Description
Network Structural Equation Modeling
Description
The network structural equation modeling conducts a network statistical analysis on a data frame of coincident observations of multiple continuous variables [1]. It builds a pathway model by exploring a pool of domain knowledge guided candidate statistical relationships between each of the variable pairs, selecting the 'best fit' on the basis of a specific criteria such as adjusted r-squared value. This material is based upon work supported by the U.S. National Science Foundation Award EEC-2052776 and EEC-2052662 for the MDS-Rely IUCRC Center, under the NSF Solicitation: NSF 20-570 Industry-University Cooperative Research Centers Program [1] Bruckman, Laura S., Nicholas R. Wheeler, Junheng Ma, Ethan Wang, Carl K. Wang, Ivan Chou, Jiayang Sun, and Roger H. French. (2013) <doi:10.1109/ACCESS.2013.2267611>.
README.md
netSEM
The R package 'netSEM' conducts a net-SEM statistical analysis (network structural equation modeling) on a data frame of coincident observations of multiple continuous variables. Principle 1 generates an inferential model through pairwise correlation of variables based on the Markovian Spirit. Principle 2 provides a predictive model through multiple regression with the model complexity and performance evaluated using either Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) specified by the user.
netSEM Usage
This is a simple example for generating degradation models using netSEM principle 1 and principle 2.
# Load in netSEM library
library(netSEM)
# Load in example data
data(acrylic)
# Perform principle 1 and principle 2
acrylic_p1 <- netSEMp1(acrylic)
acrylic_p2 <- netSEMp2(acrylic, criterion = "AIC") #AIC by default
# Plotting netSEM diagrams
plot(acrylic_p1, cutoff = c(0.3,0.6,0.8))
plot(acrylic_p2, cutoff = c(0.3,0.6,0.8))