## Script to run GLMM fit on subset of sagebrush data
# Clear workspace
rm(list=ls(all=TRUE))
# Load libraries
library(sageAbundance)
library(rstan)
library(ggmcmc)
library(parallel)
library(reshape2)
library(plyr)
library(gridExtra)
####
## 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_V2small.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$Cover <- round(growD$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_smallSet.Rdata")
####
#### Write the STAN model
####
model_string <- "
data{
int<lower=0> nobs; // number of observations
int<lower=0> npres; // number of observations
int<lower=0> nabs; // number of observations
int<lower=0> pres;
int<lower=0> absd;
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
real y[nobs]; // observation vector
//real lag[nobs]; // lag cover vector
matrix[dK1,dK2] K; // spatial field matrix
matrix[nobs,ncovs] X; // spatial field matrix
}
parameters{
real int_mu;
real a;
real b;
//real<lower=0> beta_mu;
real<lower=0.000001> sig_a;
real<lower=0.000001> sig_mu;
vector[nknots] alpha;
vector[ncovs] beta;
}
transformed parameters{
vector[ncells] eta;
vector[nobs] mu;
vector[nobs] climEffs;
vector[npres] Y_a;
vector<lower=0, upper=1>[nabs] proba; // parameter for beta distn
int<lower=0> npatches[npres]; // parameter for beta distn
eta <- K*alpha;
climEffs <- X*beta;
for(n in 1:nobs)
mu[n] <- exp(int_mu + climEffs[n] + eta[cellid[n]]);
}
model{
// Priors
alpha ~ normal(0,sig_a);
sig_a ~ uniform(0,10);
sig_mu ~ uniform(0,10);
int_mu ~ normal(0,100);
//beta_mu ~ normal(0,10);
beta ~ normal(0,10);
a ~ gamma(2,5);
b ~ gamma(2,5);
// Likelihoods
//Strictly positive percent cover values
for(k in 1:npres){
npatches[k] ~ poisson(mu[pres[k]]);
Y_a[k] <- -a*npatches[k];
Y[pres[k]] ~ gamma(Y_a[k], b);
}
//Evaluation of probabilities of zeros
for(j in 1:nabs){
proba[j] <- 1 - exp(-mu[absd[j]]);
Y[abs[j]] ~ bernoulli(proba[j]);
}
}
"
####
#### Send data to STAN function for fitting
####
y = growD$Cover
lag = growD$CoverLag
K = K.data$K
cellid = growD$ID
X = growD[,c("pptLag", "ppt1", "ppt2", "TmeanSpr1", "TmeanSpr2")]
X = scale(X, center = TRUE, scale = TRUE)
abs <- which(y==0)
pres <- which(y>0)
npres <- length(pres)
nabs <- length(abs)
# inits <- list()
# inits[[1]] <- list(int_mu = 1, beta_mu = 0.05, beta = rep(0, ncol(X)),
# alpha = rep(0,ncol(K.data$K)), sigma=0.05, sig_a=0.05,
# sig_mu=0.05, lambda=rep(1, length(y)), phi=10)
# inits[[2]] <- list(int_mu = 2, beta_mu = 0.01, beta = rep(0.5, ncol(X)),
# alpha = rep(0.5,ncol(K.data$K)), sigma=0.02, sig_a=0.005,
# sig_mu=0.025, lambda=rep(10, length(y)), phi=20)
# 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)), sigma=0.04, sig_a=0.025,
# sig_mu=0.5, lambda=rep(5, length(y)), 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), nabs=nabs, npres=npres, pres=pres, absd=abs)
pars <- c("int_mu", "alpha", "beta", "phi")
# Compile the model
mcmc_samples <- stan(model_code=model_string, data=datalist, init = list(inits[[1]]),
pars=pars, chains=0)
# test <- stan(fit=mcmc_samples, data=datalist, pars=pars,
# chains=1, iter=200, warmup=100)
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