sesem-package: Spatial structural equation modeling (SESEM)
In sesem: Spatially Explicit Structural Equation Modeling

Description Details Author(s) References See Also Examples

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex datasets with intercorrelated dependent and independent variables. Here we implement a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale.

Package:	sesem
Type:	Package
Version:	1.0
Date:	2016-06-09
License:	GPL (>= 2)

Package sesem allows spatially explicit structural equation modeling. It allows a structural equation model to be fit to a number of spatially explicit covariance matrices to explore how the strength of structural path coefficients changes with scale. In brief an SESEM analysis involves: calculating pairwise differences among samples selecting a series of lag distance bins generating spatially explicit variance - covariance matrices for each lag distance bin fitting a structural equation model to each of those bins comparing and examining how the strength of path coefficients change with scale

Eric G. Lamb, Kerrie Mengersen, Katherine J. Stewart, Udayanga Attanayake, and Steven D. Siciliano

Maintainer: Eric Lamb <eric.lamb@usask.ca>

Lamb, E. G., K. Mengersen, K. J. Stewart, U. Attanayake, and S. D. Siciliano. 2014. Spatially explicit structural equation modeling. Ecology 95:2434-2442.

Rosseel, Y. 2012 lavaan: an R package for structural equation modeling. Journal of Statistical Software 48:1-36.

sem, lavaan

data=truelove
truelove_red<-truelove[c(1:60),c(1:7)]
distancematrix<-calc.dist(truelove_red)
Truelove_bins<-make.bin(distancematrix,type="ALL",p.dist=10)
binsize<-Truelove_bins[1][[1]] #truelove lowland bin sizes
binname<-Truelove_bins[2][[1]] #truelove lowland bin names

plotbin(distancematrix,binsize)

covariances<-make.covar(truelove_red,distancematrix,binsize,binname)
covariances

# reduced path model for the truelove dataset

spatial_model<-'
	N_Fix ~ Bryoph + Lich + SoilCrust
	SoilCrust ~ Bryoph + Lich	
	Lich ~ Bryoph + Moisture
	Bryoph ~ Moisture
	'

results<-runModels(spatial_model,covariances)
modelsummary(results)
plotmodelfit(results)
plotpath(results)