randomeffects | R Documentation |
The BMSC function allows the flexibility of multilevel (generalised) linear models on single case analysis.
In particular, it is possible to specify the population-level (a.k.a. mixed effects) and the group-level (a.k.a. random effects) coefficients.
The specification of the population- and group-level effects can be done using the well-known lme4 notation with specific limitations:
it is no possible to estimate uncorrelated group-level effects
it is no possible to directly estimate nested effects. You need to use a trick that is specified in the Details section.
lmer formulation | BMSC availability |
(1 | grouping_factor) | Yes |
(1 + slope | grouping_factor) | Yes |
(0 + slope | grouping_factor) | No |
(1 | grouping_factor1 : grouping_factor2) | Yes[^1] |
(1 | grouping_factor1 / grouping_factor2) | Yes[^2] |
[^1]: The BMSC function dose not allow the use of the interaction symbol ":", but this problem is easily solved by creating a new variable within your dataframe given by the interaction of the two factors.
[^2]: The (1 | grouping_factor1 / grouping_factor2)
syntax is the
equivalent of the explicit version
(1 \| grouping_factor1:grouping_factor2) + (1 | grouping_factor1)
.
Therefore, you need to create a new grouping factor representing the
interaction between grouping_factor1
and grouping_factor2
,
and use this in the explicit version
(1 | grouping_factor_interaction) + (1 | grouping_factor1)
.
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