Bagues_run_gjam.R
In dariamed/gjamed: What the package does (short line)
#rm(list=ls())
library(repmis)
library(gjam)
library(MASS)
library(truncnorm)
library(coda)
library(RcppArmadillo)
library(arm)
library(Rcpp)
library(raster)
library(ggplot2)
library(rgdal)
library(biomod2)
library(AUC)
Rcpp::sourceCpp('src/cppFns.cpp')
source("R/gjamHfunctions_mod.R")
source("R/simple_gjam_1.R")
source("R/simple_gjam_2.R")
source("R/simple_gjam_3.R")
source("R/simple_gjam_4.R")
load_object <- function(file) {
tmp <- new.env()
load(file = file, envir = tmp)
tmp[[ls(tmp)[1]]]
}
#setwd("~/Downloads/RFate-master/data_supplements/Bauges")
B_coords_xy<- load_object("DB.XY.RData")
#B__observations_XY<- load_object("DB.observations.xy.RData")
#load abundance data
PA_data<-load_object("DOM.mat.sites.species.PA.RData")
AB_data<-load_object("DOM.mat.sites.species.abund.RData")
### Colnames PA vs AB
S_PA<- colnames(PA_data)
S_AB<- colnames(AB_data)
setdiff(S_PA,S_AB) #14797"
PA_data_df<- as.data.frame(PA_data)
PA_data_df$cite<- rownames(PA_data)
AB_data_df<- as.data.frame(AB_data)
AB_data_df$cite<- rownames(AB_data)
PA_AB_common_plots<- intersect(AB_data_df$cite, PA_data_df$cite)
#L1<- apply(!is.na(PA_data_df[,2:126]),1, which)
#L2<- apply(!is.na(AB_data_df[,2:126]),1, which)
#spdf <- SpatialPoints(B_coords_xy,proj4string = CRS("+proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80
# +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"))
#raster stack for the 100.tif
zone.name="ENV_VARIABLES"
zone.env.folder="EOBS_1970_2005"
zone.env.variables=c("bio_1_0","bio_12_0","bio_19_0","bio_8_0","slope")
env.files = list.files(path = paste0(zone.name, "/", zone.env.folder)
, pattern = paste0(paste0(zone.env.variables, ".img", collapse = "|"), "|", paste0(zone.env.variables, ".tif", collapse = "|")), full.names = TRUE)
##function from the package
getSDM_env = function(zone.name, zone.env.folder, zone.env.variables, maskSimul)
{
env.files = list.files(path = paste0(zone.name, "/", zone.env.folder)
, pattern = paste0(paste0(zone.env.variables, ".img", collapse = "|")
, "|"
, paste0(zone.env.variables, ".tif", collapse = "|"))
, full.names = TRUE)
zone.env.stk.CALIB = raster::stack(env.files)
#maskSimul=raster("MASK_100m.tif")
origin(maskSimul) = origin(zone.env.stk.CALIB)
zone.env.stk.PROJ = stack(zone.env.stk.CALIB * maskSimul)
names(zone.env.stk.PROJ) = names(zone.env.stk.CALIB)
return(list(env.CALIB = zone.env.stk.CALIB
, env.PROJ = zone.env.stk.PROJ))
}
X<- mask(zone.env.stk.CALIB, maskSimul)
new_B_env<-getSDM_env(zone.name, zone.env.folder, zone.env.variables, maskSimul=raster("MASK_100m.tif"))
#B_ENV_proj<- load_object("Bauges.zone.env.stk.PROJ.RData")
B_env_raster<- new_B_env$env.PROJ
B_env<-as.data.frame(extract(B_env_raster, B_coords_xy))
B_env$cite<- rownames(B_coords_xy)
### merge environmental covariates and presence/abscence data by cite.
PA_env_df <- merge(B_env,PA_data_df,by="cite")
## delete cites with NA for environment
NAs_values<- is.na(PA_env_df$bio_1_0)&is.na(PA_env_df$bio_12_0)&is.na(PA_env_df$bio_19_0)&is.na(PA_env_df$bio_8_0)&is.na(PA_env_df$slope)
PA_env_df_1<- PA_env_df[!NAs_values,]
### delete total 0 for environment
zeros_values<- (PA_env_df_1$bio_1_0==0)&(PA_env_df_1$bio_12_0==0)&(PA_env_df_1$bio_19_0==0)&(PA_env_df_1$bio_8_0==0)&(PA_env_df_1$slope==0)
PA_env_df_2<- PA_env_df_1[!zeros_values,]
## non missing data sets
#PA_non_miss_env<- PA_env_df_2[!apply(is.na(PA_env_df_2[,7:131]),1,any),]
#PA_non_na_env<- PA_env_df_2[!apply(is.na(PA_env_df_2[,7:131]),1,all),]
#### Keeping PA data with at least one 1/0
PA_env_df_3<- PA_env_df_2[which(!(rowSums(is.na(PA_env_df_2[,7:131]))==125)),]
###Convert from NA to 0
PA_env_df_2_fil_o_y<- PA_env_df_3[,7:131]
PA_env_df_2_fil_o_x<- PA_env_df_3[,1:6]
PA_env_df_2_fil_o_y[is.na(PA_env_df_2_fil_o_y)] <- 0
PA_env_df_2_0<- cbind(PA_env_df_2_fil_o_x,PA_env_df_2_fil_o_y)
#PA_env_df_2_0<-apply(PA_env_df_2[,7:131],1,function(x) x[is.na(x)] <- 0 )
#PA_env_df_2_00<- data.frame(PA_env_df_2[,1:6], apply(PA_env_df_2[,7:131],1, ) )
####Separation test/train
#data<- PA_env_df_3
data<- PA_env_df_2_0
smp_size <- floor(0.70 * nrow(data))
## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(data)), size = smp_size)
train <- data[train_ind, ]
test <- data[-train_ind, ]
## dim(train) / 3712 131
## dim(test) / 1591 131
########################################################################Group numbers
Species_names_groups<- read.csv("PFG_Bauges_Description_2017.csv", sep="\t")
# K=16 functional groups
#############################################################################Fitting the model
it<-1000
burn<-500
holdout<- sample(seq_len(nrow(train)), size = 100)
###################PCA
xdata_init<- train[,2:6]
#
##Normalization
scaled.data <- as.data.frame(scale(xdata_init))
###PCA
xnew<- prcomp(scaled.data)
xdata_pca<- as.data.frame(xnew$x[,1:3])
##########GJAM standart model
y<- train[1:1000,7:131]
xdata<- train[1:1000,2:6]
#xdata<- scaled.data
#xdata<- xdata_pca
#formula <- as.formula( ~ PC1 + PC2 + PC3 )
formula <- as.formula( ~ bio_1_0 + bio_8_0 + I(bio_8_0^2) + I(bio_1_0^2))
Ydata <- gjamTrimY(y,10)$y # at least 10 plots - re-group rare species
S<- ncol(Ydata)
rl <- list(r =5, N = 50)
ml <- list(ng = 1000, burnin = 100, typeNames = 'PA', reductList = rl) #change ml
fit<-gjam(formula, xdata = xdata, ydata = Ydata, modelList = ml)
###Check the rank of X, should be number of columns
x <- model.matrix(formula, xdata)
qr(x)$rank
#save(fit,file="models_Bagues_data_OSS/fit.Rda")
#no Holdout
#save(fit,file="models_forest_data_OSS/fit.Rda")
####### Out of sample prediction -DOESN't work : chol() error ??
Ykeep<- as.vector(colnames(Ydata))
y_test<- test[1:200,c(Ykeep[1:(ncol(Ydata)-1)])]
#xdata_test<- test[,2:6]
xdata_test<- test[1:200,2:6]
new <- list(xdata =xdata_test, nsim = 1000) # effort unchanged
p1 <- gjamPredict(output = fit, newdata = new)
plot(y_test[,2], p1$sdList$yMu[,2])
abline(0,1)
AUC_j_env<-vector()
for(i in 1:ncol(y_test)) AUC_j_env<-c(AUC_j_env,auc(roc(p1$sdList$yMu[,i],factor(y_test[,i]))))
Tjur_j_env<-vector()
for(k in 1:ncol(y_test)){
indx <- y_test[,k]==1
Tjur_j_env <- c(Tjur_j_env,(mean(p1$sdList$yMu[indx,k]) - mean(p1$sdList$yMu[!indx,k])))
}
############################################
####Check the trace for number of groups. From the previous analysis we know that the number of functional groups is 16
trace<-apply(fit$chains$kgibbs,1,function(x) length(unique(x)))
df<-as.data.frame(trace)
df$iter<-1:1000
#plot(apply(fit$chains$kgibbs,1,function(x) length(unique(x))))
p<-ggplot(df, aes(y=trace, x=iter)) + geom_point() +
labs(title=paste0("Trace plot for the number of groups"))+
theme_bw() + theme(axis.text.x = element_text(angle = 0, hjust = 1,size = 10), strip.text = element_text(size = 15),legend.position = "top", plot.title = element_text(hjust = 0.5))+
geom_hline(yintercept = 16,color = "red")
p
####The Dirichlet process prior with conjugation############################################
##We don't need this model
rl1 <- list(r = 5, N = 50,rate=10,shape=10)
fit1<-.gjam_1(form, xdata = xdata, ydata = Ydata, modelList = ml1, holdoutIndex =holdout)
####The Dirichlet process prior : multinomial + prior on alpha
##Computing the hyper-parameters for K=16
K=16
S=125
func<-function(x) {sum(x/(x+(1:S)-1))-16}
alpha.DP<-.bisec(func,0.01,100)
shape=((alpha.DP)^2)/20
rate=alpha.DP/20
rl2 <- list(r = 5, N = 50,rate=rate,shape=shape,V=1) #here to modify N
ml2 <- list(ng = 1000, burnin = 500, typeNames = 'PA', reductList = rl2) #change ml
fit2<-.gjam_2(formula, xdata = xdata, ydata = Ydata, modelList = ml2)
save(fit2,file="models_Bagues_data_OSS/fit2.Rda")
trace<-apply(fit2$chains$kgibbs,1,function(x) length(unique(x)))
df<-as.data.frame(trace)
df$iter<-1:1000
#plot(apply(fit$chains$kgibbs,1,function(x) length(unique(x))))
p<-ggplot(df, aes(y=trace, x=iter)) + geom_point() +
labs(title=paste0("Trace plot for the number of groups"))+
theme_bw() + theme(axis.text.x = element_text(angle = 0, hjust = 1,size = 10), strip.text = element_text(size = 15),legend.position = "top", plot.title = element_text(hjust = 0.5))+
geom_hline(yintercept = 16,color = "red")
p
#################################################################################PY
K=16
eps<-0.1
sigma_py<-0.25
funcPY_root<-function(x) {(x/sigma_py)*(prod((x+sigma_py+c(1:S) -1)/(x+c(1:S) -1))-1) - K}
alpha.PY<-.bisec(funcPY_root,0.0001,100)
N_eps<-floor(.compute_tau_mean_large_dim(sigma_py,alpha.PY,eps) + 2*.compute_tau_var_large_dim(sigma_py,alpha.PY,eps))
rl3 <- list(r = 5, N = N_eps, sigma_py=sigma_py, alpha=alpha.PY)
ml3 <- list(ng = it, burnin = burn, typeNames = 'PA', reductList = rl3)
fit3 <- .gjam_3(formula,xdata,Ydata,ml3)
save(fit3,file="models_Bagues_data_OSS/fit3.Rda")
K<-16
eps=0.1
alp_sig<-as.data.frame(matrix(NA,nrow=20,ncol=3))
colnames(alp_sig)<-c("alpha","sigma","is_less_150")
alp_sig$sigma=seq(0.05,0.4,length.out = 20)
#loop to run bisecetion on a grid for sigma
for(i in 1:20){
####corrected added -1
func<-function(x) {(x/alp_sig[i,"sigma"])*(prod((x+alp_sig[i,"sigma"]+c(1:S)-1)/(x+c(1:S) -1))-1) - K}
alp_sig[i,"alpha"]<-.bisec(func,0.01,100)
N_eps<-floor(.compute_tau_mean_large_dim(alp_sig[i,"sigma"], alp_sig[i,"alpha"],eps) + 2*.compute_tau_var_large_dim(alp_sig[i,"sigma"], alp_sig[i,"alpha"],eps))
ifelse(N_eps<=150,alp_sig[i,"is_less_150"]<-T,alp_sig[i,"is_less_150"]<-F)
N_eps
}
if(sum(alp_sig$is_less_150==T)==0) cat("!! no choice under N=150, need to recheck!!!")
k<-max(which(alp_sig$is_less_150==T)) #max sigma s.t. N<150
sigma_py<-alp_sig[k,"sigma"]
alpha.PY<-alp_sig[k,"alpha"]
#fixing hyperparameters
ro.disc=1-2* sigma_py
shape=((alpha.PY)^2)/10
rate=alpha.PY/10
# 95% quantile of alpha
alpha.max=qgamma(.95, shape=shape, rate=rate)
alpha.max_val<-5
sigma_py_max<-0.5
N_eps<-floor(.compute_tau_mean_large_dim(sigma_py_max,alpha.max_val,eps) + 2*.compute_tau_var_large_dim(sigma_py_max,alpha.max_val,eps))
rl4 <- list(r = 5, N =N_eps,rate=rate,shape=shape,V1=1,ro.disc=ro.disc) #here to modify N
ml4 <- list(ng = it, burnin = burn, typeNames = 'PA', reductList = rl4)
fit4<-.gjam_4(formula, xdata = xdata, ydata = Ydata, modelList = ml4)
save(fit4,file="models_Bagues_data_OSS/fit4.Rda")
########################################## Model comparison
fit$fit$rmspeAll #2.257202
fit1$fit$rmspeAll #2.205223
fit2$fit$rmspeAll #2.209276
fit3$fit$rmspeAll #2.203517
fit4$fit$rmspeAll #2.092136
fit$fit$DIC #2510192
fit1$fit$DIC #2496028
fit2$fit$DIC #2505273
fit3$fit$DIC #2496243
fit4$fit$DIC #2460125
#################################################################################Other models
rl2 <- list(r = 8, N = 20,rate=10,shape=10,V=1) #here to modify N
N_eps<-floor(.compute_tau_mean(0.3,2,0.1) + 2*.compute_tau_var(0.3,2,0.1))
rl3 <- list(r = 8, N = N_eps, sigma_py=0.3, alpha=2)
N_eps<-floor(.compute_tau_mean(0.5,10,0.1) + 2*.compute_tau_var(0.5,10,0.1))
rl4 <- list(r = 8, N = N_eps,rate=10,shape=10,V1=1,ro.disc=0.5) #here to modify N
ml4 <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl4) #change ml
ml3 <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl3) #change ml
ml2 <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl2) #change ml
ml1 <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl1) #change ml
ml <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl) #change ml
form <- as.formula( ~ temp*deficit + I(temp^2) + I(deficit^2) )
fit<-gjam(form, xdata = xdata, ydata = treeYdata, modelList = ml)
fit1<-.gjam_1(form, xdata = xdata, ydata = treeYdata, modelList = ml1)
fit2<-.gjam_2(form, xdata = xdata, ydata = treeYdata, modelList = ml2)
fit3 <- .gjam_3(form,xdata,treeYdata,ml3)
fit4<-.gjam_4(form, xdata = xdata, ydata = treeYdata, modelList = ml4)
fit$fit$rmspeAll #2.257202
fit1$fit$rmspeAll #2.205223
fit2$fit$rmspeAll #2.209276
fit3$fit$rmspeAll #2.203517
fit4$fit$rmspeAll #2.092136
fit$fit$DIC #2510192
fit1$fit$DIC #2496028
fit2$fit$DIC #2505273
fit3$fit$DIC #2496243
fit4$fit$DIC #2460125
#check that alpha and sigma posteriors
#gjam1
alpha<-mcmc(fit1$chains$alpha.DP_g)
plot(alpha)
acfplot(alpha)
cumuplot(alpha)
#gjam2
alpha<-mcmc(fit2$chains$alpha.DP_g)
plot(alpha)
acfplot(alpha)
cumuplot(alpha)
##gjam4
alpha<-mcmc(fit4$chains$alpha.PY_g)
alpha<-mcmc(fit4$chains$alpha.PY_g[seq(1,length(fit4$chains$alpha.PY_g),by=20)])
plot(alpha)
acfplot(alpha)
cumuplot(alpha)
discount<-mcmc(fit4$chains$discount.PY_g)
plot(discount)
acfplot(discount)
cumuplot(discount)
#check the convergence
#for sigma
gjam_mc<- mcmc(fit$chains$sgibbs)
gjam_mc1<- mcmc(fit1$chains$sgibbs)
gjam_mc2<- mcmc(fit2$chains$sgibbs)
gjam_mc3<- mcmc(fit3$chains$sgibbs)
gjam_mc4<- mcmc(fit4$chains$sgibbs)
par(mfrow=c(2,3))
hist(effectiveSize(gjam_mc), main="ess(sigma) gjam",lwd=2,col=gray(.6),breaks=100)
hist(effectiveSize(gjam_mc1), main="ess(sigma) gjam1",lwd=2,col=gray(.6),breaks=100)
hist(effectiveSize(gjam_mc2), main="ess(sigma) gjam2",lwd=2,col=gray(.6),breaks=100)
hist(effectiveSize(gjam_mc3), main="ess(sigma) gjam3",lwd=2,col=gray(.6),breaks=100)
hist(effectiveSize(gjam_mc4), main="ess(sigma) gjam4",lwd=2,col=gray(.6),breaks=100)
# for betas
beta_mcmc<-mcmc(fit$chains$bgibbs)
beta_mcmc1<-mcmc(fit1$chains$bgibbs)
beta_mcmc2<-mcmc(fit2$chains$bgibbs)
beta_mcmc3<-mcmc(fit3$chains$bgibbs)
beta_mcmc4<-mcmc(fit4$chains$bgibbs)
#nESS
par(mfrow=c(2,3))
hist(effectiveSize(beta_mcmc), main="ess(beta) gjam",lwd=2,col=gray(.6))
hist(effectiveSize(beta_mcmc1), main="ess(beta) gjam1",lwd=2,col=gray(.6))
hist(effectiveSize(beta_mcmc2), main="ess(beta) gjam2",lwd=2,col=gray(.6))
hist(effectiveSize(beta_mcmc3), main="ess(beta) gjam3",lwd=2,col=gray(.6))
hist(effectiveSize(beta_mcmc4), main="ess(beta) gjam4",lwd=2,col=gray(.6))
#check the traceplots of K
trace0<-apply(fit$chains$kgibbs,1,function(x) length(unique(x)))
#trace1<-apply(fit1$chains$kgibbs,1,function(x) length(unique(x)))
trace2<-apply(fit2$chains$kgibbs,1,function(x) length(unique(x)))
trace3<-apply(fit3$chains$kgibbs,1,function(x) length(unique(x)))
trace4<-apply(fit4$chains$kgibbs,1,function(x) length(unique(x)))
table<-data.frame()
table<-data.frame("trace"=c(trace0,
#trace1,
trace2,trace3,trace4),
"type"=c(rep("0",length(trace0)),
#rep("1",length(trace1)),
rep("2",length(trace2)),rep("3",length(trace3)),rep("4",length(trace4))),
"x"=rep(1:it,4))
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
cols = gg_color_hue(4)
#single traceplots - not useful
# p1<-ggplot(table[which(table$type=="0"),], aes(x=table$x[which(table$type=="0")],y=table$trace[which(table$type=="0")]))+geom_point()
# p1
# p2<-ggplot(table[which(table$type=="1"),], aes(x=table$x[which(table$type=="1")],y=table$trace[which(table$type=="1")]))+geom_point()
# p2
# p3<-ggplot(table[which(table$type=="2"),], aes(x=table$x[which(table$type=="2")],y=table$trace[which(table$type=="2")]))+geom_point()
# p3
# p4<-ggplot(table[which(table$type=="3"),], aes(x=table$x[which(table$type=="3")],y=table$trace[which(table$type=="3")]))+geom_point()
# p4
# traceplots altogether
p<-ggplot(table, aes(x=x,y=trace,col=as.factor(type)))+geom_point()+
scale_color_manual(name = c(""), values = cols, labels=c("Original model",
#"DP with prior on alpha 1",
"DP with prior on alpha 2","PY with fixed alpha, sigma","PY with prior on alpha, sigma"))+
labs(title="Traceplots of the posterior of the number of clusters")+xlab("iterations")+theme_bw()
#pdf("plot_forest_data/forest_data_trace_K.pdf")
p
#dev.off()
#check the last weight
pk_chains0_last<- mcmc(fit$chains$pk_g[,ncol(fit$chains$pk_g)])
plot(pk_chains1_last)
pk_chains1_last<- mcmc(fit1$chains$pk_g[,ncol(fit1$chains$pk_g)])
plot(pk_chains1_last)
pk_chains2_last<- mcmc(fit2$chains$pk_g[,ncol(fit2$chains$pk_g)])
plot(pk_chains2_last)
pk_chains3_last<- mcmc(fit3$chains$pk_g[,ncol(fit3$chains$pk_g)])
plot(pk_chains3_last)
pk_chains4_last<- mcmc(fit4$chains$pk_g[,ncol(fit4$chains$pk_g)])
plot(pk_chains4_last)
#Out of sample prediction
#TJUR coefficient
sum((fit$prediction$ypredMu[1:100,]-Ydata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5987808
sum((fit1$prediction$ypredMu[1:100,]-treeYdata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5961853
sum((fit2$prediction$ypredMu[1:100,]-treeYdata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5979932
sum((fit3$prediction$ypredMu[1:100,]-treeYdata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5977189
sum((fit4$prediction$ypredMu[1:100,]-treeYdata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5983279
#################################################################################Other models - BIOMOD
## Not run:
# species occurrences
DataSpecies <- read.csv(system.file("external/species/mammals_table.csv",package="biomod2"), row.names = 1)
# the presence/absences data for our species
myRespName <- 'VulpesVulpes'
myResp <- as.numeric(DataSpecies[,myRespName])
# the XY coordinates of species data
myRespXY <- DataSpecies[,c("X_WGS84","Y_WGS84")]
# Environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
myExpl = stack( system.file( "external/bioclim/current/bio3.grd",package="biomod2"),
system.file( "external/bioclim/current/bio4.grd",package="biomod2"),
system.file( "external/bioclim/current/bio7.grd",package="biomod2"),
system.file( "external/bioclim/current/bio11.grd", package="biomod2"),
system.file( "external/bioclim/current/bio12.grd",package="biomod2"))
# 1. Formatting Data
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName)
# 2. Defining Models Options using default options.
myBiomodOption <- BIOMOD_ModelingOptions()
# 3. Doing Modelisation
myBiomodModelOut <- BIOMOD_Modeling( myBiomodData,
models = c('SRE'),
models.options = myBiomodOption,
NbRunEval=1,
DataSplit=80,
Prevalence=0.5,
VarImport=0,
models.eval.meth = c('TSS','ROC'),
do.full.models=FALSE,
modeling.id="test2")
# files have been created on hard drive
list.files(myRespName,all.files=TRUE,recursive=TRUE)
# remove properly the modeling objects and all the file saved on hard drive
RemoveProperly(myBiomodModelOut)
# check files had been removed
list.files(myRespName,all.files=TRUE,recursive=TRUE)
## End(Not run)
r <- raster(nrow=18, ncol=36, xmn=0)
r[150:250] <- 1
r[251:450] <- 2
plot( boundaries(r, type='inner') )
plot( boundaries(r, type='outer') )
plot( boundaries(r, classes=TRUE) )
dariamed/gjamed documentation built on Sept. 27, 2019, 5:31 p.m.
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#rm(list=ls())
library(repmis)
library(gjam)
library(MASS)
library(truncnorm)
library(coda)
library(RcppArmadillo)
library(arm)
library(Rcpp)
library(raster)
library(ggplot2)
library(rgdal)
library(biomod2)
library(AUC)
Rcpp::sourceCpp('src/cppFns.cpp')
source("R/gjamHfunctions_mod.R")
source("R/simple_gjam_1.R")
source("R/simple_gjam_2.R")
source("R/simple_gjam_3.R")
source("R/simple_gjam_4.R")
load_object <- function(file) {
tmp <- new.env()
load(file = file, envir = tmp)
tmp[[ls(tmp)[1]]]
}
#setwd("~/Downloads/RFate-master/data_supplements/Bauges")
B_coords_xy<- load_object("DB.XY.RData")
#B__observations_XY<- load_object("DB.observations.xy.RData")
#load abundance data
PA_data<-load_object("DOM.mat.sites.species.PA.RData")
AB_data<-load_object("DOM.mat.sites.species.abund.RData")
### Colnames PA vs AB
S_PA<- colnames(PA_data)
S_AB<- colnames(AB_data)
setdiff(S_PA,S_AB) #14797"
PA_data_df<- as.data.frame(PA_data)
PA_data_df$cite<- rownames(PA_data)
AB_data_df<- as.data.frame(AB_data)
AB_data_df$cite<- rownames(AB_data)
PA_AB_common_plots<- intersect(AB_data_df$cite, PA_data_df$cite)
#L1<- apply(!is.na(PA_data_df[,2:126]),1, which)
#L2<- apply(!is.na(AB_data_df[,2:126]),1, which)
#spdf <- SpatialPoints(B_coords_xy,proj4string = CRS("+proj=lcc +lat_1=49 +lat_2=44 +lat_0=46.5 +lon_0=3 +x_0=700000 +y_0=6600000 +ellps=GRS80
# +towgs84=0,0,0,0,0,0,0 +units=m +no_defs"))
#raster stack for the 100.tif
zone.name="ENV_VARIABLES"
zone.env.folder="EOBS_1970_2005"
zone.env.variables=c("bio_1_0","bio_12_0","bio_19_0","bio_8_0","slope")
env.files = list.files(path = paste0(zone.name, "/", zone.env.folder)
, pattern = paste0(paste0(zone.env.variables, ".img", collapse = "|"), "|", paste0(zone.env.variables, ".tif", collapse = "|")), full.names = TRUE)
##function from the package
getSDM_env = function(zone.name, zone.env.folder, zone.env.variables, maskSimul)
{
env.files = list.files(path = paste0(zone.name, "/", zone.env.folder)
, pattern = paste0(paste0(zone.env.variables, ".img", collapse = "|")
, "|"
, paste0(zone.env.variables, ".tif", collapse = "|"))
, full.names = TRUE)
zone.env.stk.CALIB = raster::stack(env.files)
#maskSimul=raster("MASK_100m.tif")
origin(maskSimul) = origin(zone.env.stk.CALIB)
zone.env.stk.PROJ = stack(zone.env.stk.CALIB * maskSimul)
names(zone.env.stk.PROJ) = names(zone.env.stk.CALIB)
return(list(env.CALIB = zone.env.stk.CALIB
, env.PROJ = zone.env.stk.PROJ))
}
X<- mask(zone.env.stk.CALIB, maskSimul)
new_B_env<-getSDM_env(zone.name, zone.env.folder, zone.env.variables, maskSimul=raster("MASK_100m.tif"))
#B_ENV_proj<- load_object("Bauges.zone.env.stk.PROJ.RData")
B_env_raster<- new_B_env$env.PROJ
B_env<-as.data.frame(extract(B_env_raster, B_coords_xy))
B_env$cite<- rownames(B_coords_xy)
### merge environmental covariates and presence/abscence data by cite.
PA_env_df <- merge(B_env,PA_data_df,by="cite")
## delete cites with NA for environment
NAs_values<- is.na(PA_env_df$bio_1_0)&is.na(PA_env_df$bio_12_0)&is.na(PA_env_df$bio_19_0)&is.na(PA_env_df$bio_8_0)&is.na(PA_env_df$slope)
PA_env_df_1<- PA_env_df[!NAs_values,]
### delete total 0 for environment
zeros_values<- (PA_env_df_1$bio_1_0==0)&(PA_env_df_1$bio_12_0==0)&(PA_env_df_1$bio_19_0==0)&(PA_env_df_1$bio_8_0==0)&(PA_env_df_1$slope==0)
PA_env_df_2<- PA_env_df_1[!zeros_values,]
## non missing data sets
#PA_non_miss_env<- PA_env_df_2[!apply(is.na(PA_env_df_2[,7:131]),1,any),]
#PA_non_na_env<- PA_env_df_2[!apply(is.na(PA_env_df_2[,7:131]),1,all),]
#### Keeping PA data with at least one 1/0
PA_env_df_3<- PA_env_df_2[which(!(rowSums(is.na(PA_env_df_2[,7:131]))==125)),]
###Convert from NA to 0
PA_env_df_2_fil_o_y<- PA_env_df_3[,7:131]
PA_env_df_2_fil_o_x<- PA_env_df_3[,1:6]
PA_env_df_2_fil_o_y[is.na(PA_env_df_2_fil_o_y)] <- 0
PA_env_df_2_0<- cbind(PA_env_df_2_fil_o_x,PA_env_df_2_fil_o_y)
#PA_env_df_2_0<-apply(PA_env_df_2[,7:131],1,function(x) x[is.na(x)] <- 0 )
#PA_env_df_2_00<- data.frame(PA_env_df_2[,1:6], apply(PA_env_df_2[,7:131],1, ) )
####Separation test/train
#data<- PA_env_df_3
data<- PA_env_df_2_0
smp_size <- floor(0.70 * nrow(data))
## set the seed to make your partition reproducible
set.seed(123)
train_ind <- sample(seq_len(nrow(data)), size = smp_size)
train <- data[train_ind, ]
test <- data[-train_ind, ]
## dim(train) / 3712 131
## dim(test) / 1591 131
########################################################################Group numbers
Species_names_groups<- read.csv("PFG_Bauges_Description_2017.csv", sep="\t")
# K=16 functional groups
#############################################################################Fitting the model
it<-1000
burn<-500
holdout<- sample(seq_len(nrow(train)), size = 100)
###################PCA
xdata_init<- train[,2:6]
#
##Normalization
scaled.data <- as.data.frame(scale(xdata_init))
###PCA
xnew<- prcomp(scaled.data)
xdata_pca<- as.data.frame(xnew$x[,1:3])
##########GJAM standart model
y<- train[1:1000,7:131]
xdata<- train[1:1000,2:6]
#xdata<- scaled.data
#xdata<- xdata_pca
#formula <- as.formula( ~ PC1 + PC2 + PC3 )
formula <- as.formula( ~ bio_1_0 + bio_8_0 + I(bio_8_0^2) + I(bio_1_0^2))
Ydata <- gjamTrimY(y,10)$y # at least 10 plots - re-group rare species
S<- ncol(Ydata)
rl <- list(r =5, N = 50)
ml <- list(ng = 1000, burnin = 100, typeNames = 'PA', reductList = rl) #change ml
fit<-gjam(formula, xdata = xdata, ydata = Ydata, modelList = ml)
###Check the rank of X, should be number of columns
x <- model.matrix(formula, xdata)
qr(x)$rank
#save(fit,file="models_Bagues_data_OSS/fit.Rda")
#no Holdout
#save(fit,file="models_forest_data_OSS/fit.Rda")
####### Out of sample prediction -DOESN't work : chol() error ??
Ykeep<- as.vector(colnames(Ydata))
y_test<- test[1:200,c(Ykeep[1:(ncol(Ydata)-1)])]
#xdata_test<- test[,2:6]
xdata_test<- test[1:200,2:6]
new <- list(xdata =xdata_test, nsim = 1000) # effort unchanged
p1 <- gjamPredict(output = fit, newdata = new)
plot(y_test[,2], p1$sdList$yMu[,2])
abline(0,1)
AUC_j_env<-vector()
for(i in 1:ncol(y_test)) AUC_j_env<-c(AUC_j_env,auc(roc(p1$sdList$yMu[,i],factor(y_test[,i]))))
Tjur_j_env<-vector()
for(k in 1:ncol(y_test)){
indx <- y_test[,k]==1
Tjur_j_env <- c(Tjur_j_env,(mean(p1$sdList$yMu[indx,k]) - mean(p1$sdList$yMu[!indx,k])))
}
############################################
####Check the trace for number of groups. From the previous analysis we know that the number of functional groups is 16
trace<-apply(fit$chains$kgibbs,1,function(x) length(unique(x)))
df<-as.data.frame(trace)
df$iter<-1:1000
#plot(apply(fit$chains$kgibbs,1,function(x) length(unique(x))))
p<-ggplot(df, aes(y=trace, x=iter)) + geom_point() +
labs(title=paste0("Trace plot for the number of groups"))+
theme_bw() + theme(axis.text.x = element_text(angle = 0, hjust = 1,size = 10), strip.text = element_text(size = 15),legend.position = "top", plot.title = element_text(hjust = 0.5))+
geom_hline(yintercept = 16,color = "red")
p
####The Dirichlet process prior with conjugation############################################
##We don't need this model
rl1 <- list(r = 5, N = 50,rate=10,shape=10)
fit1<-.gjam_1(form, xdata = xdata, ydata = Ydata, modelList = ml1, holdoutIndex =holdout)
####The Dirichlet process prior : multinomial + prior on alpha
##Computing the hyper-parameters for K=16
K=16
S=125
func<-function(x) {sum(x/(x+(1:S)-1))-16}
alpha.DP<-.bisec(func,0.01,100)
shape=((alpha.DP)^2)/20
rate=alpha.DP/20
rl2 <- list(r = 5, N = 50,rate=rate,shape=shape,V=1) #here to modify N
ml2 <- list(ng = 1000, burnin = 500, typeNames = 'PA', reductList = rl2) #change ml
fit2<-.gjam_2(formula, xdata = xdata, ydata = Ydata, modelList = ml2)
save(fit2,file="models_Bagues_data_OSS/fit2.Rda")
trace<-apply(fit2$chains$kgibbs,1,function(x) length(unique(x)))
df<-as.data.frame(trace)
df$iter<-1:1000
#plot(apply(fit$chains$kgibbs,1,function(x) length(unique(x))))
p<-ggplot(df, aes(y=trace, x=iter)) + geom_point() +
labs(title=paste0("Trace plot for the number of groups"))+
theme_bw() + theme(axis.text.x = element_text(angle = 0, hjust = 1,size = 10), strip.text = element_text(size = 15),legend.position = "top", plot.title = element_text(hjust = 0.5))+
geom_hline(yintercept = 16,color = "red")
p
#################################################################################PY
K=16
eps<-0.1
sigma_py<-0.25
funcPY_root<-function(x) {(x/sigma_py)*(prod((x+sigma_py+c(1:S) -1)/(x+c(1:S) -1))-1) - K}
alpha.PY<-.bisec(funcPY_root,0.0001,100)
N_eps<-floor(.compute_tau_mean_large_dim(sigma_py,alpha.PY,eps) + 2*.compute_tau_var_large_dim(sigma_py,alpha.PY,eps))
rl3 <- list(r = 5, N = N_eps, sigma_py=sigma_py, alpha=alpha.PY)
ml3 <- list(ng = it, burnin = burn, typeNames = 'PA', reductList = rl3)
fit3 <- .gjam_3(formula,xdata,Ydata,ml3)
save(fit3,file="models_Bagues_data_OSS/fit3.Rda")
K<-16
eps=0.1
alp_sig<-as.data.frame(matrix(NA,nrow=20,ncol=3))
colnames(alp_sig)<-c("alpha","sigma","is_less_150")
alp_sig$sigma=seq(0.05,0.4,length.out = 20)
#loop to run bisecetion on a grid for sigma
for(i in 1:20){
####corrected added -1
func<-function(x) {(x/alp_sig[i,"sigma"])*(prod((x+alp_sig[i,"sigma"]+c(1:S)-1)/(x+c(1:S) -1))-1) - K}
alp_sig[i,"alpha"]<-.bisec(func,0.01,100)
N_eps<-floor(.compute_tau_mean_large_dim(alp_sig[i,"sigma"], alp_sig[i,"alpha"],eps) + 2*.compute_tau_var_large_dim(alp_sig[i,"sigma"], alp_sig[i,"alpha"],eps))
ifelse(N_eps<=150,alp_sig[i,"is_less_150"]<-T,alp_sig[i,"is_less_150"]<-F)
N_eps
}
if(sum(alp_sig$is_less_150==T)==0) cat("!! no choice under N=150, need to recheck!!!")
k<-max(which(alp_sig$is_less_150==T)) #max sigma s.t. N<150
sigma_py<-alp_sig[k,"sigma"]
alpha.PY<-alp_sig[k,"alpha"]
#fixing hyperparameters
ro.disc=1-2* sigma_py
shape=((alpha.PY)^2)/10
rate=alpha.PY/10
# 95% quantile of alpha
alpha.max=qgamma(.95, shape=shape, rate=rate)
alpha.max_val<-5
sigma_py_max<-0.5
N_eps<-floor(.compute_tau_mean_large_dim(sigma_py_max,alpha.max_val,eps) + 2*.compute_tau_var_large_dim(sigma_py_max,alpha.max_val,eps))
rl4 <- list(r = 5, N =N_eps,rate=rate,shape=shape,V1=1,ro.disc=ro.disc) #here to modify N
ml4 <- list(ng = it, burnin = burn, typeNames = 'PA', reductList = rl4)
fit4<-.gjam_4(formula, xdata = xdata, ydata = Ydata, modelList = ml4)
save(fit4,file="models_Bagues_data_OSS/fit4.Rda")
########################################## Model comparison
fit$fit$rmspeAll #2.257202
fit1$fit$rmspeAll #2.205223
fit2$fit$rmspeAll #2.209276
fit3$fit$rmspeAll #2.203517
fit4$fit$rmspeAll #2.092136
fit$fit$DIC #2510192
fit1$fit$DIC #2496028
fit2$fit$DIC #2505273
fit3$fit$DIC #2496243
fit4$fit$DIC #2460125
#################################################################################Other models
rl2 <- list(r = 8, N = 20,rate=10,shape=10,V=1) #here to modify N
N_eps<-floor(.compute_tau_mean(0.3,2,0.1) + 2*.compute_tau_var(0.3,2,0.1))
rl3 <- list(r = 8, N = N_eps, sigma_py=0.3, alpha=2)
N_eps<-floor(.compute_tau_mean(0.5,10,0.1) + 2*.compute_tau_var(0.5,10,0.1))
rl4 <- list(r = 8, N = N_eps,rate=10,shape=10,V1=1,ro.disc=0.5) #here to modify N
ml4 <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl4) #change ml
ml3 <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl3) #change ml
ml2 <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl2) #change ml
ml1 <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl1) #change ml
ml <- list(ng = 1000, burnin = 500, typeNames = 'DA', reductList = rl) #change ml
form <- as.formula( ~ temp*deficit + I(temp^2) + I(deficit^2) )
fit<-gjam(form, xdata = xdata, ydata = treeYdata, modelList = ml)
fit1<-.gjam_1(form, xdata = xdata, ydata = treeYdata, modelList = ml1)
fit2<-.gjam_2(form, xdata = xdata, ydata = treeYdata, modelList = ml2)
fit3 <- .gjam_3(form,xdata,treeYdata,ml3)
fit4<-.gjam_4(form, xdata = xdata, ydata = treeYdata, modelList = ml4)
fit$fit$rmspeAll #2.257202
fit1$fit$rmspeAll #2.205223
fit2$fit$rmspeAll #2.209276
fit3$fit$rmspeAll #2.203517
fit4$fit$rmspeAll #2.092136
fit$fit$DIC #2510192
fit1$fit$DIC #2496028
fit2$fit$DIC #2505273
fit3$fit$DIC #2496243
fit4$fit$DIC #2460125
#check that alpha and sigma posteriors
#gjam1
alpha<-mcmc(fit1$chains$alpha.DP_g)
plot(alpha)
acfplot(alpha)
cumuplot(alpha)
#gjam2
alpha<-mcmc(fit2$chains$alpha.DP_g)
plot(alpha)
acfplot(alpha)
cumuplot(alpha)
##gjam4
alpha<-mcmc(fit4$chains$alpha.PY_g)
alpha<-mcmc(fit4$chains$alpha.PY_g[seq(1,length(fit4$chains$alpha.PY_g),by=20)])
plot(alpha)
acfplot(alpha)
cumuplot(alpha)
discount<-mcmc(fit4$chains$discount.PY_g)
plot(discount)
acfplot(discount)
cumuplot(discount)
#check the convergence
#for sigma
gjam_mc<- mcmc(fit$chains$sgibbs)
gjam_mc1<- mcmc(fit1$chains$sgibbs)
gjam_mc2<- mcmc(fit2$chains$sgibbs)
gjam_mc3<- mcmc(fit3$chains$sgibbs)
gjam_mc4<- mcmc(fit4$chains$sgibbs)
par(mfrow=c(2,3))
hist(effectiveSize(gjam_mc), main="ess(sigma) gjam",lwd=2,col=gray(.6),breaks=100)
hist(effectiveSize(gjam_mc1), main="ess(sigma) gjam1",lwd=2,col=gray(.6),breaks=100)
hist(effectiveSize(gjam_mc2), main="ess(sigma) gjam2",lwd=2,col=gray(.6),breaks=100)
hist(effectiveSize(gjam_mc3), main="ess(sigma) gjam3",lwd=2,col=gray(.6),breaks=100)
hist(effectiveSize(gjam_mc4), main="ess(sigma) gjam4",lwd=2,col=gray(.6),breaks=100)
# for betas
beta_mcmc<-mcmc(fit$chains$bgibbs)
beta_mcmc1<-mcmc(fit1$chains$bgibbs)
beta_mcmc2<-mcmc(fit2$chains$bgibbs)
beta_mcmc3<-mcmc(fit3$chains$bgibbs)
beta_mcmc4<-mcmc(fit4$chains$bgibbs)
#nESS
par(mfrow=c(2,3))
hist(effectiveSize(beta_mcmc), main="ess(beta) gjam",lwd=2,col=gray(.6))
hist(effectiveSize(beta_mcmc1), main="ess(beta) gjam1",lwd=2,col=gray(.6))
hist(effectiveSize(beta_mcmc2), main="ess(beta) gjam2",lwd=2,col=gray(.6))
hist(effectiveSize(beta_mcmc3), main="ess(beta) gjam3",lwd=2,col=gray(.6))
hist(effectiveSize(beta_mcmc4), main="ess(beta) gjam4",lwd=2,col=gray(.6))
#check the traceplots of K
trace0<-apply(fit$chains$kgibbs,1,function(x) length(unique(x)))
#trace1<-apply(fit1$chains$kgibbs,1,function(x) length(unique(x)))
trace2<-apply(fit2$chains$kgibbs,1,function(x) length(unique(x)))
trace3<-apply(fit3$chains$kgibbs,1,function(x) length(unique(x)))
trace4<-apply(fit4$chains$kgibbs,1,function(x) length(unique(x)))
table<-data.frame()
table<-data.frame("trace"=c(trace0,
#trace1,
trace2,trace3,trace4),
"type"=c(rep("0",length(trace0)),
#rep("1",length(trace1)),
rep("2",length(trace2)),rep("3",length(trace3)),rep("4",length(trace4))),
"x"=rep(1:it,4))
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
cols = gg_color_hue(4)
#single traceplots - not useful
# p1<-ggplot(table[which(table$type=="0"),], aes(x=table$x[which(table$type=="0")],y=table$trace[which(table$type=="0")]))+geom_point()
# p1
# p2<-ggplot(table[which(table$type=="1"),], aes(x=table$x[which(table$type=="1")],y=table$trace[which(table$type=="1")]))+geom_point()
# p2
# p3<-ggplot(table[which(table$type=="2"),], aes(x=table$x[which(table$type=="2")],y=table$trace[which(table$type=="2")]))+geom_point()
# p3
# p4<-ggplot(table[which(table$type=="3"),], aes(x=table$x[which(table$type=="3")],y=table$trace[which(table$type=="3")]))+geom_point()
# p4
# traceplots altogether
p<-ggplot(table, aes(x=x,y=trace,col=as.factor(type)))+geom_point()+
scale_color_manual(name = c(""), values = cols, labels=c("Original model",
#"DP with prior on alpha 1",
"DP with prior on alpha 2","PY with fixed alpha, sigma","PY with prior on alpha, sigma"))+
labs(title="Traceplots of the posterior of the number of clusters")+xlab("iterations")+theme_bw()
#pdf("plot_forest_data/forest_data_trace_K.pdf")
p
#dev.off()
#check the last weight
pk_chains0_last<- mcmc(fit$chains$pk_g[,ncol(fit$chains$pk_g)])
plot(pk_chains1_last)
pk_chains1_last<- mcmc(fit1$chains$pk_g[,ncol(fit1$chains$pk_g)])
plot(pk_chains1_last)
pk_chains2_last<- mcmc(fit2$chains$pk_g[,ncol(fit2$chains$pk_g)])
plot(pk_chains2_last)
pk_chains3_last<- mcmc(fit3$chains$pk_g[,ncol(fit3$chains$pk_g)])
plot(pk_chains3_last)
pk_chains4_last<- mcmc(fit4$chains$pk_g[,ncol(fit4$chains$pk_g)])
plot(pk_chains4_last)
#Out of sample prediction
#TJUR coefficient
sum((fit$prediction$ypredMu[1:100,]-Ydata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5987808
sum((fit1$prediction$ypredMu[1:100,]-treeYdata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5961853
sum((fit2$prediction$ypredMu[1:100,]-treeYdata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5979932
sum((fit3$prediction$ypredMu[1:100,]-treeYdata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5977189
sum((fit4$prediction$ypredMu[1:100,]-treeYdata[1:100,])^2)/sum(treeYdata[1:100,]^2) #0.5983279
#################################################################################Other models - BIOMOD
## Not run:
# species occurrences
DataSpecies <- read.csv(system.file("external/species/mammals_table.csv",package="biomod2"), row.names = 1)
# the presence/absences data for our species
myRespName <- 'VulpesVulpes'
myResp <- as.numeric(DataSpecies[,myRespName])
# the XY coordinates of species data
myRespXY <- DataSpecies[,c("X_WGS84","Y_WGS84")]
# Environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
myExpl = stack( system.file( "external/bioclim/current/bio3.grd",package="biomod2"),
system.file( "external/bioclim/current/bio4.grd",package="biomod2"),
system.file( "external/bioclim/current/bio7.grd",package="biomod2"),
system.file( "external/bioclim/current/bio11.grd", package="biomod2"),
system.file( "external/bioclim/current/bio12.grd",package="biomod2"))
# 1. Formatting Data
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName)
# 2. Defining Models Options using default options.
myBiomodOption <- BIOMOD_ModelingOptions()
# 3. Doing Modelisation
myBiomodModelOut <- BIOMOD_Modeling( myBiomodData,
models = c('SRE'),
models.options = myBiomodOption,
NbRunEval=1,
DataSplit=80,
Prevalence=0.5,
VarImport=0,
models.eval.meth = c('TSS','ROC'),
do.full.models=FALSE,
modeling.id="test2")
# files have been created on hard drive
list.files(myRespName,all.files=TRUE,recursive=TRUE)
# remove properly the modeling objects and all the file saved on hard drive
RemoveProperly(myBiomodModelOut)
# check files had been removed
list.files(myRespName,all.files=TRUE,recursive=TRUE)
## End(Not run)
r <- raster(nrow=18, ncol=36, xmn=0)
r[150:250] <- 1
r[251:450] <- 2
plot( boundaries(r, type='inner') )
plot( boundaries(r, type='outer') )
plot( boundaries(r, classes=TRUE) )
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