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\dontrun{
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))
))
}
|
