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2

Corr is a lower triangular matrix containing the estimated correlations between the random effects. For example, the correlation between the random intercept and the random slope for timeL2 is -0.743, while the correlation between the randome slope for timeL3 and the random slope for timeL4 is 0.425, and so on.


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Leaving aside whether a correlation of 1 is really possible or not (since I know nothing about the context here), let me suggest some possible approaches. I'll simulate some data (in R) for illustration purposes: set.seed(1234) ri <- c(runif(8, .54, .84), .54, .84, 1) ni <- rep(20, 11) And I will use the metafor package for the following computations: ...


3

But, technically, one could imagine many ways in which data could be grouped even if the intent wasn’t a blocked or repeated measures design. True, but the issue isn't about blocking or repeated measures, it's about non-independence of observations within the groups. If there is non-independence, this needs to be accounted for in order to provide valid ...


3

This is expected behaviour. If it did not estimate both levels seperately there would be a missing estimate. This doesn't really have anything to do with mixed models or lmer or standardisation. It is expected, normal, behaviour whenever the intercept is omitted from a model. To see why this is the case, consider a very simple simulated dataset: set.seed(1) ...


16

As you have discovered, this happens when one of the variance components is estimated as zero. This typically has one of two explanations: the random effects structure is over-fitted - usually because of too many random slopes one of more variance components is actually very close to zero and there is insufficient data to estimate it above zero. Obviously ...


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I don't have experience with frailty models, so I can't speak to why a frailty term seems to be incompatible with a multi-state model. If you are primarily interested in accounting for intra-individual correlations and aren't wedded to the particulars of frailty modeling, however, then you can accomplish something similar with a cluster(id) term instead. ...


1

In this experiment you make repeated measures on each plant - each plant is measured twice, the left side and the right side. Due to this, measurements taken on one plant are likely to be more similar to the other measurement on the same plant, than measures on a different plant. This clustering means that observations are not independent and unless you ...


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Can I perform this kind of modelling? I mean, is it ok to leave time point 1 and 2 out of my model? I am not interested in if they learn to answer faster either. Yes, there is no problem with this. You are effectively modelling the mean reaction time.


3

You seem to be on the right track and thinking of the right things. Regressing change scores on baseline is a very bad idea as you have already learned. For the same reasons and more, don't include baseline as a random effect. That doesn't make much sense at all. Also, change scores themselves are not a good way to analyse change, as the estimand in such a ...


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If you alter the control paramenters in lmecontrol it converges: cl = lmeControl(maxIter = 200, msMaxIter = 1000, niterEM = 500, msMaxEval = 2000) two <- lme(value ~ 0 + name+ name:uerate, data = dat, random = ~0 + name+ name:uerate | id, weights = varIdent(form = ~1 | name), control = cl)


2

You can extract the conditional modes of the random effects using getME(test_model, "b") You can extract the model matrix $Z$ for the random effects using getME(mask_model,'Zt') and transpose it. This matrix determines how the random effects are "mapped" to the response in the mixed model equation: $$ Y = X\beta + Zb + \epsilon$$ You ...


0

This appears to be a "cluster randomised" design. There should be no problem using a mixed effects model, with random intercepts for school. The intervention is a fixed effect so it doesn't make sense to think of schools being nested within the intervention. Nesting is only relevant for random effects. So your model will be something like Y ~ ...


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Why not? : m1 <- lmer(doy ~ year + siteID + year:siteID + (1 + year|sppID), data = df[sppID=="spp_1"]) If the species have very dissimilar responses and you are not interested in comparing sites, an interesting option would be to perform one model for each species and add sites as random term: m2 <- lmer(doy ~ year + (1 + year|siteID), data = ...


1

Class and Type are fixed effects. They should not be grouping variables for random intercepts. participant and verb are crossed, so I would start with the model: Answer ~ Prompt + Class + Type + (1 | participant) + (1 | verb) and then consider adding random slopes, if supported by the underlying theory and the data.


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As year has only two distinct values, there is nothing to gain by representing it as a random effect, see What is the minimum recommended number of groups for a random effects factor?. That leaves you with option c). About using aic to choose between these models, see Comparing between random effects structures in a linear mixed-effects model. There is ...


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From the description, this is a partially crossed design. The 2nd model is appropriate for a design that is fully nested. The first model is appropriate for a partially nested design, provided that the factors are coded uniquely. For example, if you have tract1 in cbsa1 and you also have a tract1 in cbsa2, but these are actually different tracts, then you ...


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