Re: [R] (robust) mixed-effects model with covariate
Dear Thilo, many thanks for your reply. I realized that there was an error in my formula which should have been: aov(y ~ Group * (Time + Age) + Error (Subj/Time), data=df1) or alternatively: lme(RVP.A ~ Group*(Time+Age), random = ~ 1|Subj/Time,data=df1) but I get different results in each case, and different still from the results of another stat program (JMP). The problem is that I am not sure which one (if one indeed is) correct! Also, in the model you proposed: lme(y~Group*Time, random ~ age | Subj, data = df1) it appears that age is not between the effects of interests, so I do not get an estimate of the significance of the Age or the Age*Group effect. I have Pinheiro Bates, and I read the first chapter but it didn't seem to provide an example analogous to my case. Also, it looks like it would take me some months to study the book thoroughly and frankly that seems a bit excessive for such a (apparently?) simple problem I was hoping somebody would magically provide the correct syntax :-) ! thanks again anyway for your help best regards giuseppe Thilo Kellermann wrote: On Monday 24 July 2006 20:16, Giuseppe Pagnoni wrote: Dear all, First of all I apologize if you received this twice: I was checking the archive and I noticed that the text was scrubbed from the message, probably due to some setting in my e-mail program. I am unsure about how to specify a model in R and I thought of asking some advice to the list. I have two groups (Group= A, B) of subjects, with each subject undertaking a test before and after a certain treatment (Time= pre, post). Additionally, I want to enter the age of the subject as a covariate (the performance on the test is affected by age), and I also want to allow different slopes for the effect of age in the two groups of subjects (age might affect the performance of the two groups differentially). Is the right model to use something like the following? aov (y ~ Group*Time + Group*Age + Error(Subj/Group), data=df1 ) (If I enter that command, within summary, I get the following: Error() model is singular in: aov(y ~ Group * Time + Group * Age + Error(Subj/Group), data = df1)) try: aov(y~Group*Time*Age + Error(Subj*Time*Age), data = df1) which specifies an ANOVA (but not with mixed effects) with three main effects and all interaction terms plus an error term that is independent between groups (!) and relates to within subjects variability. For a real mixed effects analysis you should use the (n)lme function from the nlme package and one possible model could look like this: lme(y~Group*Time, random ~ age | Subj, data = df1) but the exact specification depends on your assumptions, in which it is possible to specify two or three models and compare their fits with anova(). For more information on mixed effects you should consult: Jose C. Pinheiro Douglas M. Bates (2000) Mixed-Effects Models in S and S-PLUS. Springer, New York. Good luck, Thilo As a second question: I have an outlier in one of the two groups. The outlier is not due to a measurement error but simply to the performance of the subject (possibly related to his medical history, but I have no way to determine that with certainty). This subject is signaled to be an outlier within its group: averaging the pre and post values for the performance of the subjects in his group, the Grubbs test yields a probability of 0.002 for the subject to be an outlier (the subject is marked as a significant outlier also if I perform the test separately on the pre and the post data). If I remove this subject from its group, I get significant effects of Group and Group X Age (not using the R formula above, but another stat software), but if I leave the subject in those effects disappear. Since I understand that removing outliers is always worrysome, I would like to know if it is possible in R to estimate a model similar to that outlined above but in a resistant/robust fashion, and what would be the actual syntax to do that. I will very much appreciate any help or suggestion about this. thanks in advance and best regards giuseppe -- - Giuseppe Pagnoni Psychiatry and Behavioral Sciences Emory University School of Medicine 1639 Pierce Drive, Suite 4000 Atlanta, GA, 30322 tel: 404.712.8431 fax: 404.727.3233 __ 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] (robust) mixed-effects model with covariate
Dear all, First of all I apologize if you received this twice: I was checking the archive and I noticed that the text was scrubbed from the message, probably due to some setting in my e-mail program. I am unsure about how to specify a model in R and I thought of asking some advice to the list. I have two groups (Group= A, B) of subjects, with each subject undertaking a test before and after a certain treatment (Time= pre, post). Additionally, I want to enter the age of the subject as a covariate (the performance on the test is affected by age), and I also want to allow different slopes for the effect of age in the two groups of subjects (age might affect the performance of the two groups differentially). Is the right model to use something like the following? aov (y ~ Group*Time + Group*Age + Error(Subj/Group), data=df1 ) (If I enter that command, within summary, I get the following: Error() model is singular in: aov(y ~ Group * Time + Group * Age + Error(Subj/Group), data = df1)) As a second question: I have an outlier in one of the two groups. The outlier is not due to a measurement error but simply to the performance of the subject (possibly related to his medical history, but I have no way to determine that with certainty). This subject is signaled to be an outlier within its group: averaging the pre and post values for the performance of the subjects in his group, the Grubbs test yields a probability of 0.002 for the subject to be an outlier (the subject is marked as a significant outlier also if I perform the test separately on the pre and the post data). If I remove this subject from its group, I get significant effects of Group and Group X Age (not using the R formula above, but another stat software), but if I leave the subject in those effects disappear. Since I understand that removing outliers is always worrysome, I would like to know if it is possible in R to estimate a model similar to that outlined above but in a resistant/robust fashion, and what would be the actual syntax to do that. I will very much appreciate any help or suggestion about this. thanks in advance and best regards giuseppe -- - Giuseppe Pagnoni Psychiatry and Behavioral Sciences Emory University School of Medicine 101 Woodruff Circle, Suite 4000 Atlanta, GA, 30322 tel: 404.712.8431 fax: 404.727.3233 __ 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] (robust) mixed-effects model with covariate
Dear all, I am unsure about how to specify a model in R and I thought of asking some advice to the list. I have two groups (Group= A, B) of subjects, with each subject undertaking a test before and after a certain treatment (Time= pre, post). Additionally, I want to enter the age of the subject as a covariate (the performance on the test is affected by age), and I also want to allow different slopes for the effect of age in the two groups of subjects (age might affect the performance of the two groups differentially). Is the right model to use something like the following? aov (y ~ Group*Time + Group*Age + Error(Subj/Group), data=df1 ) (If I enter that command, within summary, I get the following: Error() model is singular in: aov(y ~ Group * Time + Group * Age + Error(Subj/Group), data = df1)) As a second question: I have an outlier in one of the two groups. The outlier is not due to a measurement error but simply to the performance of the subject (possibly related to his medical history, but I have no way to determine that with certainty). This subject is signaled to be an outlier within its group: averaging the pre and post values for the performance of the subjects in his group, the Grubbs test yields a probability of 0.002 for the subject to be an outlier (the subject is marked as a significant outlier also if I perform the test separately on the pre and the post data). If I remove this subject from its group, I get significant effects of Group and Group X Age (not using the R formula above, but another stat software), but if I leave the subject in those effects disappear. Since I understand that removing outliers is always worrysome, I would like to know if it is possible in R to estimate a model similar to that outlined above but in a resistant/robust fashion, and what would be the actual syntax to do that. I will very much appreciate any help or suggestion about this. thanks in advance and best regards giuseppe Giuseppe Pagnoni Dept. of Psychiatry 101 Woodruff Circle Altanta, GA 30322 Tel: 404-712-9582 Fax: 404-727-3233 [[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 and provide commented, minimal, self-contained, reproducible code.
[R] clustering a sparse dissimilarity matrix
Dear all, I have a BIG dissimilarity matrix (around one thousand by one thousand), that I would like to cluster. Most of the elements of the matrix are zero or close to zero. Is there a way to cluster the matrix (hierarchical or partitioning methods) that discards those elements that are close to zero (by using a specified threshold on the matrix)? I am asking this because otherwise I get a huge amount of clutter for singletons or very small clusters. Also, how can you look for clusters of a specified size, apart from looking visually at the dendrogram? Is there a way to bias the algorithm specifically for clusters of a certain size? thank you very much for any suggestion best regards giuseppe PS Note that I cannot use the original data instead of the dissimilarity matrix because those are dissimilarities (computed from the spatial correlation coefficient) between fMRI brain maps, each of which has around 6 variables. -- - Giuseppe Pagnoni Psychiatry and Behavioral Sciences Emory University School of Medicine 1639 Pierce Drive, Suite 4000 Atlanta, GA, 30322 tel: 404.712.8431 fax: 404.727.3233 __ 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
[R] power in a specific frequency band
Dear R users I have a really simple question (hoping for a really simple answer :-): Having estimated the spectral density of a time series x (heart rate data) with: x.pgram - spectrum(x,method=pgram) I would like to compute the power in a specific energy band. Assuming that frequency(x)=4 (Hz), and that I am interested in the band between f1 and f2, is the power in the band simply the following? sum(x.pgram$spec[(x.pgram$freq f1/frequency(x)) (x.pgram$freq = f2/frequency(x))]) If it is so, are the returned units the same units as the original time series, but squared (if x is bpm, then the power is in bpm^2)? I own a copy of Venables and Ripley (MASS 2003), but I was not able to extract this information from the time series chapter thanks for any help (please cc to my e-mail, if possible) giuseppe Giuseppe Pagnoni Dept. Psychiatry and Behavioral Sciences Emory University 1639 Pierce Drive, Suite 4000 Atlanta, GA, 30322 tel: 404.712.8431 fax: 404.727.3233 __ [EMAIL PROTECTED] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
Re: [R] (REPOST) Simple main effects in 2-way repeated measure ANOVA
Dear Prof. Ripley I am sorry that reposting the mail was perceived as not complying to the correct list etiquette: I do not usually frequent the list and I just thought that the message went unnoticed. I apologize for the insistence. On the subject matter of the e-mail, yes, there was a typo, the error term was indeed Error(Subj/Time). But I am still interested in examining the simple main effects, when the interaction of Time and Group is significant: Time: -- ControlGroup: before vs after TreatmentGroup: before vs after Group: -- Before: ControlGroup vs TreatmentGroup After: ControlGroup vs TreatmentGroup Is this incorrect statistical praxis? Other software packages display the results for the above tests after the ANOVA. However, I couldn't find a simple way to extract this information in R. Also, I am not sure how to include a confounding variable, like Age, in the formulation of the model. Thanks for your help giuseppe Prof Brian Ripley wrote: Why has this been REPOSTed? It was delivered last Thursday. On Sun, 8 Aug 2004, Giuseppe Pagnoni wrote: I am running a 2-way repeated measure anova with 1 between-subjects factor (Group=treatment, control), and 1 within-subject factor (Time of measurement: time1, time2). I extract the results of the anova with: summary(aov(effect ~ Group*Time + Error=Subj/Time, data=mydata)) That's not valid syntax for an R formula. Did you mean Error(Subj/Time)? Now, this must be clearly a dumb question, but how can I quickly extract in R all the post-hoc t-tests for the simple main effects? I very much hope you cannot, as you have specified an interaction, and you should not want t-tests for main effects in the presence of an interaction, and certainly not with the default R coding. Did you mean Group + Time? Also, while I am at it, how do I enter in the model a counfounding covariate (e.g., Age)? And on a different matter, is there a way to receive interactive user input in an R script? Something like Enter the name of the factor: , or even more simply Press Enter to see the result of the next analysis ?readline, or cat + scan or ... using connections. -- Giuseppe Pagnoni, Ph.D. Dept. of Psychiatry and Behavioral Sciences 1639 Pierce Drive, Suite 4000 WMB Bldg., Atlanta, GA 30322, U.S. phone: 404-712-8431 fax: 404-727-3233 e-mail: [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] (REPOST) Simple main effects in 2-way repeated measure ANOVA
Hi all I am running a 2-way repeated measure anova with 1 between-subjects factor (Group=treatment, control), and 1 within-subject factor (Time of measurement: time1, time2). I extract the results of the anova with: summary(aov(effect ~ Group*Time + Error=Subj/Time, data=mydata)) Now, this must be clearly a dumb question, but how can I quickly extract in R all the post-hoc t-tests for the simple main effects? Also, while I am at it, how do I enter in the model a counfounding covariate (e.g., Age)? And on a different matter, is there a way to receive interactive user input in an R script? Something like Enter the name of the factor: , or even more simply Press Enter to see the result of the next analysis thanks in advance for any suggestions! giuseppe Giuseppe Pagnoni Dept. Psychiatry and Behavioral Sciences Emory University 1639 Pierce Drive, Suite 4000 Atlanta, GA, 30322 tel: 404.712.8431 fax: 404.727.3233 __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
[R] Post-hoc t-tests in 2-way repeated measure ANOVA
Hi all I am running a 2-way repeated measure anova with 1 between-subjects factor (Group=treatment, control), and 1 within-subject factor (Time of measurement: time1, time2). I extract the results of the anova with: summary(aov(effect ~ Group*Time + Error=Subj/Time, data=mydata)) Now, this must be clearly a dumb question, but how can I quickly extract in R all the post-hoc t-tests for the simple main effects? Also, while I am at it, how do I enter in the model a counfounding covariate (e.g., Age)? And on a different matter, is there a way to receive interactive user input in an R script? Something like Enter the name of the factor: , or even more simply Press Enter to see the result of the next analysis thanks in advance for any suggestions! giuseppe -- Giuseppe Pagnoni, Ph.D. Dept. of Psychiatry and Behavioral Sciences 1639 Pierce Drive, Suite 4000 WMB Bldg., Atlanta, GA 30322, U.S. phone: 404-712-8431 fax: 404-727-3233 e-mail: [EMAIL PROTECTED] __ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
