Estimated marginal means for a fitted mixed model

Computes estimated marginal means, or pairwise comparisons of them, for a fitted linear mixed model. For a model fitted by lme4::lmer(), lmerTest::lmer() or nlme::lme() the optional package emmeans does the work, as in version 1.1.0; for a result of vlm_fit() the least-squares means of vlm_lsmeans() are returned.

Usage

emmeans_mixed_model(
  model,
  specs,
  pairwise = FALSE,
  method = "pairwise",
  adjust = "tukey",
  ...
)

# Default S3 method
emmeans_mixed_model(
  model,
  specs,
  pairwise = FALSE,
  method = "pairwise",
  adjust = "tukey",
  ...
)

# S3 method for class 'vlm_fit'
emmeans_mixed_model(
  model,
  specs,
  pairwise = FALSE,
  method = "pairwise",
  adjust = "tukey",
  ...
)

Arguments

model: a fitted model: a result of vlm_fit(), or a model from lme4::lmer(), lmerTest::lmer() or nlme::lme().
specs: specifications of the marginal means, such as ~ dose or ~ dose | Drug.
pairwise: logical; FALSE returns the means, TRUE the contrasts (pairwise differences).
method: contrast method passed to emmeans::contrast() (default "pairwise"); a vlm_fit result always uses pairwise differences.
adjust: multiplicity adjustment (default "tukey"; NULL leaves it to emmeans).
...: further arguments passed to emmeans::emmeans().

Value

For a vlm_fit result, a vlm_lsmeans result (see vlm_lsmeans()); otherwise an emmGrid object of emmeans.

Details

Estimated marginal means (least-squares means) average the model predictions over the levels of the other factors with equal weights. For a vlm_fit result, specs gives the effect (for example ~ dose | Drug or ~ Drug:dose); pairwise = TRUE adds all pairwise differences with the adjustment adjust ("tukey", "bonferroni", "sidak" or "none").

References

Lenth, R. V. (2024). emmeans: Estimated Marginal Means, aka Least-Squares Means. R package.

Duchateau, L., Janssen, P. and Rowlands, G. J. (1998). Linear Mixed Models. An Introduction with Applications in Veterinary Research. International Livestock Research Institute, Nairobi.

Author

Muhammad Yaseen (myaseen208@gmail.com)

Examples

fm <-
  vlm_fit(
      fixed    = Pcv ~ Drug + dose + Drug:dose
    , data     = ex125
    , random   = ~ Region + Region:Drug
    , repeated = NULL
    , method   = "REML"
    , ddfm     = "default"
    , type     = 3
    , class    = NULL
    , bound    = TRUE
    , control  = list()
    )
emmeans_mixed_model(
    model    = fm
  , specs    = ~ dose | Drug
  , pairwise = TRUE
  , method   = "pairwise"
  , adjust   = "tukey"
  )
#> Least-squares means (denominator df: contain; 95% limits)
#> 
#> Least squares means
#>     Drug dose estimate    se df     t      p lower upper
#>  BERENIL    h    25.45 1.128 10 22.56 <1e-04 22.94 27.96
#>  BERENIL    l    24.28 1.128 10 21.53 <1e-04 21.77 26.80
#>  samorin    h    21.48 1.128 10 19.05 <1e-04 18.97 24.00
#>  samorin    l    17.13 1.128 10 15.19 <1e-04 14.62 19.65
#> 
#> Differences of least squares means (adjustment: tukey)
#>    level_1   level_2 estimate     se df     t        p p_adjusted   lower upper
#>  BERENIL h BERENIL l    1.167 0.8359 10 1.396 0.193041    0.52926  -1.391 3.724
#>  BERENIL h samorin h    3.967 0.9098 10 4.360 0.001422    0.00650   1.183 6.750
#>  BERENIL h samorin l    8.317 0.9098 10 9.141  < 1e-04    < 1e-04   5.533 11.10
#>  BERENIL l samorin h    2.800 0.9098 10 3.077 0.011691    0.04858 0.01648 5.584
#>  BERENIL l samorin l    7.150 0.9098 10 7.859  < 1e-04    < 1e-04   4.366 9.934
#>  samorin h samorin l    4.350 0.8359 10 5.204 0.000399    0.00188   1.793 6.907
#> 
#> Sources: Duchateau, Janssen and Rowlands (1998); Searle, Speed and Milliken
#> (1980); Kramer (1956). Full references: vlm_cite(x).