Re: [R] Syntax for lme function to model random factors and interactions
Hi Joshua, Thank you again for your very-generous help. I think I could follow most of your explanations. To give you some background about myself, I'm doing a PhD at the Institute of Sound Recording (IoSR) at the University of Surrey in the UK. As I have no statistics background, I've been teaching myself the topic using introductory statistics books such as Andy Field's Discovering Statistics using SPSS. We do have a statistician on our campus, but I haven't visited him yet because I was told that I need to be very specific in the type of questions asked. Perhaps it's high time I saw him soon though. I can clarify what the A, B, C and D factors are. Sorry for not doing this before. A and B are components of stimuli used in a listening test. C refers to a subject factor, while D refers to a repetition factor (i.e. multiple evaluations of the same stimulus by the same subject). I've treated A and B as fixed factors because I've assumed their scope extends only within the experiment. Regarding C and D, these were treated as random factors because the former was assumed to generalise to the general population, while the latter was treated as a random factor because a paper which conducted a similar listening test did this. Finally, Y is a perceptual attribute with numerical ratings. I've decided not to include the D factor in my model any more because it was shown to be not statistically significant in an ANOVA. In this case, is the following the correct R code (A and B are fixed factors, C is a random factor, 2- and 3-way interactions are modelled and the maximum log-likeliood is calculated)? lme(Y ~ (A + B)^3, data = myData, random = ~ 1 + B | C, method = ML) I hope the random = ~ 1 + B | C, syntax is correct here because I'm assuming that the subjects do not have independent observations and that the B components of the auditory stimuli are rated differently depending on the subject. Please let me know if otherwise. Originally I wanted to model 2- and 3-way interactions for all three factors, However, in the above model, I've only specified the interactions between A and B. Are the A:C, B:C and A:B:C interactions handled automatically somehow? Best regards, Daisuke -- View this message in context: http://r.789695.n4.nabble.com/Syntax-for-lme-function-to-model-random-factors-and-interactions-tp4630744p4631101.html Sent from the R help mailing list archive at Nabble.com. [[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] Syntax for lme function to model random factors and interactions
See inline On Mon, May 21, 2012 at 11:17 AM, i_like_macs dk...@mac.com wrote: Hello Joshua, Many thanks for your help, especially from a fellow Bruin (I went there as an undergrad!). I understand that there is another forum for mixed models. If my problem can't be solved within this thread, I'll have to go there. I do understand some theory about mixed models, but obviously am far from an expert. My question is not so much statistical advice, as it concerns the correct syntax to include random factors and interactions (which include these random factors) for the lme function. Maybe it's because I'm used to SPSS, but I find R very difficult to use, even after looking up its built-in help. I could run your neat code suggestion: lme(Y ~ (A + B + C + D)^3, data = myData, random = ~ 1 | C, method = ML) but would like to know how to also include D as a random factor. My understanding is that the random argument for the lme function is coded as: ~ x1 + ... + xn | g1 / ... / gm so in this case x1 ... xn are random effects where the effects are allowed to vary across levels of g1 .. gm. where the left side describes the model for random effects, and the right side describes the grouping structure. Reading other posts, I learned that I need both sides for the code to run without errors. However, it's not clear that is correct, the left side is the random effect (random intercepts and/or random slopes), the right side is whatever variable codes the levels that the random effect can vary across. to me what both sides represent. The left side appears to be where the random factors are specified, perhaps like this: random = ~ C + D so this would indicate that C and D are random effects, but you will need something to indicate what they get to vary across. But then this results in errors. Does this mean I have to somehow join the two following lines of code to specify both random factors? random = ~ 1 | C random = ~ 1 | D I do not think that is what you want (and besides I do not think that lme() allows multiple random arguments though I could be wrong because I work with lmer more than lme). It's not clear what the ~ 1 represents here, as I would have guessed that this is where the random factors would be specified. Is this related to an intercept-only model? yes, ~ 1 | C means that there is a random intercept for each level of C. If you do this, you will get an estimate of the average intercept for the intercept in your model, but you will also get an estimate of the variance in intercepts (technically the intercepts are assumed to come from a normal distribution, you will get the mean and variance (or standard deviation) of the maximum likelihood estimate of that distribution). I'm sorry for sounding so lost. This is because I am. Perhaps I need to know more theory of mixed models, but this seems to be possible only if I Understanding more theory would probably help. The Pinheiro and Bates text as wonderful as it is, may not be the easiest place to start. I have not seen you mention anything about a grouping or nesting structure in your data. This may be part of the confusion too. Many uses of mixed models are for that case. A classical example would be students nested within classrooms. In that case, the research question could be does number of hours spent on homework predict grades. The model could look like: grades ~ 1 + homework or in ordinary regression notation instead of R's formula: grades = Xb + e where X is a design matrix the first few rows of which might look like: 1 2 1 2.5 1 3 1 2 the first column being the intercept adn the second the number of hours spent on homework for each student. b will be a vector of coefficients, the first coefficient being the estimated intercept and the second the slope of grades on homework. e is a vector of residuals, that part of grades which cannot be explained by the intercept and homework. The assumption in ordinary regression is that e is identically and independently distributed, but students are within classrooms, and we might guess that in fact, each student was not really an indepedent observation---there is some similarity because they share a classroom. Mixed models address this by adding random effects. Following the above example, we might do: grades ~ 1 + homework random = ~ 1 | ClassroomID this allows the intercepts to randomly vary by classroom, which is sensible---some classes may have more or less skilled students so given that everyone did 0 hours of homework, we still might expect some classrooms to have higher or lower grades. This models that. Now lets say that further, you think that the effects of homework might vary across classrooms. Perhaps for students in very low performing classes, they get an enormous benefit from spending time on homework, whereas in the very high performing classes, their grades only marginally improve for every additional hour of homework.
[R] Syntax for lme function to model random factors and interactions
Hello, I have a question regarding the syntax of the lme function in the nlme package. What I'm trying to do is to calculate an estimate of R^2 based on the likelihood ratio test. For this calculation, I need to determine the maximum log-likelihood of the intercept-only model and the model of interest (with the desired factors and interactions). My model has four independent factors (i.e. A, B, C and D). A and B are fixed factors, whilst C and D are random factors. Presently, I'm trying to code the lme function for the full ANOVA model, but am unsure how to code both random factors. Additionally, I'm unsure whether I coded the interactions correctly (i.e. I'm unsure whether the interactions which contain random factors also need to be specified in the random argument). I've skimmed through the Pinheiro and Bates book, Mixed-Effects Models in S and S-PLUS, but could not find an answer. My R code for the lme function which includes all the main factors and interactions (2- and 3-way) so far is: lme(Y ~ A + B + C + D + A * B + A * C + A * D + B * C + B * D + C * D + A * B * C + A * B * D + A * C * D + B * C * D, data = myData, random = ~ 1|C, method = ML) Can someone help me with how to code the random factors and interactions which contain these random factors please? I apologise in advance, that I am self-taught in statistics, and just started using R. Hence I hope my question made sense. Thank you very much. Daisuke Koya Confused PhD student -- View this message in context: http://r.789695.n4.nabble.com/Syntax-for-lme-function-to-model-random-factors-and-interactions-tp4630744.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] Syntax for lme function to model random factors and interactions
Hi, See inline below On Mon, May 21, 2012 at 6:03 AM, i_like_macs dk...@mac.com wrote: Hello, I have a question regarding the syntax of the lme function in the nlme package. What I'm trying to do is to calculate an estimate of R^2 based on the likelihood ratio test. For this calculation, I need to determine the maximum log-likelihood of the intercept-only model and the model of interest (with the desired factors and interactions). My model has four independent factors (i.e. A, B, C and D). A and B are fixed factors, whilst C and D are random factors. Presently, I'm trying to code the lme function for the full ANOVA model, but am unsure how to code both random factors. Additionally, I'm unsure whether I coded the interactions correctly (i.e. I'm unsure whether the interactions which contain random factors also need to be specified in the random argument). I've skimmed through the Pinheiro and Bates book, Mixed-Effects Models in S and S-PLUS, but could not find an answer. Do they need to be specified for the model to run? No. Do they need to be specified for the model to make sense? That depends on your data, theory, and what question you are trying to answer. I think you would be better off posting this sort of question to R-sig-mixedeffects list. That said, it sounds to me like maybe it would be easiest to spend some more time learning about mixed effects models (I know they can be tricky) and then come back to trying to specify the model in R. We can help with the code aspect, but do not typically provide statistical advice on what is or is not an appropriate model. My R code for the lme function which includes all the main factors and interactions (2- and 3-way) so far is: lme(Y ~ A + B + C + D + A * B + A * C + A * D + B * C + B * D + C * D + A * B * C + A * B * D + A * C * D + B * C * D, data = myData, random = ~ 1|C, method = ML) note that this can be written much more succinctly as: lme(Y ~ (A + B + C + D)^3, data = myData, random = ~ 1 | C, method = ML) which will expand to all three-way interactions and lower terms. Have you tried running this model? Cheers, Josh Can someone help me with how to code the random factors and interactions which contain these random factors please? I apologise in advance, that I am self-taught in statistics, and just started using R. Hence I hope my question made sense. Thank you very much. Daisuke Koya Confused PhD student -- View this message in context: http://r.789695.n4.nabble.com/Syntax-for-lme-function-to-model-random-factors-and-interactions-tp4630744.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. -- Joshua Wiley Ph.D. Student, Health Psychology Programmer Analyst II, Statistical Consulting Group University of California, Los Angeles https://joshuawiley.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] Syntax for lme function to model random factors and interactions
Hello Joshua, Many thanks for your help, especially from a fellow Bruin (I went there as an undergrad!). I understand that there is another forum for mixed models. If my problem can't be solved within this thread, I'll have to go there. I do understand some theory about mixed models, but obviously am far from an expert. My question is not so much statistical advice, as it concerns the correct syntax to include random factors and interactions (which include these random factors) for the lme function. Maybe it's because I'm used to SPSS, but I find R very difficult to use, even after looking up its built-in help. I could run your neat code suggestion: lme(Y ~ (A + B + C + D)^3, data = myData, random = ~ 1 | C, method = ML) but would like to know how to also include D as a random factor. My understanding is that the random argument for the lme function is coded as: ~ x1 + ... + xn | g1 / ... / gm where the left side describes the model for random effects, and the right side describes the grouping structure. Reading other posts, I learned that I need both sides for the code to run without errors. However, it's not clear to me what both sides represent. The left side appears to be where the random factors are specified, perhaps like this: random = ~ C + D But then this results in errors. Does this mean I have to somehow join the two following lines of code to specify both random factors? random = ~ 1 | C random = ~ 1 | D It's not clear what the ~ 1 represents here, as I would have guessed that this is where the random factors would be specified. Is this related to an intercept-only model? I'm sorry for sounding so lost. This is because I am. Perhaps I need to know more theory of mixed models, but this seems to be possible only if I understand what parts of the lme function are. Thank you very much again, Daisuke -- View this message in context: http://r.789695.n4.nabble.com/Syntax-for-lme-function-to-model-random-factors-and-interactions-tp4630744p4630789.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] Syntax for lme function to model random factors and interactions
Inline: On 2012-05-21 11:17, i_like_macs wrote: Hello Joshua, Many thanks for your help, especially from a fellow Bruin (I went there as an undergrad!). I understand that there is another forum for mixed models. If my problem can't be solved within this thread, I'll have to go there. I do understand some theory about mixed models, but obviously am far from an expert. My question is not so much statistical advice, as it concerns the correct syntax to include random factors and interactions (which include these random factors) for the lme function. Maybe it's because I'm used to SPSS, but I find R very difficult to use, even after looking up its built-in help. Just a small comment, since you're interested in correct syntax: you apparently consider A*B to represent the (A,B) interaction term; in R, it's A:B and this *is* clearly documented in the help pages. Peter Ehlers I could run your neat code suggestion: lme(Y ~ (A + B + C + D)^3, data = myData, random = ~ 1 | C, method = ML) but would like to know how to also include D as a random factor. My understanding is that the random argument for the lme function is coded as: ~ x1 + ... + xn | g1 / ... / gm where the left side describes the model for random effects, and the right side describes the grouping structure. Reading other posts, I learned that I need both sides for the code to run without errors. However, it's not clear to me what both sides represent. The left side appears to be where the random factors are specified, perhaps like this: random = ~ C + D But then this results in errors. Does this mean I have to somehow join the two following lines of code to specify both random factors? random = ~ 1 | C random = ~ 1 | D It's not clear what the ~ 1 represents here, as I would have guessed that this is where the random factors would be specified. Is this related to an intercept-only model? I'm sorry for sounding so lost. This is because I am. Perhaps I need to know more theory of mixed models, but this seems to be possible only if I understand what parts of the lme function are. Thank you very much again, Daisuke -- View this message in context: http://r.789695.n4.nabble.com/Syntax-for-lme-function-to-model-random-factors-and-interactions-tp4630744p4630789.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-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.
