Description Usage Arguments Examples
This function plot posterior distributions of the parameters.
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o |
Observed vector |
p |
Predicted Gibbs samples |
nburnin |
numbe of burn-in itterations |
xlim |
x-axis range |
ylim |
y-axis range |
xlab |
x-axis label |
ylab |
y-axis label |
colSet |
vector of colors for points, bars and the 1:1 line |
cex |
cex value for size |
lwd |
line width |
pch |
pch value for symbols |
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 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ssSim <- phenoSim(nSites = 2, #number of sites
nTSet = 30, #number of Time steps
beta = c(1, 2), #beta coefficients
sig = .01, #process error
tau = .1, #observation error
plotFlag = TRUE, #whether plot the data or not
miss = 0.05, #fraction of missing data
ymax = c(6, 3) #maximum of saturation trajectory
)
ssOut <- fitCDM(x = ssSim$x, #predictors
nGibbs = 200,
nBurnin = 100,
z = ssSim$z,#response
connect = ssSim$connect, #connectivity of time data
quiet=TRUE)
summ <- getGibbsSummary(ssOut, burnin = 100, sigmaPerSeason = FALSE)
colMeans(summ$ymax)
colMeans(summ$betas)
colMeans(summ$tau)
colMeans(summ$sigma)
par(mfrow = c(1,3), oma = c(1,1,3,1), mar=c(2,2,0,1), font.axis=2)
plotPost(chains = ssOut$chains[,c("beta.1", "beta.2")], trueValues = ssSim$beta)
plotPost(chains = ssOut$chains[,c("ymax.1", "ymax.2")], trueValues = ssSim$ymax)
plotPost(chains = ssOut$chains[,c("sigma", "tau")], trueValues = c(ssSim$sig, ssSim$tau))
mtext('Posterior distributions of the parameters', side = 3, outer = TRUE, line = 1, font = 2)
legend('topleft', legend = c('posterior', 'true value'),
col = c('black', 'red'), lty = 1, bty = 'n', cex=1.5, lwd =2)
yGibbs <- ssOut$latentGibbs
zGibbs <- ssOut$zpred
o <- ssOut$data$z
p <- apply(ssOut$rawsamples$y, 1, mean)
R2 <- cor(na.omit(cbind(o, p)))[1,2]^2
#Plot Observed vs Predicted
par( mar=c(4,4,1,1), font.axis=2)
plotPOGibbs(o = o , p = zGibbs,
xlim = c(0,10), ylim=c(0,10),
cex = .7, nburnin = 1000)
points(o, p, pch = 3)
mtext(paste0('R² = ', signif(R2, 3)), line = -1, cex = 2, font = 2, side = 1, adj = .9)
legend('topleft', legend = c('mean', '95th percentile', '1:1 line', 'latent states'),
col = c('#fb8072','#80b1d3','black', 'black'),
bty = 'n', cex=1.5,
lty = c(NA, 1, 2, NA), lwd =c(NA, 2, 2, 2), pch = c(16, NA, NA, 3))
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