sesem (version 1.0.1)

gam.path: Prints and displays spatial sem results using gam models

Description

This function fits generalized additive models (gam) of the path coefficient vs. lag distance relationship for each path in the spatial SEM model. Gam functions and figures are produced. Requires function mgcv

Usage

gam.path(spatial_model_results, path.type = "directed",selectpath = "none selected", 
	plot.points = T, se.plot = T, lwd.pred = 2, lty.pred = 1, lwd.se = 2, lty.se = 3, 
	cex = 1, cex.axis = 1, cex.lab = 1, xlab = "Lag Distance", 
	ylab = "Unst. Path Coeff.", yaxt = "s", xaxt = "s")

Arguments

spatial_model_results

a list object produced by function runModels

path.type

An option to select the paths to be plotted. "directed" = only directed paths plotted; "undirected" = only undirected correlations plotted; "both" = all paths plotted; "user" = allows user to specify particular paths and a particular order for plottin

selectpath

An option to select specific paths for plotting. Usage is as follows: selectpath==c(5,18,16,23,29) where values refer to path numbers. Path numbers can be obtained using spatial_model_results[[2]]

plot.points

Should points for individual models be plotted?

se.plot

Should standard error lines for each gam model be plotted?

lwd.pred

width of the predicted line from the gam model

lty.pred

format of the predicted line from the gam model

lwd.se

width of the standard error line

lty.se

format of the standard error line

cex

point size

cex.axis

axis font size

cex.lab

label font size

xlab

x-axis label

ylab

y-axis label

yaxt

argument to suppress plotting of y-axis if set to "n"

xaxt

argument to suppress plotting of x-axis if set to "n"

Details

Generalized additive models (gam) allow flexible modeling of nonlinear relationships with minimal assumptions about the shape of those relationships. This function utilizes the gam fitting function in library mgcv to fit and display gam models of the relationships between lag distance and unstandardized path coefficients. This is an alternative to the lowess smoothed lines available in function plot.path. Potential advantages of the gam models include the ability to obtain predictions for lag distance values intermediate between modeled lag distances.

References

Lamb, E. G., K. Mengersen, K. J. Stewart, U. Attanayake, and S. D. Siciliano. Submitted. Spatially explicit structural equation modeling. Ecology. Rosseel, Y. 2012 lavaan: an R package for structural equation modeling. Journal of Statistical Software 48:1-36 Wood, S.N. 2011 Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36 Wood, S.N. 2006 Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC

See Also

sem, gam, make.covar, runModels, modelsummary, plotmodelfit, plotpath

Examples

#data=truelove
#distancematrix<-calc.dist(truelove)
#Truelove_bins<-make.bin(distancematrix,type="ALL",p.dist=20)
#binsize<-Truelove_bins[1][[1]] #truelove lowland bin sizes
#binname<-Truelove_bins[2][[1]] #truelove lowland bin names

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

# path model for the truelove dataset
#spatial_model<-'
#	Gram ~ Moisture
#	N_Fix ~ Bryoph + Lich + SoilCrust
#	SoilCrust ~ Bryoph + Lich + Gram + Shrubs + Forbs	
#	Bryoph ~ Gram + Shrubs + Forbs + Moisture
#	Lich ~ Moisture + Forbs + Gram + Shrubs + Bryoph
#	Forbs ~ Moisture
#	Gram ~~ Forbs
#	Shrubs ~ Moisture	
#	Gram ~~ Shrubs
#	Shrubs ~~ Forbs
#	'
#
#results<-runModels(spatial_model,covariances)

#The above script produces the sesem object stored as truelove_results

data=truelove_results

gam.path(truelove_results)
truelove_results[[2]]# list of path names
gam.path(truelove_results,path.type="user",selectpath=c(5,7,8))