Unstandardized estimates for PlsMultilevelModel objects

Computes the unstandardized estimates of each level of a two-level model.

# S4 method for class 'PlsMultilevelModel'
unstandardized_estimates(
  model,
  unstandardized = "all",
  se = c("delta", "none"),
  scale.uncertainty = FALSE,
  eps = 1e-04,
  zero.tol = 1e-10,
  rm.tmp.ov = TRUE,
  clean.tmp.ind = TRUE,
  clean.tmp.mimic = TRUE
)

Arguments

model

A fitted PlsModel object.

unstandardized

Character vector naming variables to unstandardize, or one of "all", "ov", or "lv".

se

Character string selecting delta-method standard errors ("delta") or no standard errors ("none").

scale.uncertainty

Should scale uncertainty be included? defaults to FALSE.

eps

Positive numeric finite-difference step used for the delta-method Jacobian.

zero.tol

Non-negative numeric tolerance below which standard errors are returned as missing.

rm.tmp.ov

Logical; whether rows involving temporary observed variables should be removed from the returned parameter table.

clean.tmp.ind

Logical; whether rows involving temporary indicators should be cleaned from the returned parameter table.

clean.tmp.mimic

Logical; whether rows involving temporary mimic indicators should be cleaned from the returned parameter table.

Value

A PlsSemParTable data frame, with a level column.

Details

The levels are unstandardized separately, using the (decomposed) scales of the variables at each level, i.e., sqrt(1 - icc) * sd at level 1, and sqrt(icc) * sd at level 2 for variables at both levels. The latent variables are scaled by their first indicators. The scales of the random slopes (y ~ rv(s) * x) are rsd * sd(y) / sd(x), where sd(y) and sd(x) are the scales of y and x at level 1. For ordered variables, the latent responses are scaled such that their (total) residual variances are 1, i.e., (1 - icc) * theta_W + icc * theta_B = 1, where theta_W and theta_B are the standardized residual variances at each level. The thresholds are rescaled accordingly.