained, as I understand it -- to be a bell curve. The fixed effect model does not have that constraint. How are the values of labs effects distributed in your lm model? On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson johan.h.jack...@gmail.com wrote: Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non-significant (|t| 1.5). What might this be telling me about my data? I guess the second (X|labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped for joy, but now I'm muddled and uncertain if I'm overlooking something. Is there still room for optimism (with respect to X affecting Y)? JJ [[alternative HTML version deleted]] __ R-help@r-project.org 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. [[alternative HTML version deleted]] __ R-help@r-project.org 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] what does it mean when my main effect 'disappears' when using lme4?
On Aug 18, 2010, at 1:19 PM, Johan Jackson wrote: Hi all, Thanks for the replies (including off list). I have since resolved the discrepant results. I believe it has to do with R's scoping rules - I had an object called 'labs' and a variable in the dataset (DATA) called 'labs', and apparently (to my surprise), when I called this: lmer(Y~X + (1|labs),dataset=DATA) lmer was using the object 'labs' rather than the object 'DATA$labs'. Is this expected behavior?? help(lmer, package=lme4) It would be if you use the wrong data argument for lmer(). I doubt that the argument dataset would result in lmer processing DATA. My guess is that the function also accessed objects Y and X from the calling environment rather than from within DATA. This would have been fine, except I had reordered DATA in the meantime! Best, JJ On Tue, Aug 17, 2010 at 7:17 PM, Mitchell Maltenfort mmal...@gmail.com wrote: One difference is that the random effect in lmer is assumed -- implicitly constrained, as I understand it -- to be a bell curve. The fixed effect model does not have that constraint. How are the values of labs effects distributed in your lm model? On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson johan.h.jack...@gmail.com wrote: Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non- significant (|t| 1.5). What might this be telling me about my data? I guess the second (X| labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped for joy, but now I'm muddled and uncertain if I'm overlooking something. Is there still room for optimism (with respect to X affecting Y)? JJ [[alternative HTML version deleted]] __ R-help@r-project.org 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. [[alternative HTML version deleted]] __ R-help@r-project.org 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. David Winsemius, MD West Hartford, CT __ R-help@r-project.org 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] what does it mean when my main effect 'disappears' when using lme4?
No, apologies (good catch David!), I merely copied the script incorrectly. It was lmer(Y~X + (1|labs),data=DATA) in my original script. So my question still stands: is it expected behavior for lmer to access the object 'labs' rather than the object 'DATA$labs' when using the data= argument? JJ On Wed, Aug 18, 2010 at 11:29 AM, David Winsemius dwinsem...@comcast.netwrote: On Aug 18, 2010, at 1:19 PM, Johan Jackson wrote: Hi all, Thanks for the replies (including off list). I have since resolved the discrepant results. I believe it has to do with R's scoping rules - I had an object called 'labs' and a variable in the dataset (DATA) called 'labs', and apparently (to my surprise), when I called this: lmer(Y~X + (1|labs),dataset=DATA) lmer was using the object 'labs' rather than the object 'DATA$labs'. Is this expected behavior?? help(lmer, package=lme4) It would be if you use the wrong data argument for lmer(). I doubt that the argument dataset would result in lmer processing DATA. My guess is that the function also accessed objects Y and X from the calling environment rather than from within DATA. This would have been fine, except I had reordered DATA in the meantime! Best, JJ On Tue, Aug 17, 2010 at 7:17 PM, Mitchell Maltenfort mmal...@gmail.com wrote: One difference is that the random effect in lmer is assumed -- implicitly constrained, as I understand it -- to be a bell curve. The fixed effect model does not have that constraint. How are the values of labs effects distributed in your lm model? On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson johan.h.jack...@gmail.com wrote: Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non-significant (|t| 1.5). What might this be telling me about my data? I guess the second (X|labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped for joy, but now I'm muddled and uncertain if I'm overlooking something. Is there still room for optimism (with respect to X affecting Y)? JJ [[alternative HTML version deleted]] __ R-help@r-project.org 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. [[alternative HTML version deleted]] __ R-help@r-project.org 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. David Winsemius, MD West Hartford, CT [[alternative HTML version deleted]] __ R-help@r-project.org 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] what does it mean when my main effect 'disappears' when using lme4?
On 2010-08-18 11:49, Johan Jackson wrote: No, apologies (good catch David!), I merely copied the script incorrectly. It was lmer(Y~X + (1|labs),data=DATA) in my original script. So my question still stands: is it expected behavior for lmer to access the object 'labs' rather than the object 'DATA$labs' when using the data= argument? JJ I don't think that's expected behaviour, nor do I think that it occurs. There must be something else going on. Can you produce this with a small reproducible example? -Peter Ehlers On Wed, Aug 18, 2010 at 11:29 AM, David Winsemiusdwinsem...@comcast.netwrote: On Aug 18, 2010, at 1:19 PM, Johan Jackson wrote: Hi all, Thanks for the replies (including off list). I have since resolved the discrepant results. I believe it has to do with R's scoping rules - I had an object called 'labs' and a variable in the dataset (DATA) called 'labs', and apparently (to my surprise), when I called this: lmer(Y~X + (1|labs),dataset=DATA) lmer was using the object 'labs' rather than the object 'DATA$labs'. Is this expected behavior?? help(lmer, package=lme4) It would be if you use the wrong data argument for lmer(). I doubt that the argument dataset would result in lmer processing DATA. My guess is that the function also accessed objects Y and X from the calling environment rather than from within DATA. This would have been fine, except I had reordered DATA in the meantime! Best, JJ On Tue, Aug 17, 2010 at 7:17 PM, Mitchell Maltenfortmmal...@gmail.com wrote: One difference is that the random effect in lmer is assumed -- implicitly constrained, as I understand it -- to be a bell curve. The fixed effect model does not have that constraint. How are the values of labs effects distributed in your lm model? On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson johan.h.jack...@gmail.com wrote: Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non-significant (|t| 1.5). What might this be telling me about my data? I guess the second (X|labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped for joy, but now I'm muddled and uncertain if I'm overlooking something. Is there still room for optimism (with respect to X affecting Y)? JJ [[alternative HTML version deleted]] __ R-help@r-project.org 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. [[alternative HTML version deleted]] __ R-help@r-project.org 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. David Winsemius, MD West Hartford, CT __ R-help@r-project.org 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] what does it mean when my main effect 'disappears' when using lme4?
On Aug 18, 2010, at 6:45 PM, Peter Ehlers wrote: On 2010-08-18 11:49, Johan Jackson wrote: No, apologies (good catch David!), I merely copied the script incorrectly. It was lmer(Y~X + (1|labs),data=DATA) in my original script. So my question still stands: is it expected behavior for lmer to access the object 'labs' rather than the object 'DATA $labs' when using the data= argument? JJ I don't think that's expected behaviour, nor do I think that it occurs. There must be something else going on. Can you produce this with a small reproducible example? This makes me wonder if there couldn't be a Wiki page where questioners could be referred that would illustrate the quick and easy construction of examples that could test such theories? I would imagine that in (this instance) the page would start with the data.frame that were on the help page for lmer() (for example) and then put in the workspace a mangled copy of a vector that migh exhibit the pathological structure that might exist in the OP's version of labs and then run lmer() to see if such an unexpected behavior might be exhibited. Just an idea. (I've never managed to get any R-Wiki contributions accepted through the gauntlet that it puts up.) -- David. -Peter Ehlers On Wed, Aug 18, 2010 at 11:29 AM, David Winsemiusdwinsem...@comcast.net wrote: On Aug 18, 2010, at 1:19 PM, Johan Jackson wrote: Hi all, Thanks for the replies (including off list). I have since resolved the discrepant results. I believe it has to do with R's scoping rules - I had an object called 'labs' and a variable in the dataset (DATA) called 'labs', and apparently (to my surprise), when I called this: lmer(Y~X + (1|labs),dataset=DATA) lmer was using the object 'labs' rather than the object 'DATA $labs'. Is this expected behavior?? help(lmer, package=lme4) It would be if you use the wrong data argument for lmer(). I doubt that the argument dataset would result in lmer processing DATA. My guess is that the function also accessed objects Y and X from the calling environment rather than from within DATA. This would have been fine, except I had reordered DATA in the meantime! Best, JJ On Tue, Aug 17, 2010 at 7:17 PM, Mitchell Maltenfortmmal...@gmail.com wrote: One difference is that the random effect in lmer is assumed -- implicitly constrained, as I understand it -- to be a bell curve. The fixed effect model does not have that constraint. How are the values of labs effects distributed in your lm model? On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson johan.h.jack...@gmail.com wrote: Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non- significant (|t| 1.5). What might this be telling me about my data? I guess the second (X|labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped for joy, but now I'm muddled and uncertain if I'm overlooking something. Is there still room for optimism (with respect to X affecting Y)? JJ [[alternative HTML version deleted]] __ R-help@r-project.org 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. [[alternative HTML version deleted]] __ R-help@r-project.org 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. David Winsemius, MD West Hartford, CT __ R-help@r-project.org 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,
Re: [R] what does it mean when my main effect 'disappears' when using lme4?
On 2010-08-18 18:41, Johan Jackson wrote: Hi all, I figured out why this was happening. It is because my actual code was: lmer(Y~X + (1|as.factor(labs)),data=DATA) In this case, the as.factor function looks for object 'labs' not object 'DATA$labs.' Scope is something you hear about don't worry about until it bites you on your ass I guess. JJ Now I agree with you and I don't think that lmer() should do that. Confirmed using the sleepstudy data: library(lme4) # lme4_0.999375-34 Matrix_0.999375-42 sleepstudy$subj - rep(1:18, each=10) fm - lmer(Reaction ~ Days + (1|as.factor(subj)), data=sleepstudy) # Error in inherits(x, factor) : object 'subj' not found and, of course, if you have a variable 'subj' in your workspace, then that will be used. It appears that as.factor() takes precedence over 'data=', as you surmise. I haven't had time to look into the lmer code to see what gives and it may well be a design decision that I'm not aware of. I can't see anything in the help page that refers to this effect. -Peter Ehlers On Wed, Aug 18, 2010 at 5:52 PM, David Winsemiusdwinsem...@comcast.netwrote: On Aug 18, 2010, at 6:45 PM, Peter Ehlers wrote: On 2010-08-18 11:49, Johan Jackson wrote: No, apologies (good catch David!), I merely copied the script incorrectly. It was lmer(Y~X + (1|labs),data=DATA) in my original script. So my question still stands: is it expected behavior for lmer to access the object 'labs' rather than the object 'DATA$labs' when using the data= argument? JJ I don't think that's expected behaviour, nor do I think that it occurs. There must be something else going on. Can you produce this with a small reproducible example? This makes me wonder if there couldn't be a Wiki page where questioners could be referred that would illustrate the quick and easy construction of examples that could test such theories? I would imagine that in (this instance) the page would start with the data.frame that were on the help page for lmer() (for example) and then put in the workspace a mangled copy of a vector that migh exhibit the pathological structure that might exist in the OP's version of labs and then run lmer() to see if such an unexpected behavior might be exhibited. Just an idea. (I've never managed to get any R-Wiki contributions accepted through the gauntlet that it puts up.) -- David. -Peter Ehlers On Wed, Aug 18, 2010 at 11:29 AM, David Winsemiusdwinsem...@comcast.net wrote: On Aug 18, 2010, at 1:19 PM, Johan Jackson wrote: Hi all, Thanks for the replies (including off list). I have since resolved the discrepant results. I believe it has to do with R's scoping rules - I had an object called 'labs' and a variable in the dataset (DATA) called 'labs', and apparently (to my surprise), when I called this: lmer(Y~X + (1|labs),dataset=DATA) lmer was using the object 'labs' rather than the object 'DATA$labs'. Is this expected behavior?? help(lmer, package=lme4) It would be if you use the wrong data argument for lmer(). I doubt that the argument dataset would result in lmer processing DATA. My guess is that the function also accessed objects Y and X from the calling environment rather than from within DATA. This would have been fine, except I had reordered DATA in the meantime! Best, JJ On Tue, Aug 17, 2010 at 7:17 PM, Mitchell Maltenfortmmal...@gmail.com wrote: One difference is that the random effect in lmer is assumed -- implicitly constrained, as I understand it -- to be a bell curve. The fixed effect model does not have that constraint. How are the values of labs effects distributed in your lm model? On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson johan.h.jack...@gmail.com wrote: Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non-significant (|t| 1.5). What might this be telling me about my data? I guess the second (X|labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped
Re: [R] what does it mean when my main effect 'disappears' when using lme4?
Hi all, I figured out why this was happening. It is because my actual code was: lmer(Y~X + (1|as.factor(labs)),data=DATA) In this case, the as.factor function looks for object 'labs' not object 'DATA$labs.' Scope is something you hear about don't worry about until it bites you on your ass I guess. JJ On Wed, Aug 18, 2010 at 5:52 PM, David Winsemius dwinsem...@comcast.netwrote: On Aug 18, 2010, at 6:45 PM, Peter Ehlers wrote: On 2010-08-18 11:49, Johan Jackson wrote: No, apologies (good catch David!), I merely copied the script incorrectly. It was lmer(Y~X + (1|labs),data=DATA) in my original script. So my question still stands: is it expected behavior for lmer to access the object 'labs' rather than the object 'DATA$labs' when using the data= argument? JJ I don't think that's expected behaviour, nor do I think that it occurs. There must be something else going on. Can you produce this with a small reproducible example? This makes me wonder if there couldn't be a Wiki page where questioners could be referred that would illustrate the quick and easy construction of examples that could test such theories? I would imagine that in (this instance) the page would start with the data.frame that were on the help page for lmer() (for example) and then put in the workspace a mangled copy of a vector that migh exhibit the pathological structure that might exist in the OP's version of labs and then run lmer() to see if such an unexpected behavior might be exhibited. Just an idea. (I've never managed to get any R-Wiki contributions accepted through the gauntlet that it puts up.) -- David. -Peter Ehlers On Wed, Aug 18, 2010 at 11:29 AM, David Winsemiusdwinsem...@comcast.net wrote: On Aug 18, 2010, at 1:19 PM, Johan Jackson wrote: Hi all, Thanks for the replies (including off list). I have since resolved the discrepant results. I believe it has to do with R's scoping rules - I had an object called 'labs' and a variable in the dataset (DATA) called 'labs', and apparently (to my surprise), when I called this: lmer(Y~X + (1|labs),dataset=DATA) lmer was using the object 'labs' rather than the object 'DATA$labs'. Is this expected behavior?? help(lmer, package=lme4) It would be if you use the wrong data argument for lmer(). I doubt that the argument dataset would result in lmer processing DATA. My guess is that the function also accessed objects Y and X from the calling environment rather than from within DATA. This would have been fine, except I had reordered DATA in the meantime! Best, JJ On Tue, Aug 17, 2010 at 7:17 PM, Mitchell Maltenfortmmal...@gmail.com wrote: One difference is that the random effect in lmer is assumed -- implicitly constrained, as I understand it -- to be a bell curve. The fixed effect model does not have that constraint. How are the values of labs effects distributed in your lm model? On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson johan.h.jack...@gmail.com wrote: Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non-significant (|t| 1.5). What might this be telling me about my data? I guess the second (X|labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped for joy, but now I'm muddled and uncertain if I'm overlooking something. Is there still room for optimism (with respect to X affecting Y)? JJ [[alternative HTML version deleted]] __ R-help@r-project.org 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. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide
[R] what does it mean when my main effect 'disappears' when using lme4?
Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non-significant (|t| 1.5). What might this be telling me about my data? I guess the second (X|labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped for joy, but now I'm muddled and uncertain if I'm overlooking something. Is there still room for optimism (with respect to X affecting Y)? JJ [[alternative HTML version deleted]] __ R-help@r-project.org 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] what does it mean when my main effect 'disappears' when using lme4?
One difference is that the random effect in lmer is assumed -- implicitly constrained, as I understand it -- to be a bell curve. The fixed effect model does not have that constraint. How are the values of labs effects distributed in your lm model? On Tue, Aug 17, 2010 at 8:50 PM, Johan Jackson johan.h.jack...@gmail.com wrote: Hello, Setup: I have data with ~10K observations. Observations come from 16 different laboratories (labs). I am interested in how a continuous factor, X, affects my dependent variable, Y, but there are big differences in the variance and mean across labs. I run this model, which controls for mean but not variance differences between the labs: lm(Y ~ X + as.factor(labs)). The effect of X is highly significant (p .1) I then run this model using lme4: lmer(Y~ X + (1|labs)) #controls for mean diffs bw labs lmer(Y~X + (X|labs)) #and possible slope heterogeneity bw labs. For both of these latter models, the effect of X is non-significant (|t| 1.5). What might this be telling me about my data? I guess the second (X|labs) may tell me that there are big differences in the slope across labs, and that the slope isn't significant against the backdrop of 16 slopes that differ quite a bit between each other. Is that right? (Still, the enormous drop in p-value is surprising!). I'm not clear on why the first (1|labs), however, is so discrepant from just controlling for the mean effects of labs. Any help in interpreting these data would be appreciated. When I first saw the data, I jumped for joy, but now I'm muddled and uncertain if I'm overlooking something. Is there still room for optimism (with respect to X affecting Y)? JJ [[alternative HTML version deleted]] __ R-help@r-project.org 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@r-project.org 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.
