Re: [R] Homogeneity of regression slopes
That's good insight, and gives me some good ideas for what direction to this. Thanks everyone ! Doug P.S. - I guess if you have a significant interaction, that implies the slopes of the individual regression lines are significantly different anyway, doesn't it... On Tue, Sep 14, 2010 at 11:33 AM, Thomas Stewart tgstew...@gmail.com wrote: If you are interested in exploring the homogeneity of variance assumption, I would suggest you model the variance explicitly. Doing so allows you to compare the homogeneous variance model to the heterogeneous variance model within a nested model framework. In that framework, you'll have likelihood ratio tests, etc. This is why I suggested the nlme package and the gls function. The gls function allows you to model the variance. -tgs P.S. WLS is a type of GLS. P.P.S It isn't clear to me how a variance stabilizing transformation would help in this case. On Tue, Sep 14, 2010 at 6:53 AM, Clifford Long gnolff...@gmail.com wrote: Hi Thomas, Thanks for the additional information. Just wondering, and hoping to learn ... would any lack of homogeneity of variance (which is what I believe you mean by different stddev estimates) be found when performing standard regression diagnostics, such as residual plots, Levene's test (or equivalent), etc.? If so, then would a WLS routine or some type of variance stabilizing transformation be useful? Again, hoping to learn. I'll check out the gls() routine in the nlme package, as you mentioned. Thanks. Cliff On Mon, Sep 13, 2010 at 10:02 PM, Thomas Stewart tgstew...@gmail.com wrote: Allow me to add to Michael's and Clifford's responses. If you fit the same regression model for each group, then you are also fitting a standard deviation parameter for each model. The solution proposed by Michael and Clifford is a good one, but the solution assumes that the standard deviation parameter is the same for all three models. You may want to consider the degree by which the standard deviation estimates differ for the three separate models. If they differ wildly, the method described by Michael and Clifford may not be the best. Rather, you may want to consider gls() in the nlme package to explicitly allow the variance parameters to vary. -tgs On Mon, Sep 13, 2010 at 4:52 PM, Doug Adams f...@gmx.com wrote: Hello, We've got a dataset with several variables, one of which we're using to split the data into 3 smaller subsets. (as the variable takes 1 of 3 possible values). There are several more variables too, many of which we're using to fit regression models using lm. So I have 3 models fitted (one for each subset of course), each having slope estimates for the predictor variables. What we want to find out, though, is whether or not the overall slopes for the 3 regression lines are significantly different from each other. Is there a way, in R, to calculate the overall slope of each line, and test whether there's homogeneity of regression slopes? (Am I using that phrase in the right context -- comparing the slopes of more than one regression line rather than the slopes of the predictors within the same fit.) I hope that makes sense. We really wanted to see if the predicted values at the ends of the 3 regression lines are significantly different... But I'm not sure how to do the Johnson-Neyman procedure in R, so I think testing for slope differences will suffice! Thanks to any who may be able to help! Doug Adams __ 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. __ 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] Homogeneity of regression slopes
Hello, We've got a dataset with several variables, one of which we're using to split the data into 3 smaller subsets. (as the variable takes 1 of 3 possible values). There are several more variables too, many of which we're using to fit regression models using lm. So I have 3 models fitted (one for each subset of course), each having slope estimates for the predictor variables. What we want to find out, though, is whether or not the overall slopes for the 3 regression lines are significantly different from each other. Is there a way, in R, to calculate the overall slope of each line, and test whether there's homogeneity of regression slopes? (Am I using that phrase in the right context -- comparing the slopes of more than one regression line rather than the slopes of the predictors within the same fit.) I hope that makes sense. We really wanted to see if the predicted values at the ends of the 3 regression lines are significantly different... But I'm not sure how to do the Johnson-Neyman procedure in R, so I think testing for slope differences will suffice! Thanks to any who may be able to help! Doug Adams __ 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] aov - subjects nested within groups crossed with questions
Hello, I was going to use lmer() on this data, but it seemed easier -- and more importantly, more meaningful -- to just analyze smaller sections of it individually. I'd like to ask for help to see if I'm analyzing the separate parts correctly. Each part is the same, and they all look like this: __ question 1 question 2 question 3 question 4 group 1 subject 1 # # # # subject 2 # # # # subject 3 # # # # subject 4 # # # # subject 5 # # # # subject 6 # # # # subject 7 # # # # subject 8 # # # # subject 9 # # # # subject 10 # # # # group 2 subject 11 # # # # subject 12 # # # # subject 13 # # # # subject 14 # # # # subject 15 # # # # subject 16 # # # # subject 17 # # # # subject 18 # # # # subject 19 # # # # subject 20 # # # # ¯¯ This is the call to aov I used: aov (response ~ (group*question) + Error(person/question), data) ...and this is the output I get: __ Error: person Df Sum Sq Mean Sq F value Pr(F) group 1 6.086 6.0860 7.2069 0.008867 ** question 1 0.720 0.7199 0.8525 0.358696 Residuals 78 65.869 0.8445 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Error: person:question Df Sum Sq Mean Sq F valuePr(F) question1 18.014 18.0144 24.7920 3.671e-06 *** group:question 1 0.004 0.0041 0.0057 0.94 Residuals 79 57.403 0.7266 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Error: Within Df Sum Sq Mean Sq F value Pr(F) Residuals 483 390.78 0.80907 ¯¯ If I want to test for group difference, am I looking at the F value for group under Error: person or does that section of the output not take into account the entire variance structure I should be acknowledging? Does my aov syntax seem appropriate in the first place? Thanks everyone very much for any help you can give, Doug Adams question 1 question 2 question 3 question 4 group 1 subject 1 # # # # subject 2 # # # # subject 3 # # # # subject 4 # # # # subject 5 # # # # subject 6 # # # # subject 7 # # # # subject 8 # # # # subject 9 # # # # subject 10 # # # # group 2 subject 11 # # # # subject 12 # # # # subject 13 # # # # subject 14 # # # # subject 15 # # # # subject 16 # # # # subject 17 # # # # subject 18
Re: [R] lmer, mcmcsamp, coda, HPDinterval
Ah, that did it. Thank you! - Doug Adams MStat Student University of Utah -- View this message in context: http://n4.nabble.com/lmer-mcmcsamp-coda-HPDinterval-tp1457803p1459380.html Sent from the R help mailing list archive at Nabble.com. __ 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] lmer, mcmcsamp, coda, HPDinterval
Hi, I've got a linear mixed model created using lmer: A6mlm - lmer(Score ~ division + (1|school), data=Age6m) (To those of you to whom this model looks familiar, thanks for your patience with this my other questions.) Anyway, I was trying this to look at the significance of my fixed effects: A6post - mcmcsamp(A6mlm, 5) library(coda) HPDinterval(A6post) ..but I got this message: no applicable method for 'HPDinterval' applied to an object of class merMCMC Should I be coercing A6post to another type, or am I missing other steps altogether? Thanks :) Doug Adams - Doug Adams MStat Student University of Utah -- View this message in context: http://n4.nabble.com/lmer-mcmcsamp-coda-HPDinterval-tp1457803p1457803.html Sent from the R help mailing list archive at Nabble.com. __ 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] Hierarchical Linear Model using lme4's lmer
Hehe (about the kitchen sink) Thanks very much to all three of you. Douglas Bates-2 wrote: On Sat, Jan 16, 2010 at 8:20 AM, Walmes Zeviani walmeszevi...@hotmail.com wrote: Doug, It appears you are mixing nlme and lme4 formulation type. On nlme library you type lme(y~x, random=~1|subjetc) On lme4 library you type lmer(y~x+(1|subject)) You mixed them. At your disposal. Which is what I tell my wife when I am standing by our sink. Walmes. Doug Adams wrote: Hi, I was wondering: I've got a dataset where I've got student 'project's nested within 'school's, and 'division' (elementary, junior, or senior) at the student project level. (Division is at the student level and not nested within schools because some students are registered as juniors others as seniors within the same school.) So schools are random, division is fixed, and the student Score is the outcome variable. This is what I've tried: lmer(data=Age6m, Score ~ division + (1|school), random=~1 | school) Am I on the right track? Thanks everyone, :) Doug Adams MStat Student University of Utah Walmes is correct that this is mixing two formulations of the model. It turns out that the model will be fit correctly anyway. The lmer function has a ... argument which will silently swallow the argument random = ~ 1|school and ignore it. Looks like we should add a check for specification of a random argument and provide a warning if it is present. __ 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. - Doug Adams MStat Student University of Utah -- View this message in context: http://n4.nabble.com/Hierarchical-Linear-Model-using-lme4-s-lmer-tp1015485p1015916.html Sent from the R help mailing list archive at Nabble.com. __ 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] Hierarchical Linear Model using lme4's lmer
Hi, I was wondering: I've got a dataset where I've got student 'project's nested within 'school's, and 'division' (elementary, junior, or senior) at the student project level. (Division is at the student level and not nested within schools because some students are registered as juniors others as seniors within the same school.) So schools are random, division is fixed, and the student Score is the outcome variable. This is what I've tried: lmer(data=Age6m, Score ~ division + (1|school), random=~1 | school) Am I on the right track? Thanks everyone, :) Doug Adams MStat Student University of Utah __ 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.
