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Fit a linear mixed-effects model

Source: R/models-mixed.R
fitMixedModel.Rd

Fits a linear mixed model with lme4::lmer and returns tidy fixed-effect and random-effect tables in an AnalysisResult. Denominator degrees of freedom (and the associated t tests) use lmerTest when it is installed - either the Satterthwaite or the Kenward-Roger approximation.

Usage

fitMixedModel(
  data,
  formula,
  random = NULL,
  method = c("REML", "ML"),
  df = c("satterthwaite", "kenward-roger")
)

Arguments

data

A data.frame in long format.

formula

A model formula. It may carry the random-effects terms directly (e.g. y ~ x + (x | subject)), or supply them via random.

random

Optional one-sided random-effects formula (e.g. ~ (1 | subject)) appended to formula when the latter has none.

method

"REML" (default) or "ML".

df

Denominator-df method for the fixed-effect tests: "satterthwaite" (default) or "kenward-roger" (both need lmerTest).

Value

An AnalysisResult (type = "mixed_model") whose estimate is the fixed-effect coefficient vector, with result$fixed (the tidy fixed-effect table), result$random (variance components), result$sigma, result$fit (the fitted model, for estimatedMarginalMeans()) and result$formula.

References

Bates D, Maechler M, Bolker B, Walker S (2015). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67(1). Kuznetsova A, Brockhoff PB, Christensen RHB (2017). lmerTest package. Journal of Statistical Software, 82(13).

See also

fitMMRM(), estimatedMarginalMeans()

Examples

if (requireNamespace("lme4", quietly = TRUE)) {
  data(sleepstudy, package = "lme4")
  fitMixedModel(sleepstudy, Reaction ~ Days + (Days | Subject))
}
#> <AnalysisResult> mixed_model 
#>   estimate: 251.40510,  10.46729 
#>   method: REML 
#>   fields: fixed, random, sigma, fit, formula, df_method 
#>   provenance: 1 entr(ies)