Description
Conditional Process Analysis (CPA) via Structural Equation Modeling (SEM) Approach.
Description
Provides Reliability-Adjusted Product Indicator (RAPI) method to estimate effects among latent variables, thus allowing for more precise definition and analysis of mediation and moderation models. Our simulation studies reveal that while 'silp' may exhibit instability with smaller sample sizes and lower reliability scores (e.g., N = 100, omega = 0.7), implementing nearest positive definite matrix correction and bootstrap confidence interval estimation can significantly ameliorate this volatility. When these adjustments are applied, 'silp' achieves estimations akin in quality to those derived from latent moderated structural equations (LMS). In conclusion, the 'silp' package is a valuable tool for researchers seeking to explore complex relational structures between variables without resorting to commercial software. Hsiao et al.(2018)<doi:10.1177/0013164416679877> Kline & Moosbrugger(2000)<doi:10.1007/BF02296338> Cheung et al.(2021)<doi:10.1007/s10869-020-09717-0>.
README.md
silp
The goal of silp is to …
Installation
You can install the development version of silp from GitHub with: devtools::install_github(“TomBJJJ/silp”)
# install.packages("devtools")
devtools::install_github("TomBJJJ/silp")
Example
This is a basic example which shows you how to solve a common problem:
library(silp)
## basic example code
data = generate_data(100, 0.3, 0.4, c(1,1,1,1), 0.9)
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)
refit = resilp(fit)