Multilevel Models with plssem

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This vignette shows examples of multilevel random slopes and intercept models, with both continuous and ordinal data.

Random Slopes Model

slopes_model <- "
  X =~ x1 + x2 + x3
  Z =~ z1 + z2 + z3
  Y =~ y1 + y2 + y3
  W =~ w1 + w2 + w3
  Y ~ X + Z + (1 + X + Z | cluster)
  W ~ X + Z + (1 + X + Z | cluster)
"

Continuous Indicators

fit_slopes_cont <- pls(
  slopes_model,
  data      = randomSlopes,
  bootstrap = TRUE,
  boot.R    = 50
)
summary(fit_slopes_cont)

Ordered Indicators

fit_slopes_ord <- pls(
  slopes_model,
  data      = randomSlopesOrdered,
  bootstrap = TRUE,
  boot.R    = 50,
  ordered   = colnames(randomSlopesOrdered) # explicitly specify variables as ordered
)
summary(fit_slopes_ord)

Random Intercepts Model

intercepts_model <- '
  f =~ y1 + y2 + y3
  f ~ x1 + x2 + x3 + w1 + w2 + (1 | cluster)
'

Continuous Indicators

fit_intercepts_cont <- pls(
  intercepts_model,
  data      = randomIntercepts,
  bootstrap = TRUE,
  boot.R    = 50
)
summary(fit_intercepts_cont)

Ordered Indicators

fit_intercepts_ord <- pls(
  intercepts_model,
  data      = randomInterceptsOrdered,
  bootstrap = TRUE,
  boot.R    = 50,
  ordered   = colnames(randomInterceptsOrdered) # explicitly specify variables as ordered
)
summary(fit_intercepts_ord)

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They may not be fully stable and should be used with caution. We make no claims about them.