Finding the optimal structure of fixed / random effects in a 3 way full factoria

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This topic contains 7 replies, has 2 voices, and was last updated by  blazej 1 week, 6 days ago.

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  • #255

    blazej
    Participant

    Greetings!
    Disclaimer: this is a rephrased question posted also at CrossValidated

    I’m looking for help in finding the best / optimal structure of fixed and random effects in my repeated measures experiment data along with an explanation on why do I choose one solution over the other.

    My experiment was investigating the reaction times of Yes/No judgments regarding two different targets (self vs other) on two different aspects (feelings vs behavior) in two different time perspectives (recent vs distant).

    Each of the 3 factors (target, aspect, time) was a within subjects factor with fully counterbalanced order between subjects.

    Each participant made a total of 128 judgements (on a Yes / No scale) – number of trials breaks down to 16 repetitions for every 2x2x2 (target * mode * time) combination. Each question (judgment) addressed a unique trait (happy, sad etc), but traits itself are not interesting to me.

    The important part is that for every combination of within subject factors there is a total of 16 judgments (repetitions?) and that the dependend variable is latency of the judgment controlled at all 3 experimental factors + response type (was it Yes or was it No? – yes judgments are generally faster and I’d like to account for that) and trial number (responses become faster as the procedure advances and I’d like to control that too).

    In previous research I used a regular GLM repeated measures in SPSS to analyze data averaged across factors.
    Here I’ve been advised by a colleague reviewer to shift toward multilevel models (HLM, MLM, LMEM) as this can help me with:

    Controlling for response type (Yes vs No) which would result in a lot of missing cells when RTs are collapsed across all factors + response type
    Controlling for the fact, that extremely low latencies (<200msec) are considered invalid and dropped from the analysis anyway.

    Eventually I’d like to report anova-like type III F’s (or Chi2) of all main effects and their interactions along with the possibility to test for simple main effects equivalents if a 3-way interaction comes up to be significant.

    To simulate data for 100 (in reality I have data for 244 subjects and a total of 28124 rows) participants representing a structure I will work with I came up with this code:

    library(AlgDesign) #for generating a factorial design)
    df <-gen.factorial(c(16,2,2,2,100), factors = "all", varNames = c("rep", "A", "B", "C", "Subject"))
    df$rep <- as.numeric(df$rep)
    df$Subject <- as.numeric(df$Subject)
    logRT <- rnorm(n=12800, m=7, sd=1) # outcome variable
    trialno<- rep(1:128, times = 100)
    response<- rbinom(12800, size = 1, prob = c(0.3, 0.7)) 
    df <- cbind(df, logRT, trialno, response)

    The output (main analysis) I’d like to test is a full factorial of 2x2x2 within factors on latency data, corrected for trial number at subject level.
    Then I want to introduce response (yes/no) as a 4th factor and again run a full factorial on a unbalanced design.
    Analyze simple main effects within significant interactions and report estimated marginal means.

    Otherwise any comments or full answers on best practices with my data structure are really more than welcome.

    Thank you very much.

  • #256

    blazej
    Participant

    As a follow up here is what I tested so far:

    The main analysis – a balanced full factorial of 2x2x2 factors mixed(logRT ~ A * B * C + (A * B * C | Subject), df)

    Can I assume that this is a mixed model “equivalent” of a “normal” repeated measure anova (as done with SPSS) computed on aggregated data (wide format with one mean logRT for each combination of A * B * C) ?
    If not, what syntax would give me something similar (conceptually), considering natural differences between analysis (mixed vs rm anova) ?

    How can I extend the above syntax to account for trialnumber (a tendency to have a negative correlation between trialno and logRT)?

    What would be considered “best practice” if I wanted to add response as a separate factor making it a A * B * C * response design and considering that adding response breaks the balance of A*B*C? I’m not sure how to approach both fixed and random parts for this.

    Should I consider any non default covariance structures for both random and fixed effects? I’ve been advised to use compound symmetry for the fixed part – but to be honest I don’t know any reason for it, nor I can explain differences between various structures (avaible for example in SPSS procedure MIXED)

    I’m really stuck here and would love to avoid guessing, eventually describing my results and analysis with decent understanding of the theoretical background of what I did.

    Thank you 🙂

    • This reply was modified 2 weeks, 1 day ago by  blazej.
    • This reply was modified 2 weeks, 1 day ago by  blazej.
  • #259

    henrik
    Keymaster

    There are many questions here. In general, I would advise you to read our introductory chapter that covers quite a few of them: http://singmann.org/download/publications/singmann_kellen-introduction-mixed-models.pdf

    Can I assume that this is a mixed model “equivalent” of a “normal” repeated measure anova (as done with SPSS) computed on aggregated data (wide format with one mean logRT for each combination of A * B * C) ?

    This is of course a different model that will produce different results, but this is probably the recommended model (if you ignore response and trialno). For the within-subject design this is the “maximal model justified by the design”, which is recommended following Barr, Levy, Scheeperes and Tily (2013, JML).

    How can I extend the above syntax to account for trialnumber (a tendency to have a negative correlation between trialno and logRT)?
    What would be considered “best practice” if I wanted to add response as a separate factor making it a A * B * C * response design and considering that adding response breaks the balance of A*B*C? I’m not sure how to approach both fixed and random parts for this

    Let us first consider response. As each participant should have (in principle) both yes and no responses for each cell of the design, the natural way to extend this model would be to add response as both fixed and random:
    mixed(logRT ~ A * B * C * response + (A * B * C * response | Subject), df)
    I do not immediately see how the issue of imbalance is a huge problem. Of course, balance would be better, but the model should be able to deal with it.
    The question of trialno is more difficult. Of cxourse you could simply add this as fixed and random effect as well, for example:
    mixed(logRT ~ A * B * C * response + trialno + (A * B * C * response + trialno | Subject), df)
    However, this would only allow a linear effect. Maybe some other functional form is better. If this is important another type of model such as a GAMM may be better. See:
    Baayen, H., Vasishth, S., Kliegl, R., & Bates, D. (2017). The cave of shadows: Addressing the human factor with generalized additive mixed models. Journal of Memory and Language, 94, 206–234. https://doi.org/10.1016/j.jml.2016.11.006

    Should I consider any non default covariance structures for both random and fixed effects? I’ve been advised to use compound symmetry for the fixed part – but to be honest I don’t know any reason for it, nor I can explain differences between various structures (avaible for example in SPSS procedure MIXED)

    Unfortunately, this is at the moment not really possible with lme4 (which mixed uses).

    One final thought. Your model seems to have quite a lot of data, in this case method = "S" is probably indicated in the call to mixed(). It is faster and very similar.

    Hope that helps.

  • #260

    blazej
    Participant

    Henrik, thank you very much for the links and some clarifications.

    A couple of remarks:

    This is of course a different model that will produce different results, but this is probably the recommended model (if you ignore response and trialno). For the within-subject design this is the “maximal model justified by the design”, which is recommended following Barr, Levy, Scheeperes and Tily (2013, JML).

    Of course, I’m aware that this is a different model. What I wanted to say, that I’m after something as close (conceptually) as possible to a rmAnova.
    Is it common to reduce the maximal model based on results? I’m not that familiar with any agreement what to do and what to avoid in order to get results allowing tests of interactions within factors of a psychological (social cognitive) experiment.

    Let us first consider response. As each participant should have (in principle) both yes and no responses for each cell of the design, the natural way to extend this model would be to add response as both fixed and random:
    mixed(logRT ~ A * B * C * response + (A * B * C * response | Subject), df)
    I do not immediately see how the issue of imbalance is a huge problem. Of course, balance would be better, but the model should be able to deal with it.

    That’s clear and I already tried this analysis. Unfortunatly I’m getting convergence errors and don’t really know how to deal with them.
    I mentioned imbalance as the way I see it (mostly SPSS rm Anova), if I aggregate my data according to A*B*C*response i will have empty cells. There is no clear pattern between yes and no’s besides the fact, that in my entire sample 30% are NOs and 70% are YESes

    The question of trialno is more difficult. Of cxourse you could simply add this as fixed and random effect as well, for example:
    mixed(logRT ~ A * B * C * response + trialno + (A * B * C * response + trialno | Subject), df)
    However, this would only allow a linear effect. Maybe some other functional form is better. If this is important another type of model such as a GAMM may be better. See:
    Baayen, H., Vasishth, S., Kliegl, R., & Bates, D. (2017). The cave of shadows: Addressing the human factor with generalized additive mixed models. Journal of Memory and Language, 94, 206–234. https://doi.org/10.1016/j.jml.2016.11.006

    I’ll have a look at the article. Thank you. Please let me explain why I want to account for trialno and how I did it before when running rmAnovas in SPSS on previous research:
    1) there is a general tendency to speed up with consequtive trails. Negative correlation betwen trialno and logRT. The degree to which participants speed up varies across subjects.
    2) in order to deal with the above, in previous research I would compute standardized residuals from a lm(logRT~trialno) equation separatly of each subject. This standardized result would make my main dependend variable and replace the orignal log of RTs.
    So again – i’m interested in including trialno just to get something conceptually similar to what I did with SPSS Anovas, but in a mixed model.

    Unfortunately, this is at the moment not really possible with lme4 (which mixed uses).

    One final thought. Your model seems to have quite a lot of data, in this case method = “S” is probably indicated in the call to mixed(). It is faster and very similar.

    Models on original data indeed take a lot of time and I already figured out that “S” is faster then anything else.

    I saw you commented on CV – if that’s of any value (for you or CV community) I can rephrase that question the way I did here, allowing you a normal answer that I can eventually accept.
    Speaking of my original post on CV – what’s your view on the syntax proposed by A.Field in his book, where factors defining the repeated measure structure of data are specified as lme(logRT~ 1, random = ~1|Subject/A/B/C, data = df, method = "ML")?
    Same syntax suggestion can be found here and it confuses me a lot.
    Are those guys wrong? Or is this specific to nlme syntax and basically random = ~1|Subject/A/B/C means the same as (A*B*C|Subject) in lme4?
    Or maybe it’s because Field is not thinking about a repeaed measure experiment as having random slopes? But if that’s the case – maybe I should’t do it with my data as well…

    Once again thank you for your time and helping me out.

    • This reply was modified 2 weeks, 1 day ago by  blazej.
  • #262

    blazej
    Participant

    Another way to put my confusion between your syntax and Field’s syntax is this example

    m1 <- mixed(logRT ~ A*B*C + (A*B*C|Subject), 
                    data = df, progress = TRUE, method = "S")
    m2 <- mixed(logRT ~ A*B*C + (1|Subject/A/B/C), 
                data = df, progress = TRUE, method = "S")

    Considering we are still talking about the same experiment, how do those two models differ, and why should I pick one over the other?

    PS.
    I added a “simulation” of missing values that are initially present in my data (i don’t know if that makes any difference)

    library(AlgDesign) #for generating all levels a factorial design)
    
    df <-gen.factorial(c(16,2,2,2,100), factors = "all", 
                       varNames = c("rep", "A", "B", "C", "Subject"))
    df$rep <- as.numeric(df$rep)
    df$Subject <- as.numeric(df$Subject)
    logRT <- rnorm(n=12800, m=7, sd=1)
    trialno<- rep(1:128, times = 100)
    response <- sample(0:1, 12800, prob = c(0.3, 0.7), replace = T)
    
    #Simulate some values to drop (set as missings) due to extremly low latency
    missingRTs<- as.logical(sample(0:1, 12800, prob = c(0.96, 0.04), replace = T))
    logRT[missingRTs==1] <- NA
    
    df <- cbind(df, logRT, trialno, response)
    df <- df[complete.cases(df),]
    • This reply was modified 2 weeks, 1 day ago by  blazej.
  • #264

    henrik
    Keymaster

    I suggest you take an intensive look at our chapter and further literature. This is probably more efficient than waiting for my responses. For example, the chapter contains references to the main references for whether or not it is appropriate to reduce the random-effects structure based on data. One camp, basically Barr, Levy, Scheepers, and Tily (2013), suggest that this is almost never a good idea. The other camp, Bates et al. and Matuschek et al. (see rferences below), suggest that it is reasonable in case of convergence problems. I think both sides have good arguments. But if your model does not converge, what else can you do other than reduce the random-effects structure. My first preferred solution is to start by removing the correlation among random-effects (also discussed in the chapter).

    I am not sure how relevant it is to try to build the equivalent model to a repeated-measures ANOVA. Mixed models are more adequate for your data. So try to use them in the best way possible.
    Perhaps one concrete response. Including a fixed and random effect for trialno is essentially equivalent to using the residuals. You “control” for this effect (you can search CV, there are plenty of relevant questions with answers on controlling in a regression framework).

    The old lme syntax did not allow random-slopes in the same way lme4 did. People who still use this older approach therefore have to use the nesting syntax. As discussed in detail in for example Barr et al., the recommended approach nowadays uses random slopes. Some more details can again be found on CV. For example:
    https://stats.stackexchange.com/a/131011/442
    https://stats.stackexchange.com/q/325316/442
    https://stats.stackexchange.com/q/14088/442
    https://stats.stackexchange.com/q/117660/442

    Good luck!

    Bates, D., Kliegl, R., Vasishth, S., & Baayen, H. (2015). Parsimonious Mixed Models. arXiv:1506.04967 [stat]. Abgerufen von http://arxiv.org/abs/1506.04967
    Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H., & Bates, D. (2017). Balancing Type I error and power in linear mixed models. Journal of Memory and Language, 94, 305–315. https://doi.org/10.1016/j.jml.2017.01.001

  • #265

    blazej
    Participant

    Thank you.
    I’ll do my homework with all the reading and get back here if any major question pops up.

  • #268

    blazej
    Participant

    Henrik,
    thanks a lot for pointing out your chapter (it’s great and I learned a lot!) as well as links to your CV questions (seems like you had some similar doubts, only a couple of years earlier).

    Anyway I did my homework and eventually came up with the “maximum model justified by design” as you suggested on exemplary data.
    I do however get convergence errors on my real data. I’ve been trying to sort it out in various ways – for example uncorrelated factor:

    mixed(logRT ~ A*B*C*response + (A*B*C*response||Subject), 
                data = df, progress = TRUE, method = "S", expand_re = TRUE)

    but then I started to wonder whether or not they are truly uncorrelated and how to set 2 factors as uncorrelated but leave two as correlated.
    Would this syntax make any sense?

    mixed(logRT ~ A*B*C*response + (A*B||Subject) + (C*response|Subject), 
                data = df, progress = TRUE, method = "S", expand_re = TRUE)

    A*B are uncorrelated as those factors conceptually could be uncorrelated. C and response on the other hand can be strongly correlated.

    Also, do you know of any (preferably easy) way to simulate predicted values from different models (as the ones above) and then compare their predictions between each other to (for example) establish which of those fits better in terms a true prediction (and not only fit)?
    I found Placida code on CV for something really similar (lost the link accidentally) that was based on sleep study data and compared p-values from 1000 simulations of two different random structures. My take (but not working for reasons I don’t understand) on exemplary data was this:

    fm.sim <- lmer(logRT ~ A*B*C +(1|Subject), data=df, REML=FALSE) 
    anova(fm.sim)
    n.sim <- 50
    sim.data <- vector(mode="list",)
    tempReaction <- simulate(fm.sim, nsim=n.sim)
    tempdata <- model.frame(fm.sim)
    
    for (i in 1:n.sim){
          tempdata$rtLN <- tempReaction[,i]
          output0 <- lmer(logRT ~ A*B*C +(A*B*C|Subject), data=tempdata, REML=FALSE)
          output1 <- lmer(logRT ~ A*B*C +(A*B*C||Subject), data=tempdata, REML=FALSE)
          temp <- anova(output0,output1)
          pval <- temp$<code>Pr(>Chisq)</code>[2]
          sim.data[[i]] <- list(model0=output0,modelA=output1, pvalue=pval)
    }
    
    pval_l <- sapply(sim.data, "[", "pvalue")
    pval_df<- do.call(rbind.data.frame, pval_l)
    
    hist(pval_df[,1])

    In original Placida’a answer histogram showed a nice uniform distribution of all p-values indicating no true difference between two models (as I understand it). My code on the other hand shows only one, fixed result across all simulations.

    If I’m expecting too much from you and your time, please let me know. Otherwise answers or links to similar questions / solutions are also very helpful.
    Thanks!

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Author: blazej