Description Usage Arguments Details Author(s) References See Also Examples
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
1 2 3 4 | 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")
|
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 plotting. Argument selectpath must also be provided with path.type="user" |
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" |
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.
Eric Lamb
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
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
sem
, gam
, make.covar
, runModels
,
modelsummary
, plotmodelfit
, plotpath
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | #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))
|
path.name gam.dev.exp gam.s.edf gam.s.Ref.df gam.s.F gam.s.pvalue
1 Gram ~ Moisture 0.967 8.101 8.783 52.377 0.000
2 N_Fix ~ Bryoph 0.814 4.506 5.531 14.838 0.000
3 N_Fix ~ Lich 0.366 2.157 2.690 4.219 0.022
4 N_Fix ~ SoilCrust 0.725 5.963 7.120 6.076 0.001
5 SoilCrust ~ Bryoph 0.912 6.243 7.397 24.288 0.000
6 SoilCrust ~ Lich 0.421 4.764 5.826 1.812 0.146
7 SoilCrust ~ Gram 0.572 4.061 5.009 4.860 0.004
8 SoilCrust ~ Shrubs 0.668 5.529 6.671 4.989 0.002
9 SoilCrust ~ Forbs 0.052 1.000 1.000 1.271 0.271
10 Bryoph ~ Gram 0.927 6.954 8.033 26.334 0.000
11 Bryoph ~ Shrubs 0.655 4.542 5.571 5.907 0.001
12 Bryoph ~ Forbs 0.367 1.000 1.000 13.313 0.001
13 Bryoph ~ Moisture 0.667 6.103 7.260 4.452 0.004
14 Lich ~ Moisture 0.294 1.980 2.470 3.062 0.054
15 Lich ~ Forbs 0.444 2.375 2.959 4.955 0.009
16 Lich ~ Gram 0.648 5.862 7.018 4.220 0.006
17 Lich ~ Shrubs 0.879 7.843 8.652 12.896 0.000
18 Lich ~ Bryoph 0.935 7.058 8.118 29.450 0.000
19 Forbs ~ Moisture 0.131 1.798 2.242 0.953 0.378
20 Shrubs ~ Moisture 0.814 7.201 8.228 8.291 0.000
max.path.value
1 100.000
2 92.242
3 100.000
4 35.030
5 82.545
6 79.636
7 4.000
8 82.545
9 100.000
10 37.939
11 29.212
12 4.000
13 88.364
14 39.879
15 47.636
16 100.000
17 4.000
18 92.242
19 47.636
20 17.576
parameter.number parameter.name
1 1 Gram ~ Moisture
2 2 N_Fix ~ Bryoph
3 3 N_Fix ~ Lich
4 4 N_Fix ~ SoilCrust
5 5 SoilCrust ~ Bryoph
6 6 SoilCrust ~ Lich
7 7 SoilCrust ~ Gram
8 8 SoilCrust ~ Shrubs
9 9 SoilCrust ~ Forbs
10 10 Bryoph ~ Gram
11 11 Bryoph ~ Shrubs
12 12 Bryoph ~ Forbs
13 13 Bryoph ~ Moisture
14 14 Lich ~ Moisture
15 15 Lich ~ Forbs
16 16 Lich ~ Gram
17 17 Lich ~ Shrubs
18 18 Lich ~ Bryoph
19 19 Forbs ~ Moisture
20 20 Gram ~~ Forbs
21 21 Shrubs ~ Moisture
22 22 Gram ~~ Shrubs
23 23 Forbs ~~ Shrubs
24 24 Gram ~~ Gram
25 25 N_Fix ~~ N_Fix
26 26 SoilCrust ~~ SoilCrust
27 27 Bryoph ~~ Bryoph
28 28 Lich ~~ Lich
29 29 Forbs ~~ Forbs
30 30 Shrubs ~~ Shrubs
31 31 Moisture ~~ Moisture
path.name gam.dev.exp gam.s.edf gam.s.Ref.df gam.s.F gam.s.pvalue
1 SoilCrust ~ Bryoph 0.912 6.243 7.397 24.288 0.000
2 SoilCrust ~ Gram 0.572 4.061 5.009 4.860 0.004
3 SoilCrust ~ Shrubs 0.668 5.529 6.671 4.989 0.002
max.path.value
1 82.545
2 4.000
3 82.545
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