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HierarchicalPartitioning_Summer_2004-2018.R
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HierarchicalPartitioning_Summer_2004-2018.R
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###########################################################################################################################################
#Full-annual-cycle model for monarch butterflies in eastern North America
#Years included: 2004-2018
#Running summer submodels with different combinations of covariate "groups"
#Using hierarchical partitioning methods to calculate percent of explained variation in summer counts attributable to each covariate group.
###########################################################################################################################################
#------------------------------------------------------------#
# Set working directory and load packages
#------------------------------------------------------------#
setwd()
# rm(list=ls()) #clear workspace if needed
library(plyr)
library(reshape2)
library(rstan)
rstan_options(auto_write = TRUE)
#------------------------------------------------------------#
# Read in data (assuming these files are in a "Data" subfolder)
#------------------------------------------------------------#
#Summer monarch data
summer <- read.csv('Data/Monarchs_summer.csv',header=TRUE,stringsAsFactors=FALSE)
#Covariates: year
cov.y <- read.csv('Data/Covariates_Year.csv',header=TRUE)
#Covariates: county
cov.c <- read.table('Data/Covariates_County.txt',sep='\t',header=TRUE,quote='\"',
colClasses=c(rep('numeric',2),rep('character',4),rep('numeric',8)))
#Covariates: county * year
cov.cy <- read.csv('Data/Covariates_CountyYear.csv',header=TRUE)
#Covariates: county * year * week
cov.cw <- read.csv('Data/Covariates_CountyWeek.csv',header=TRUE)
#------------------------------------------------------------#
# Format summer survey data
#------------------------------------------------------------#
#Specify number of weeks, years, counties, sites, etc.
uyears <- sort(unique(summer$yr))
n_years <- length(uyears)
uweeks <- sort(unique(summer$wk))
n_weeks <- length(uweeks)
ucounties <- cov.c$county.ind
n_counties <- length(ucounties)
usites <- sort(unique(summer$site.ind))
n_sites <- length(usites)
#Add indicators for state monitoring programs (using NABA as a reference level)
summer$ia.ind <- ifelse(summer$program=='Iowa',1,0)
summer$il.ind <- ifelse(summer$program=='Illinois',1,0)
summer$mi.ind <- ifelse(summer$program=='Michigan',1,0)
summer$oh.ind <- ifelse(summer$program=='Ohio',1,0)
#Scale effort by the mean
effortmean <- mean(summer$duration) #2.85 party hours
summer$effort.sc <- summer$duration/effortmean
#Standardize estimates of percent non-forested in immediate survey area (open)
openS.m <- mean(summer$perc.open)
openS.sd <- sd(summer$perc.open)
summer$openS.st <- (summer$perc.open - openS.m)/openS.sd
#------------------------------------------------------------#
# Standardize annual covariates for summer model
#------------------------------------------------------------#
#Size of monarch population in late February (Feb)
feb.m <- mean(cov.y$feb)
feb.sd <- sd(cov.y$feb)
cov.y$feb.st <- (cov.y$feb - feb.m)/feb.sd
#GDD in spring, eastern Texas (spGDD)
spGDD.m <- mean(cov.y$spGDD.east)
spGDD.sd <- sd(cov.y$spGDD.east)
cov.y$spGDD.st <- (cov.y$spGDD.east - spGDD.m)/spGDD.sd
#Quadratic
cov.y$spGDD.st2 <- cov.y$spGDD.st*cov.y$spGDD.st
#Precipitation in spring, eastern Texas (spPCP)
spPCP.m <- mean(cov.y$spPCP.east)
spPCP.sd <- sd(cov.y$spPCP.east)
cov.y$spPCP.st <- (cov.y$spPCP.east - spPCP.m)/spPCP.sd
#Quadratic
cov.y$spPCP.st2 <- cov.y$spPCP.st*cov.y$spPCP.st
#------------------------------------------------------------#
# Standardize county covariates
#------------------------------------------------------------#
#First, make sure dataframe is sorted by county index
cov.c <- cov.c[with(cov.c,order(county.ind)),]
#Average GDD for weeks 10-24 across all years of the study (avgGDD)
avgGDD.m <- mean(cov.c$avgGDD)
avgGDD.sd <- sd(cov.c$avgGDD)
cov.c$avgGDD.st <- (cov.c$avgGDD - avgGDD.m)/avgGDD.sd
#Average precipitation for AMJJA across all years of the study (avgPCP)
avgPCP.m <- mean(cov.c$avgPCP)
avgPCP.sd <- sd(cov.c$avgPCP)
cov.c$avgPCP.st <- (cov.c$avgPCP - avgPCP.m)/avgPCP.sd
#Percent crop (crop)
cropC.m <- mean(cov.c$perc.crop)
cropC.sd <- sd(cov.c$perc.crop)
cov.c$cropC.st <- (cov.c$perc.crop - cropC.m)/cropC.sd
#------------------------------------------------------------#
# Standardize county*year covariates
#------------------------------------------------------------#
#First, make sure dataframe is sorted by county and year index
cov.cy <- cov.cy[with(cov.cy,order(yr,county.ind)),]
#Proportion of corn/soy crops sprayed with glyphosate (gly)
gly.m <- mean(cov.cy$glyphosate)
gly.sd <- sd(cov.cy$glyphosate)
cov.cy$gly.st <- (cov.cy$glyphosate-gly.m)/gly.sd
#Summer precipitation (annual deviations from 2004-2018 means for AMJJA) (diffPCP)
diffPCP.m <- mean(cov.cy$diffPCP)
diffPCP.sd <- sd(cov.cy$diffPCP)
cov.cy$diffPCP.st <- (cov.cy$diffPCP - diffPCP.m)/diffPCP.sd
#------------------------------------------------------------#
# Standardize county*week covariates
#------------------------------------------------------------#
#First, make sure dataframe is sorted by county, week, and year index
cov.cw <- cov.cw[with(cov.cw,order(yr,wk,county.ind)),]
#Difference between GDD and average GDD for that week and county across all years of the study (diffGDD)
diffGDD.m <- mean(cov.cw$diffGDD)
diffGDD.sd <- sd(cov.cw$diffGDD)
cov.cw$diffGDD.st <- (cov.cw$diffGDD-diffGDD.m)/diffGDD.sd
#------------------------------------------------------------#
# Combine ecological covariates for summer model in long form (nrows = counties*years*weeks)
#------------------------------------------------------------#
cyw <- expand.grid(wk=uweeks,county.ind=ucounties,yr=uyears)
cyw$yr.ind <- cyw$yr-2003
#Standarize week
wk.m <- mean(cyw$wk)
wk.sd <- sd(cyw$wk)
cyw$wk.st <- (cyw$wk-wk.m)/wk.sd
cyw <- join(cyw,cov.y[,c('yr','feb.st','spGDD.st','spGDD.st2','spPCP.st','spPCP.st2')],by='yr',type='left')
cyw <- join(cyw,cov.c[,c('county.ind','avgGDD.st','avgPCP.st','cropC.st')],by='county.ind',type='left')
cyw <- join(cyw,cov.cy[,c('county.ind','yr','diffPCP.st','gly.st')],by=c('county.ind','yr'),type='left')
cyw <- join(cyw,cov.cw[,c('county.ind','yr','wk','diffGDD.st')],by=c('county.ind','yr','wk'),type='left')
cyw$diffavgGDD <- cyw$diffGDD.st*cyw$avgGDD.st
cyw$diffGDD.st2 <- cyw$diffGDD.st*cyw$diffGDD.st
cyw$diffavgPCP <- cyw$diffPCP.st*cyw$avgPCP.st
cyw$diffPCP.st2 <- cyw$diffPCP.st*cyw$diffPCP.st
cyw$glycrop <- cyw$gly.st*cyw$cropC.st
cyw <- cyw[,c('county.ind','yr','yr.ind','wk','wk.st','feb.st','spGDD.st','spGDD.st2','spPCP.st','spPCP.st2',
'avgGDD.st','diffGDD.st','diffGDD.st2','diffavgGDD',
'avgPCP.st','diffPCP.st','diffPCP.st2','diffavgPCP',
'gly.st','cropC.st','glycrop')]
names(cyw)[6:21] <- c('feb','spGDD','spGDD2','spPCP','spPCP2',
'avgGDD','diffGDD','diffGDD2','diffavgGDD',
'avgPCP','diffPCP','diffPCP2','diffavgPCP',
'gly','crop','glycrop')
#Need to sort in a particular way to create the model-based index in STAN
#Put first 5 weeks (16-20) on top
#Then sort week-fastest, year-slowest: first 4 rows would be county=1, yr=2004, wk=21-24, next 4 rows = county=2, yr=2004, wk=21-24
cyw15 <- cyw[cyw$wk %in% 16:20,]
cyw69 <- cyw[cyw$wk %in% 21:24,]
# head(cyw69); tail(cyw69)
cyw <- rbind(cyw15,cyw69)
#Create an index for unique combinations of county, year, and week
cyw$cyw.ind <- 1:nrow(cyw)
#Attach indices to the survey data
summer <- join(summer,cyw[,c('county.ind','wk','yr','cyw.ind')],by=c('county.ind','wk','yr'),type='left')
#------------------------------------------------------------#
# Calculate observed values for posterior predictive checks
#------------------------------------------------------------#
#Summer counts
#Counts divided by scaled effort (note: includes 0 counts)
sccounts <- summer$monarch/summer$effort.sc
sccount.mn <- mean(sccounts)
sccount.sd <- sd(sccounts)
#------------------------------------------------------------#
# Package up data, initial values, parameters, and run in STAN
#------------------------------------------------------------#
#Survey-level covariates in matrix
X_survey <- as.matrix(summer[,c('ia.ind','il.ind','mi.ind','oh.ind','openS.st')])
#Parameters to monitor
params <- c('alpha0','alphaFE','alpha_week','alpha_week2','betaFE','phi',
'sccount_pred_mn','sccount_pred_sd','sum_log_lik')
#MCMC parameter
ni <- 3000 # No. iterations (including warmup)
nb <- 500 # No. burn-in iterations to discard (ie, warmup)
nt <- 1 # Thin rate
nc <- 3 # No. chains
#-Null model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
#,'feb'
#,'spGDD','spGDD2','spPCP','spPCP2'
#,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
#,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.null <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-February model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
,'feb'
#,'spGDD','spGDD2','spPCP','spPCP2'
#,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
#,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.F <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-Spring weather model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
#,'feb'
,'spGDD','spGDD2','spPCP','spPCP2'
#,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
#,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.spC <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-Summer weather model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
#,'feb'
#,'spGDD','spGDD2','spPCP','spPCP2'
,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
#,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.suC <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-Summer land-use model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
#,'feb'
#,'spGDD','spGDD2','spPCP','spPCP2'
#,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.suL <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-February, spring weather model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
,'feb'
,'spGDD','spGDD2','spPCP','spPCP2'
#,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
#,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.FspC <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-February, summer weather model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
,'feb'
#,'spGDD','spGDD2','spPCP','spPCP2'
,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
#,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.FsuC <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-February, summer land-use model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
,'feb'
#,'spGDD','spGDD2','spPCP','spPCP2'
#,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.FsuL <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-Spring weather, summer weather model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
#,'feb'
,'spGDD','spGDD2','spPCP','spPCP2'
,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
#,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.spCsuC <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-Spring weather, summer land-use model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
#,'feb'
,'spGDD','spGDD2','spPCP','spPCP2'
#,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.spCsuL <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-Summer weather, summer land-use model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
#,'feb'
#,'spGDD','spGDD2','spPCP','spPCP2'
,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.suCsuL <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-February, spring weather, summer weather model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
,'feb'
,'spGDD','spGDD2','spPCP','spPCP2'
,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
#,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.FspCsuC <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-February, spring weather, summer land-use model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
,'feb'
,'spGDD','spGDD2','spPCP','spPCP2'
#,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.FspCsuL <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-February, summer weather, summer land-use model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
,'feb'
#,'spGDD','spGDD2','spPCP','spPCP2'
,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.FsuCsuL <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-Spring weather, summer weather, summer land-use model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
#,'feb'
,'spGDD','spGDD2','spPCP','spPCP2'
,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.spCsuCsuL <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#-February, spring weather, summer weather, summer land-use model----------------------------------------------------------------#
#County-year-week covariates
X_county <- as.matrix(cyw[,c('avgGDD','avgPCP','crop'
,'feb'
,'spGDD','spGDD2','spPCP','spPCP2'
,'diffGDD','diffGDD2','diffavgGDD','diffPCP','diffPCP2','diffavgPCP'
,'gly','glycrop'
)])
#Bundle data
standata <- list(n_years=n_years,
n_counties=n_counties,
n_cyw=nrow(cyw),
n_surveys=nrow(summer),
year_id=cyw$yr.ind,
cyw_id=summer$cyw.ind,
n_cov_alpha=ncol(X_county),
n_cov_beta=ncol(X_survey),
X_county=X_county,
week_st=cyw$wk.st,
X_survey=X_survey,
effort=summer$effort.sc,
y_count=summer$monarch)
#Initial values
set.seed(123)
inits <- lapply(1:nc, function(i)
list(alpha0=runif(1,1,4),
alphaFE=runif(ncol(X_county),-1,1),
alpha_week=runif(n_years,-1,1),
alpha_week2=runif(n_years,-1,1),
betaFE=runif(ncol(X_survey),-1,1),
phi=runif(1,0,5)))
out.FspCsuCsuL <- stan('FullModel_HierPart_Summer.stan',
control=list(adapt_delta=0.8),data=standata, init=inits, pars=params,
chains=nc, iter=ni, warmup=nb, thin=nt,seed=1,cores=3,open_progress=FALSE)
#------------------------------------------------------------#
# Hierarchical partitioning
#------------------------------------------------------------#
outlist.s <- list(out.null=out.null,
out.F=out.F,
out.spC=out.spC,
out.suC=out.suC,
out.suL=out.suL,
out.FspC=out.FspC,
out.FsuC=out.FsuC,
out.FsuL=out.FsuL,
out.spCsuC=out.spCsuC,
out.spCsuL=out.spCsuL,
out.suCsuL=out.suCsuL,
out.FspCsuC=out.FspCsuC,
out.FspCsuL=out.FspCsuL,
out.FsuCsuL=out.FsuCsuL,
out.spCsuCsuL=out.spCsuCsuL,
out.FspCsuCsuL=out.FspCsuCsuL)
df.s <- data.frame(model=names(outlist.s),stringsAsFactors=FALSE)
df.s$F <- ifelse(grepl('F',df.s$model),1,0)
df.s$spC <- ifelse(grepl('spC',df.s$model),1,0)
df.s$suC <- ifelse(grepl('suC',df.s$model),1,0)
df.s$suL <- ifelse(grepl('suL',df.s$model),1,0)
df.s$logL <- sapply(outlist.s,function(x) round(summary(x)$summary['sum_log_lik','50%']))
df.s$maxR <- sapply(outlist.s,function(x) round(max(summary(x)$summary[,'Rhat']),3))
df.s[order(-df.s$logL),]
#Level of hierarchy (0-4 = 0-4 covariate groups, respectively)
df.s$hier <- apply(df.s[,c('F','spC','suC','suL')],1,sum)
#Binary string that indicates which covariates in each model
df.s$modelc <- paste0(df.s$F,df.s$spC,df.s$suC,df.s$suL)
#Create dataframe with all nested model pairs
allpairs.s <- expand.grid(model1=df.s$modelc,model0=df.s$modelc)
allpairs.s <- allpairs.s[allpairs.s$model1!=allpairs.s$model0,]
allpairs.s$hier1 <- df.s$hier[match(allpairs.s$model1,df.s$modelc)]
allpairs.s$hier0 <- df.s$hier[match(allpairs.s$model0,df.s$modelc)]
allpairs.s <- allpairs.s[allpairs.s$hier1-allpairs.s$hier0==1,]
allpairs.s$F.1 <- as.numeric(substr(allpairs.s$model1,1,1))
allpairs.s$spC.1 <- as.numeric(substr(allpairs.s$model1,2,2))
allpairs.s$suC.1 <- as.numeric(substr(allpairs.s$model1,3,3))
allpairs.s$suL.1 <- as.numeric(substr(allpairs.s$model1,4,4))
allpairs.s$F.0 <- as.numeric(substr(allpairs.s$model0,1,1))
allpairs.s$spC.0 <- as.numeric(substr(allpairs.s$model0,2,2))
allpairs.s$suC.0 <- as.numeric(substr(allpairs.s$model0,3,3))
allpairs.s$suL.0 <- as.numeric(substr(allpairs.s$model0,4,4))
allpairs.s$F <- allpairs.s$F.1 + allpairs.s$F.0
allpairs.s$spC <- allpairs.s$spC.1 + allpairs.s$spC.0
allpairs.s$suC <- allpairs.s$suC.1 + allpairs.s$suC.0
allpairs.s$suL <- allpairs.s$suL.1 + allpairs.s$suL.0
allpairs.s$nBoth <- apply(allpairs.s[,c('F','spC','suC','suL')],1,function(x) sum(x==2))
allpairs.s <- allpairs.s[allpairs.s$hier0==allpairs.s$nBoth,]
#Calculate difference in logL for each pair
allpairs.s$logL1 <- df.s$logL[match(allpairs.s$model1,df.s$modelc)]
allpairs.s$logL0 <- df.s$logL[match(allpairs.s$model0,df.s$modelc)]
allpairs.s$diff <- allpairs.s$logL1 - allpairs.s$logL0
#Identify covariate group that's different in each pair
allpairs.s$param.F <- ifelse(allpairs.s$F.1==1 & allpairs.s$F.0==0,1,0)
allpairs.s$param.spC <- ifelse(allpairs.s$spC.1==1 & allpairs.s$spC.0==0,1,0)
allpairs.s$param.suC <- ifelse(allpairs.s$suC.1==1 & allpairs.s$suC.0==0,1,0)
allpairs.s$param.suL <- ifelse(allpairs.s$suL.1==1 & allpairs.s$suL.0==0,1,0)
#Average logL differences for each covariate group and level of hierarchy
hpl.s <- data.frame(expand.grid(param=c('F','spC','suC','suL'),hier=unique(allpairs.s$hier1),stringsAsFactors=FALSE))
for(i in 1:nrow(hpl.s)){
hpl.s$avg.level[i] <- mean(allpairs.s$diff[allpairs.s$hier1==hpl.s$hier[i] & allpairs.s[,paste0('param.',hpl.s$param[i])]==1])
}
#Mean of those averages for each covariate group
hp.s <- data.frame(param=unique(hpl.s$param))
for(i in 1:nrow(hp.s)){
hp.s$IC[i] <- mean(hpl.s$avg.level[hpl.s$param==hp.s$param[i]])
}
#Relativize values (divide by total)
hp.s$IC.perc <- round(hp.s$IC/sum(hp.s$IC)*100,1)
hp.s