Structural equation modeling is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with inter-correlated dependent and independent variables. Here we implement a simple method for spatially explicit structural equation modeling based on the analysis of variance co-variance matrices calculated across a range of lag distances. This method provides readily interpreted plots of the change in path coefficients across scale.
|Author||Eric Lamb [aut, cre], Kerrie Mengersen [aut], Katherine Stewart [aut], Udayanga Attanayake [aut], Steven Siciliano [aut]|
|Date of publication||2016-06-10 23:12:40|
|Maintainer||Eric Lamb <firstname.lastname@example.org>|
|License||GPL (>= 2)|
alexfiord: Alexandra Fiord transect dataset
avg.modindices: Function to display averaged modification indices for a...
bin.results: Extract results for a particular bin
bin.rsquare: Extract r-square values for dependant variables a spatial SEM...
calc.dist: Calculate intersample distances for a set of X-Y coordinates
gam.path: Prints and displays spatial sem results using gam models
make.bin: Function to make lag distance bins
make.covar: Function to calculate covariance matrices for a set of lag...
modelsummary: Function to extract and display basic summary information for...
plantcomp: Plant Competition dataset
plotbin: Function to plot the distribution of lag distance bin sizes
plotmodelfit: Function to plot model fit indices for spatial SEM analyses
plotpath: Function to plot spatial SEM results for individual paths
runModels: Run a spatial SEM analysis
sesem-package: Spatial structural equation modeling (SESEM)
truelove: Truelove lowland transect dataset
truelove_covar: Truelove lowland example covariances
truelove_results: Truelove lowland example sesem output