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Which is better to use lme4 or nlme?
My recommendation about which to use depends on the problem. lme4 is a much smaller tool kit, and the formula representation is a neat, concise way of depicting some very common mixed effects models. nlme is the very large tool box, including a TIG welder to make any tools you need. You say you want to allow for “variation over time”.
How to use LME for repeated measures ANOVA?
I’m trying to use lme from the nlme package to replicate results from aov for repeated measures ANOVAs.
How to use LME and lmer in R?
Using lme from the nlme package, the code would be Using lmer from the lme4 package, the code would be something like These threads from R-help may be helpful (and to give credit, that’s where I got the nlme code from). This last link refers to p.165 of Pinheiro/Bates; that may be helpful too.
How is LME used to compare treatment to wait list?
For instance, we might compare a treatment group to a wait-list condition. Subjects in the wait-list will not be nested, but subjects in treatment group will be nested within therapists. Only lme allows modeling heteroscedastic residual variance at level 1.
Can you fit panel data with the LM function?
You can fit panel data with the lm function too! My recommendation about which to use depends on the problem. lme4 is a much smaller tool kit, and the formula representation is a neat, concise way of depicting some very common mixed effects models. nlme is the very large tool box, including a TIG welder to make any tools you need.
Are there correlation structures that nlme does not handle?
Yep there are still correlation structures that nlme handles which lme4 will not handle. However, for as long as nlme allows the user to define general corstrs and lme4 does not, this will be the case. This has surprisingly little practical impact.
When to use lme4 for mixed effect regression?
From a practical perspective, the two most important criteria seem, however, that. lme4 extends nlme with other link functions: in nlme, you cannot fit outcomes whose distribution is not gaussian, lme4 can be used to fit mixed-effects logistic regression, for example.
What are the performance capabilities of the lmer function?
the performance capabilities of lmer. 1.3. High-level modular structure The lmer function is composed of four largely independent modules. In the first module, a mixed-model formula is parsed and converted into the inputs required to specify a linear mixed model (Section 2). The second module uses these inputs to construct an R function which
How are the RMA and LME functions different?
The models fitted by the rma () function assume that the sampling variances are known. The models fitted by the lm (), lme (), and lmer () functions assume that the sampling variances are known only up to a proportionality constant. These are different models than typically used in meta-analyses.
Why are there discrepancies between LME and lmer?
These discrepancies are due to the exact same reason described earlier. The lme () and lmer () functions assume that the sampling variances are not exactly known, but again just up to a proportionality constant, namely the residual variance.