Update and re-fit a mixeff model

update() re-fits an mm_lmm or mm_glmm with a modified model specification, mirroring stats::update() and lme4's update() for the common cases: changing the formula (. ~ . - x), toggling REML, swapping weights/offset/family/control, or supplying new data.

Usage

# S3 method for class 'mm_lmm'
update(object, formula., ..., evaluate = TRUE)

# S3 method for class 'mm_glmm'
update(object, formula., ..., evaluate = TRUE)

Arguments

object

A fitted mm_lmm or mm_glmm.

formula.

A formula-change applied with stats::update.formula(); omit to keep the current formula. Random-effect terms ((x | g), (x || g)) are preserved across . ~ . edits.

...

Arguments to override on the re-fit. For mm_lmm: data, REML, weights, control. For mm_glmm: additionally family, offset, method, nAGQ, inference.

evaluate

If TRUE (default) re-fit and return the new model; if FALSE return the unevaluated call.

Value

A new fitted model of the same class as object, or an unevaluated call when evaluate = FALSE.

Details

The re-fit reuses the fitted model frame (model.frame()) as the default data source, so formula edits that remove terms or change estimation options work without re-supplying data. A formula edit that introduces a new variable absent from the original model frame requires an explicit data = argument.

Examples

set.seed(1)
df <- data.frame(
  y = rnorm(80), x = rnorm(80), z = rnorm(80),
  g = factor(rep(seq_len(10), each = 8))
)
fit <- lmm(y ~ x + z + (1 | g), df, control = mm_control(verbose = -1))
# drop a fixed term
fit2 <- update(fit, . ~ . - z)
# refit by ML for a likelihood-ratio comparison
fit_ml <- update(fit, REML = FALSE)
fixef(fit2)
#> (Intercept)           x 
#>  0.07858531 -0.28479350