coef.glmertree function

Obtaining Fixed-Effects Coefficient Estimates of (Generalized) Linear Mixed Model Trees

coef and fixef methods for (g)lmertree objects.

## S3 method for class 'lmertree'
coef(object, which = "tree", drop = FALSE, ...)
## S3 method for class 'lmertree'
fixef(object, which = "tree", drop = FALSE, ...)
## S3 method for class 'glmertree'
coef(object, which = "tree", drop = FALSE, ...)
## S3 method for class 'glmertree'
fixef(object, which = "tree", drop = FALSE, ...)

Arguments

• object: an object of class lmertree or glmertree.
• which: character; "tree" (default) or "global". Specifies whether local (tree) or global fixed-effects estimates should be returned.
• drop: logical. Only used when which = "tree"; delete the dimensions of the resulting array if it has only one level?
• ...: Additional arguments, curretnly not used.

Details

The code is still under development and might change in future versions.

Returns

If type = "local", returns a matrix of estimated local fixed-effects coefficients, with a row for every terminal node and a column for every fixed effect. If type = "global", returns a numeric vector of estimated global fixed-effects coefficients.

References

Fokkema M, Smits N, Zeileis A, Hothorn T, Kelderman H (2018). Detecting Treatment-Subgroup Interactions in Clustered Data withGeneralized Linear Mixed-Effects Model Trees . Behavior Research Methods, 50 (5), 2016--2034. tools:::Rd_expr_doi("10.3758/s13428-017-0971-x")

Fokkema M, Zeileis A (2024). Subgroup Detection in Linear Growth Curve Models with GeneralizedLinear Mixed Model (GLMM) Trees.

Behavior Research Methods, 56 (7), 6759--6780. tools:::Rd_expr_doi("10.3758/s13428-024-02389-1")

Examples

## load artificial example data
data("DepressionDemo", package = "glmertree")

## fit LMM tree with local fixed effects only
lt <- lmertree(depression ~ treatment + age | cluster | anxiety + duration,
  data = DepressionDemo)
coef(lt)

## fit LMM tree including both local and global fixed effect
lt <- lmertree(depression ~ treatment | (age + (1|cluster)) | anxiety + duration,
  data = DepressionDemo)
coef(lt, which = "tree") # default behaviour
coef(lt, which = "global")

## fit GLMM tree with local fixed effects only
gt <- glmertree(depression_bin ~ treatment | cluster | 
  age + anxiety + duration, data = DepressionDemo)
coef(gt)

## fit GLMM tree including both local and global fixed effect
gt <- glmertree(depression_bin ~ treatment | (age + (1|cluster)) | 
  anxiety + duration, data = DepressionDemo)
coef(gt, which = "tree") # default behaviour
coef(gt, which = "global")

See Also

lmertree, glmertree, party-plot.