resilp: resilp

In silp: Conditional Process Analysis (CPA) via SEM Approach

resilp

Description

An extended function from silp, applying the bootstrap method to obtain standard error estimation. Note: When using silp with the nearest positive definite matrix (npd = TRUE), this function should be used to obtain reliable inference.

Usage

resilp(fit, R = 2000, progress = T, max_try = 100)

Arguments

fit	A result object from silp.
R	Integer. The number of bootstrap samples. Default is 2000.
progress	Logical. Whether to display a progress bar. Default is FALSE.
max_try	Maximum resampling attempts per bootstrap sample.

Value

An object of class "Silp".

Examples

n_obs = 100
corr = 0.1
effect = 0.12
ld = c(1,1,1,1)
alp = 0.9
data = generate_data(n_obs, corr, effect, ld, alp)
model = "
  fy =~ y1 + y2 + y3 + y4
  fx =~ x1 + x2 + x3 + x4
  fz =~ z1 + z2 + z3 + z4
  fy ~  fx + fz + fx:fz
"
fit = silp(model, data)
resilp(fit, R = 10)

silp documentation built on April 11, 2025, 6:18 p.m.