In cboettig/nonparametric-bayes: Nonparametric Bayes inference for ecological models

Custom plotting

For presentation figures for allen.Rmd script

model = "MLE"
ggplot(subset(dt, method == model)) + 
  geom_line(aes(time, fishstock, group=interaction(reps,method), color=method), alpha=.1) +
  scale_colour_manual(values=colorkey, guide = guide_legend(override.aes = list(alpha = 1))) +
  ylim(0, 9)
ggsave(paste(model, "_sim.pdf", sep=""), width=6, height=4)

dt$reps <- as.integer(dt$reps) 

require(animation)
ani.options(loop=TRUE)
saveMovie({    
    for (i in seq(1, OptTime, by=2)) {
      print(
        ggplot(subset(dt, method %in% c("True", "Ricker") & reps < 5 & time <= i), 
               aes(time, fishstock, group=interaction(reps,method), color = method), alpha=.9) + 
          geom_line() + xlim(0, OptTime)
        )
    }
}, interval = 0.1, movie.name = "wrong-model.gif", ani.width = 600, ani.height = 600)





require(MASS)
s <- length(gp_dat$Cf_posterior)

# Samples from the posterior of GPs
draws <- sapply(1:s, function(i)
  mvrnorm(1, gp_dat$Ef_posterior[,i], gp_dat$Cf_posterior[[i]]))
draws <- melt(data.frame(x=x_grid, draws), id="x")


mvrnorm(1, gp_f_at_obs$Ef_posterior[,i], gp_f_at_obs$Cf_posterior[[i]])

## Samples from the expected GP
gp_draws <- mvrnorm(6, gp_dat$E_Ef, gp_dat$E_Cf)
gp_draws <- melt(data.frame(x=x_grid, t(gp_draws)), id="x")

ggplot(tgp_dat) + geom_ribbon(aes(x,y,ymin=ymin,ymax=ymax), fill="gray80") +
  geom_line(aes(x,y,ymin=ymin,ymax=ymax)) +
  # geom_line(data=gp_draws, aes(x=x, y=value, col=variable))  +  
  geom_line(data=draws, aes(x=x, y=value, index=variable), alpha=.1)

cboettig/nonparametric-bayes documentation built on May 13, 2019, 2:09 p.m.