Extract components from a fitted mixeff LMM — mm

These methods provide the common lme4-style extractor surface for lmm() fits. The required values are stored directly on the R object or rebuilt lazily from the serialized artifact, so these methods do not require a live Rust handle after saveRDS() / readRDS().

ngrps() returns a named integer vector giving the number of levels of each random-effect grouping factor, mirroring lme4::ngrps().

Produces the long form returned by as.data.frame(lme4::VarCorr(.)): one row per variance (var2 = NA) and one row per covariance (var1, var2 both set), with a final Residual row for LMMs. vcov holds the (co)variance and sdcor the standard deviation (diagonal) or correlation (off-diagonal). This is the shape broom.mixed::tidy() expects.

Produces the long form returned by as.data.frame(lme4::ranef(.)): columns grpvar, term, grp, condval, and condsd. condsd is the conditional standard deviation, taken from the postVar attribute when the modes were extracted with condVar = TRUE, and NA otherwise.

Usage

fixef(object, ...)

# S3 method for class 'mm_lmm'
fixef(object, ...)

# S3 method for class 'mm_glmm'
fixef(object, ...)

ranef(object, ...)

# S3 method for class 'mm_lmm'
ranef(object, condVar = FALSE, ...)

# S3 method for class 'mm_glmm'
ranef(object, condVar = FALSE, ...)

# S3 method for class 'mm_lmm'
coef(object, ...)

# S3 method for class 'mm_glmm'
coef(object, ...)

VarCorr(x, ...)

# S3 method for class 'mm_lmm'
VarCorr(x, ...)

# S3 method for class 'mm_glmm'
VarCorr(x, ...)

# S3 method for class 'mm_lmm'
sigma(object, ...)

# S3 method for class 'mm_glmm'
sigma(object, ...)

# S3 method for class 'mm_lmm'
logLik(object, REML = NULL, ...)

# S3 method for class 'mm_glmm'
logLik(object, REML = NULL, ...)

# S3 method for class 'mm_lmm'
deviance(object, REML = NULL, ...)

# S3 method for class 'mm_glmm'
deviance(object, REML = NULL, ...)

# S3 method for class 'mm_lmm'
AIC(object, ..., k = 2)

# S3 method for class 'mm_glmm'
AIC(object, ..., k = 2)

# S3 method for class 'mm_lmm'
BIC(object, ...)

# S3 method for class 'mm_glmm'
BIC(object, ...)

# S3 method for class 'mm_lmm'
nobs(object, ...)

# S3 method for class 'mm_glmm'
nobs(object, ...)

# S3 method for class 'mm_lmm'
df.residual(object, ...)

# S3 method for class 'mm_glmm'
df.residual(object, ...)

# S3 method for class 'mm_lmm'
formula(x, ...)

# S3 method for class 'mm_glmm'
formula(x, ...)

# S3 method for class 'mm_lmm'
model.frame(formula, ...)

# S3 method for class 'mm_glmm'
model.frame(formula, ...)

ngrps(object, ...)

# Default S3 method
ngrps(object, ...)

# S3 method for class 'mm_lmm'
ngrps(object, ...)

# S3 method for class 'mm_glmm'
ngrps(object, ...)

# S3 method for class 'mm_lmm'
weights(object, ...)

# S3 method for class 'mm_glmm'
weights(object, ...)

# S3 method for class 'mm_lmm'
extractAIC(fit, scale, k = 2, ...)

# S3 method for class 'mm_glmm'
extractAIC(fit, scale, k = 2, ...)

# S3 method for class 'mm_lmm'
terms(x, ...)

# S3 method for class 'mm_glmm'
terms(x, ...)

# S3 method for class 'mm_varcorr'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

# S3 method for class 'mm_ranef'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

# S3 method for class 'mm_lmm'
model.matrix(object, type = c("fixed", "random"), ...)

# S3 method for class 'mm_glmm'
model.matrix(object, type = c("fixed", "random"), ...)

# S3 method for class 'mm_lmm'
vcov(object, type = c("fixed", "theta"), correlation = FALSE, ...)

# S3 method for class 'mm_glmm'
vcov(object, type = c("fixed", "theta"), correlation = FALSE, ...)

Arguments

object, x, formula, fit: A fitted mm_lmm or mm_glmm object.
...: Reserved for generic compatibility.
condVar: Logical; when TRUE, Phase 2 returns the random-effects tables with an NA postVar array and an mm_unavailable_reason attribute rather than fabricating conditional variances.
REML: Ignored; included for S3 compatibility with likelihood and deviance generics.
k: Penalty per parameter for AIC().
scale: Ignored; included for S3 compatibility with extractAIC().
row.names, optional: Ignored; present for S3 consistency.
type: For model.matrix(), "fixed" returns the fixed-effect design matrix and "random" returns the sparse random-effect design matrix. For vcov(), "fixed" returns the fixed-effect covariance surface and "theta" returns an unavailable theta-covariance matrix with a reason attribute.
correlation: Logical; accepted for S3 compatibility with vcov().

Value

A named integer vector of group counts.

Examples

set.seed(1)
df <- data.frame(
  y = rnorm(60), x = rnorm(60),
  g = factor(rep(seq_len(10), each = 6))
)
fit <- lmm(y ~ x + (1 | g), df, control = mm_control(verbose = -1))
fixef(fit)
#> (Intercept)           x 
#>   0.1122739  -0.0416571 
VarCorr(fit)
#> Variance components:
#>  group        name variance std_dev correlation       note
#>      g (Intercept)        0       0             [boundary]
#> [boundary]: variance component is at the boundary of the parameter space.
#> Residual std. dev.: 0.861654
head(ranef(fit)$g)
#>   (Intercept)
#> 1           0
#> 2           0
#> 3           0
#> 4           0
#> 5           0
#> 6           0
sigma(fit)
#> [1] 0.8616536
logLik(fit)
#> 'log Lik.' -77.65847 (df=4)
nobs(fit)
#> [1] 60