scripts/cache/run_large_test.R

In atredennick/sageAbundance: Fits population model for sagebrush based on remote sensing data.

##  Script to run GLMM fit on subset of sagebrush data

library(sageAbundance)
library(rstan)
library(ggmcmc)
# library(parallel)

####
####  Get data
####
datapath <-  "/Users/atredenn/Dropbox/sageAbundance_data/"
climD <- read.csv(paste(datapath,
                        "/studyarea1/climate/DAYMET/FormattedClimate_WY_SA1.csv",
                        sep=""))
rawD <- read.csv(paste(datapath, "WY_SAGECoverData_V2check.csv", sep=""))

# Merge in climate data 
fullD <- merge(rawD,climD,by.x="Year", by.y="year",all.x=T)

# Get data structure right
growD <- subset(fullD, Year>1984) # get rid of NA lagcover years
# growD <- subset(growD, Year<1994) # subset just 10 years
growD$Cover <- round(growD$Cover,0) # round for count-like data
growD$CoverLag <- round(growD$CoverLag,0) # round for count-like data

# Load knot data
load("../results/Knot_cell_distances.Rdata")
K <- K.data$K

# Remove NA values
rms <- which(is.na(growD$Cover) == TRUE)
rmsK <- which(is.na(subset(growD, Year==1985)["Cover"])==TRUE)
K <- K[-rmsK,]
growD <- growD[-rms, ]
# growD[rms, "Cover"] <- 0
# growD[rms, "CoverLag"] <- 0

# Set up new, continuous cell IDs
num_ids <- length(unique(growD$ID))
num_reps <- nrow(growD)/num_ids
new_ids <- rep(c(1:num_ids), num_reps)
growD$newID <- new_ids

####
####  Write the STAN model
####
model_string <- "
data{
  int<lower=0> nobs; // number of observations
  int<lower=0> ncovs; // number of climate covariates
  int<lower=0> nknots; // number of interpolation knots
  int<lower=0> ncells; // number of cells
  int<lower=0> cellid[nobs]; // cell id
  int<lower=0> dK1; // row dim for K
  int<lower=0> dK2; // column dim for K
  int y[nobs]; // observation vector
  int lag[nobs]; // lag cover vector
  matrix[dK1,dK2] K; // spatial field matrix
  matrix[nobs,ncovs] X; // spatial field matrix
}
parameters{
  real int_mu;
  real<lower=0> beta_mu;
  real<lower=0.0001> sig_a;
  vector[nknots] alpha;
  vector[ncovs] beta;
  real<lower=0.0001> phi; // neg. binomial dispersion parameter
}
transformed parameters{
  vector[ncells] eta;
  vector[nobs] mu;
  vector[nobs] climEffs;
  eta <- K*alpha;
  climEffs <- X*beta;
  for(n in 1:nobs)
    mu[n] <- int_mu + beta_mu*lag[n] + climEffs[n] + eta[cellid[n]];
}
model{
  // Priors
  alpha ~ normal(0,sig_a);
  int_mu ~ normal(0,100);
  beta_mu ~ normal(0,10);
  beta ~ normal(0,10);
  sig_a ~ cauchy(0,5);
  phi ~ cauchy(0,5);
  // Likelihood
  y ~ neg_binomial_2_log(mu, phi);
}
"

####
####  Send data to STAN function for fitting
####
y = growD$Cover
lag = growD$CoverLag
# K = K.data$K
cellid = growD$newID
# cellid = growD$ID
X = growD[,c("pptLag", "ppt1", "ppt2", "TmeanSpr1", "TmeanSpr2")]
X = scale(X, center = TRUE, scale = TRUE)

inits <- list()
inits[[1]] <- list(int_mu = 1, beta_mu = 0.05, beta = rep(0, ncol(X)),
                   alpha = rep(0,ncol(K.data$K)), sig_a=0.05,
                   phi=10)
inits[[2]] <- list(int_mu = 2, beta_mu = 0.01, beta = rep(0.5, ncol(X)),
                   alpha = rep(0.5,ncol(K.data$K)), sig_a=0.005,
                   phi=50)
inits[[3]] <- list(int_mu = 1.5, beta_mu = 0.02, beta = rep(0.2, ncol(X)),
                   alpha = rep(0.25,ncol(K.data$K)), sig_a=0.025,
                   phi=100)

datalist <- list(y=y, lag=lag, nobs=length(lag), ncells=length(unique(cellid)),
                 cellid=cellid, nknots=ncol(K), K=K, dK1=nrow(K), dK2=ncol(K),
                 X=X, ncovs=ncol(X))
pars <- c("int_mu", "beta_mu",  "alpha", "beta", "phi")
  
# Compile the model
mcmc_config <- stan(model_code=model_string, data=datalist,
                    pars=pars, chains=0)

test <- stan(fit=mcmc_config, data=datalist, pars=pars, chains=1,
             iter=100, warmup=50, init=list(inits[[2]]))
traceplot(test)
# Run parallel chains
# rng_seed <- 123
# sflist <-
#   mclapply(1:3, mc.cores=3,
#             function(i) stan(fit=mcmc_samples, data=datalist, pars=pars,
#                             seed=rng_seed, chains=1, chain_id=i, refresh=-1,
#                             iter=2000, warmup=1000, init=list(inits[[i]])))
# fit <- sflist2stanfit(sflist)
# long <- ggs(fit)
# saveRDS(mcmc, "stanmcmc_sage.RDS")
# 
#