testing = FALSE
if ( testing) {
ca = speciescomposition.db(DS="ca", p=p)
ca$variance
pca = speciescomposition.db(DS="pca", p=p)
toplot = pca$cscores
spec = as.numeric( as.character( rownames( toplot )))
rownames(toplot) = taxonomy.recode( from="spec", tolookup=spec)
plot.ordination (X=toplot)
# spatial autocorrelation
require(gstat)
drange = 150
nmax = 200
SC = speciescomposition.db( DS="speciescomposition.merged", p=p )
sc = SC[ SC$yr==2011,]
g = gstat(id="sc", formula=pca1~1, loc=~plon+plat, data=sc, maxdist=drange, nmax=nmax )
g.e = variogram(g, cutoff=drange, cressie=T)
plot(g.v)
# spBayes attempt
require( spBayes)
o = spLM( pca1~1, coords=as.matrix(sc[,c("plon","plat")]), data=sc,
knots=c(6,6,0),
starting=list("phi"=0.6,"sigma.sq"=1, "tau.sq"=1),
sp.tuning=list("phi"=0.01, "sigma.sq"=0.01, "tau.sq"=0.01),
priors=list("phi.Unif"=c(0.3, 3), "sigma.sq.IG"=c(2, 1),
"tau.sq.IG"=c(2, 1)),
cov.model="exponential",
n.samples=1000, verbose=TRUE)
print(summary(o$p.samples))
plot(o$p.samples)
##Requires MBA package to
##make surfaces
library(MBA)
par(mfrow=c(1,2))
obs.surf <-
mba.surf(cbind(coords, w), no.X=100, no.Y=100, extend=TRUE)$xyz.est
image(obs.surf, xaxs = "r", yaxs = "r", main="Observed response")
points(coords)
contour(obs.surf, add=T)
w.hat <- rowMeans(o$sp.effects)
w.surf <-
mba.surf(cbind(coords, w.hat), no.X=100, no.Y=100, extend=TRUE)$xyz.est
image(w.surf, xaxs = "r", yaxs = "r", main="Estimated random effects")
contour(w.surf, add=T)
points(coords, pch=1, cex=1)
points(o$knot.coords, pch=19, cex=1)
legend(1.5,2.5, legend=c("Obs.", "Knots"), pch=c(1,19), bg="white")
# GAM analysis
Family: gaussian
Link function: identity
Formula:
ca1 ~ s(plon, plat, k = 400) + s(z, k = 3) + s(t, k = 3) + s(yr) +
s(julian, k = 3)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04727 0.00344 13.8 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(plon,plat) 280.90 344.55 27.4 <2e-16 ***
s(z) 1.00 1.00 89.9 <2e-16 ***
s(t) 1.59 1.83 282.4 <2e-16 ***
s(yr) 8.97 9.00 1611.3 <2e-16 ***
s(julian) 1.99 2.00 382.1 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.795 Deviance explained = 79.8%
GCV score = 0.21988 Scale est. = 0.21633 n = 18320
>
Family: gaussian
Link function: identity
Formula:
ca2 ~ s(plon, plat, k = 400) + s(z, k = 3) + s(t, k = 3) + s(yr) +
s(julian, k = 3)
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.09189 0.00309 29.8 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Approximate significance of smooth terms:
edf Ref.df F p-value
s(plon,plat) 342.60 384.87 22.2 <2e-16 ***
s(z) 1.80 1.95 3056.5 <2e-16 ***
s(t) 1.00 1.00 239.2 <2e-16 ***
s(yr) 8.86 8.99 466.4 <2e-16 ***
s(julian) 2.00 2.00 506.5 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
R-sq.(adj) = 0.824 Deviance explained = 82.8%
GCV score = 0.17794 Scale est. = 0.17447 n = 18320
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.