sesem (version 1.0.1)

plotmodelfit: Function to plot model fit indices for spatial SEM analyses

Description

A function to plot model fit indices across lag distances. The default is to plot all of the chi square, cfi, rmsea (including confidence intervals), and srmr indices. Horizontal lines indicating significant cutoffs for each index are plotted (chi square p=0.05, cfi=0.9, rmsea=0.05, and srmr=0.08). Options to add trend lines are available.

Usage

plotmodelfit(spatial_model_results, plots = "all", add.line = "none", rmsea_err = T, 
     pch = 16, lwd = 2, lty = 1, cex = 1, cex.lab = 1, cex.axis = 1, cex.main = 1.5)

Arguments

spatial_model_results

a list object produced by function runModels

plots

Indicates which indices should be plotted. The default "all" produces plots of all of the chi square, cfi, rmsea (including confidence intervals), and srmr indices. plot="chi", "cfi", "rmsea", or "srmr" will produce only a single plot.

add.line

Indicates whether a trendline should be added connecting the points. The default "none" indicates no line, "step" plots straight line segments between points, and "smooth" plots a smoothed curve fit using function lowess. Smoothed lines do not include the

rmsea_err

Should the rmsea index be plotted with confidence intervals? rmsea_err=T is the default, rmsea_err=F will suppress confidence intervals. Note that warnings will likely arise if rmsea_err=T is used and there are confidence intervals of zero. All these warn

pch

plotting symbol

lwd

line width

lty

line format

cex

symbol size

cex.lab

label font size

cex.axis

axis label font size

cex.main

plot title font size

References

Lamb, E. G., K. Mengersen, K. J. Stewart, U. Attanayake, and S. D. Siciliano. Submitted. Spatially explicit structural equation modeling. Ecology.

See Also

sem, make.covar, runModels, modelsummary, avg.modindices, plotpath, gam.path

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

plotmodelfit(truelove_results) #note that the warnings that arise here can be ignored
plotmodelfit(truelove_results,rmsea_err=FALSE)
plotmodelfit(truelove_results,plots="chi")