semTools (version 0.5-6)

lavaan2emmeans: emmeans Support Functions for lavaan Models

Description

Provide emmeans support for lavaan objects

Usage

recover_data.lavaan(object, lavaan.DV, ...)
emm_basis.lavaan(object, trms, xlev, grid, lavaan.DV, ...)

Arguments

object

An object of class lavaan. See Details.

lavaan.DV

character string maming the variable(s) for which expected marginal means / trends should be produced. A vector of names indicates a multivariate outcome, treated by default as repeated measures.

...

Further arguments passed to emmeans::recover_data.lm or emmeans::emm_basis.lm

trms, xlev, grid

See emmeans::emm_basis

Details

Supported DVs

lavaan.DV must be an endogenous variable, by appearing on the left-hand side of either a regression operator ("~") or an intercept operator ("~1"), or both.

lavaan.DV can also be a vector of endogenous variable, in which case they will be treated by emmeans as a multivariate outcome (often, this indicates repeated measures) represented by an additional factor named rep.meas by default. The rep.meas= argument can be used to overwrite this default name.

Unsupported Models

This functionality does not support the following models:

• Multi-level models are not supported.

• Models not fit to a data.frame (i.e., models fit to a covariance matrix).

Dealing with Fixed Parameters

Fixed parameters (set with lavaan's modifiers) are treated as-is: their values are set by the users, and they have a SE of 0 (as such, they do not co-vary with any other parameter).

Dealing with Multigroup Models

If a multigroup model is supplied, a factor is added to the reference grid, the name matching the group argument supplied when fitting the model. Note that you must set nesting = NULL.

Dealing with Missing Data

Limited testing suggests that these functions do work when the model was fit to incomplete data.

Dealing with Factors

By default emmeans recognizes binary variables (0,1) as a "factor" with two levels (and not a continuous variable). With some clever contrast defenitions it should be possible to get the desired emmeans / contasts. See example below.

Examples

# NOT RUN {
  library(lavaan)
  library(emmeans)

  #### Moderation Analysis ####

  mean_sd <- function(x) mean(x) + c(-sd(x), 0, sd(x))

  model <- '
  # regressions
  Sepal.Length ~ b1 * Sepal.Width + b2 * Petal.Length + b3 * Sepal.Width:Petal.Length


  # define mean parameter label for centered math for use in simple slopes
  Sepal.Width ~ Sepal.Width.mean * 1

  # define variance parameter label for centered math for use in simple slopes
  Sepal.Width ~~ Sepal.Width.var * Sepal.Width

  # simple slopes for condition effect
  SD.below := b2 + b3 * (Sepal.Width.mean - sqrt(Sepal.Width.var))
  mean     := b2 + b3 * (Sepal.Width.mean)
  SD.above := b2 + b3 * (Sepal.Width.mean + sqrt(Sepal.Width.var))
  '

  semFit <- sem(model = model,
                data = iris)

  ## Compare simple slopes
  # From `emtrends`
  test(
    emtrends(semFit, ~ Sepal.Width, "Petal.Length",
             lavaan.DV = "Sepal.Length",
             cov.red = mean_sd)
  )

  # From lavaan
  parameterEstimates(semFit, output = "pretty")[13:15, ]
  # Identical slopes.
  # SEs differ due to lavaan estimating uncertainty of the mean / SD
  # of Sepal.Width, whereas emmeans uses the mean+-SD as is (fixed).


  #### Latent DV ####

  model <- '
  LAT1 =~ Sepal.Length + Sepal.Width

  LAT1 ~ b1 * Petal.Width + 1 * Petal.Length

  Petal.Length ~ Petal.Length.mean * 1

  V1 := 1 * Petal.Length.mean + 1 * b1
  V2 := 1 * Petal.Length.mean + 2 * b1
  '

  semFit <- sem(model = model,
                data = iris, std.lv = TRUE)

  ## Compare emmeans
  # From emmeans
  test(
    emmeans(semFit, ~ Petal.Width,
            lavaan.DV = "LAT1",
            at = list(Petal.Width = 1:2))
  )

  # From lavaan
  parameterEstimates(semFit, output = "pretty")[15:16, ]
  # Identical means.
  # SEs differ due to lavaan estimating uncertainty of the mean
  # of Petal.Length, whereas emmeans uses the mean as is.

  #### Multi-Variate DV ####

  model <- '
  ind60 =~ x1 + x2 + x3

  # metric invariance
  dem60 =~ y1 + a*y2 + b*y3 + c*y4
  dem65 =~ y5 + a*y6 + b*y7 + c*y8

  # scalar invariance
  y1 + y5 ~ d*1
  y2 + y6 ~ e*1
  y3 + y7 ~ f*1
  y4 + y8 ~ g*1

  # regressions (slopes differ: interaction with time)
  dem60 ~ b1*ind60
  dem65 ~ b2*ind60 + NA*1 + Mean.Diff*1

  # residual correlations
  y1 ~~ y5
  y2 ~~ y4 + y6
  y3 ~~ y7
  y4 ~~ y8
  y6 ~~ y8

  # conditional mean differences (besides mean(ind60) == 0)
   low := (-1*b2 + Mean.Diff) - (-1*b1) # 1 SD below M
  high := (b2 + Mean.Diff) - b1         # 1 SD above M
'

  semFit <- sem(model, data = PoliticalDemocracy)


  ## Compare contrasts
  # From emmeans
  emmeans(semFit, pairwise ~ rep.meas|ind60,
          lavaan.DV = c("dem60","dem65"),
          at = list(ind60 = c(-1,1)))[[2]]

  # From lavaan
  parameterEstimates(semFit, output = "pretty")[49:50, ]


  #### Multi Group ####

  model <- 'x1 ~ c(int1, int2)*1 + c(b1, b2)*ageyr
  diff_11 := (int2 + b2*11) - (int1 + b1*11)
  diff_13 := (int2 + b2*13) - (int1 + b1*13)
  diff_15 := (int2 + b2*15) - (int1 + b1*15)
'
  semFit <- sem(model, group = "school", data = HolzingerSwineford1939)


  ## Compare contrasts
  # From emmeans (note `nesting = NULL`)
  emmeans(semFit, pairwise ~ school | ageyr, lavaan.DV = "x1",
          at = list(ageyr = c(11, 13, 15)), nesting = NULL)[[2]]

  # From lavaan
  parameterEstimates(semFit, output = "pretty")

  #### Dealing with factors ####

  warpbreaks <- cbind(warpbreaks,
                      model.matrix(~ wool + tension, data = warpbreaks))

  model <- "
  # Split for convenience
  breaks ~ 1
  breaks ~ woolB
  breaks ~ tensionM + tensionH
  breaks ~ woolB:tensionM + woolB:tensionH
  "

  semFit <- sem(model, warpbreaks)

  ## Compare contrasts
  # From lm -> emmeans
  lmFit <- lm(breaks ~ wool * tension, data = warpbreaks)
  lmEM <- emmeans(lmFit, ~ tension + wool)
  contrast(lmEM, method = data.frame(L_all = c(-1, .05, 0.5),
                                     M_H   = c(0, 1, -1)), by = "wool")

  # From lavaan -> emmeans
  lavEM <- emmeans(semFit, ~ tensionM + tensionH + woolB,
                   lavaan.DV = "breaks")
  contrast(lavEM,
           method = list(
             "L_all|A" = c(c(-1, .05, 0.5, 0), rep(0, 4)),
             "M_H  |A" = c(c(0, 1, -1, 0),     rep(0, 4)),
             "L_all|A" = c(rep(0, 4),          c(-1, .05, 0.5, 0)),
             "M_H  |A" = c(rep(0, 4),          c(0, 1, -1, 0))
           ))
# }