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Results tagged with lme4-nlme
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user 87305
lme4 and nlme are R packages used for fitting linear, generalized linear and nonlinear mixed effects models. For general questions about mixed models use [mixed-model] tag.
9
votes
longitudinal data with unequal samples and end points
I believe that you are fine to use the approach you outlined in your question. There is more between id variance at time==0 (the random intercept) than there is in the rate of change (random slope for …
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6
votes
Accepted
Clarification on Random Effects Structure in Linear Mixed Models in R
The answer depends a bit on how your country and id variables are coded. Is it the case that:
a) In each country the numbering for individual ids starts at 1?
or
b) In country 1 you have ids 1-10 a …
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5
votes
Difference between Fixed Effects predictor Value and ggeffects predicted values
Keep in mind that the parameter estimate for PC1 in the lmer model summary is conditional any variables that PC1 is interacted with. In this case, you interacted PC1 with DominancePatternCod and WTW. …
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4
votes
Accepted
lme4: what is gained by scaling variables?
You mention that you cannot properly interpret the coefficient of a standardized variable, and I would disagree. Imagine you have a variable that is scaled from 0 to 100. A 1-unit difference in such a …
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4
votes
multilevel modeling with lmer(): understanding failure to converge in a toy example
Building on @Emma Jean's response, Your created data frame is not multilevel in the sense that person (the grouping variable) and female are at the same level. In order for your data to accommodate a …
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4
votes
Follow-up: Complete-pooling, no-pooling, and partial-pooling regression in R
The problem is that sch.id is stored as an integer (see str(d)) and in order for it to work as intended for the no pooling model, it needs to be treated as a factor variable. You can do this either by …
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4
votes
Accepted
Multi-level model [lme4 package] specification with cross-level predictors and group-level o...
You are on the right track, but there are some key things to keep in mind. In multilevel or mixed models the dependent variable has to be at the lowest level of the hierarchy. In the case of your indi …
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4
votes
Modeling repeated measures data in R - Interpretation and Validation
To help make your original regression results more interpretable, I suggest that you code timepoint such that the first occasion is given a value of 0. This is because in regression models, the interc …
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4
votes
Accepted
Specific group effects (coefficients) in mixed-effect modeling in R (lmer)
This is a great question that hits at one of the superpowers of multilevel or mixed effects models, in my opinion. In your example, A, B, and C all vary within groups but, very likely, groups vary in …
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4
votes
0
answers
134
views
Can we identify whether random effects are nested or crossed from a lme4 fit?
My colleagues and I are working on a suite of lmer post-estimation tools for a R package we are developing. One of the tools is an ICC function that would calculate the appropriate ICC for models with …
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3
votes
Accepted
Multilevel Modelling , 2 level -statistical significance test on random effects
Welcome to the site, Zubaer. There is no equivalent significance test for random effects as there are for fixed effects. Instead, you can run model comparison tests, in which you run a likelihood rati …
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3
votes
summarize extent of pooling or shrinkage in multilevel models estimated with lmer()
Fleshing out Dimitris' comment, you can look at this by considering the estimates you get from lmer and lm. Using your lmer model, we can ask for the estimated intercepts and slopes with the coef() fu …
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3
votes
low marginal and high conditional R2 for mixed models
According to the documentation for r2_nakagawa,
The marginal r-squared considers only the variance of the fixed
effects, while the conditional r-squared takes both the fixed and
random effect …
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3
votes
Accepted
Imputing panel data in the wide format, obtaining pooled standard errors after using lmer
If the imputed datasets are in long form (dataset 2 stacked onto dataset 1) then you can use mitml the to do the pooling of the estimates from your model to give you the correct standard errors. See t …
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2
votes
Accepted
Mixed model with random slope and Intercept syntax?
Some thoughts on each of your assumptions:
I want to assume that measurements that are further apart in time are less correlated than measurements that closer (random slope).
In theory, t …
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