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2b_REA_hc.ma_compare_models.R
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################################################################################
#Compare to other model structures
################################################################################
## Mostly interested in whether there should be some version of latitudinal spatial structure in the model
## OR
## Whether the relationship should be fit with a polynomial rather than a linear relationship
#******************************************************************************
#Final model; Hc*Depth*Shelf
#******************************************************************************
load(file=paste0(FINAL_REA_PATH, 'depthshelf.inla.presence.RData'))
dic.final<- data.frame(model= "final",
dic=depthshelf.inla.presence$dic$dic,
waic=depthshelf.inla.presence$waic$waic)
#******************************************************************************
#Depth*Shelf + sector
#******************************************************************************
load(file=paste0(REA_PROCESSED_PATH, 'dat.ma.present.RData'))
load(file=paste0(REA_PROCESSED_PATH, 'dat.beta.RData'))
i.start <- 1 + dat.ma.present %>% nrow()
newdata.1 <- dat.beta %>%
filter(Cover.ma>0) %>% droplevels() %>%
group_by(shelf, depth) %>%
nest() %>%
mutate(hc = map(.x = data, .f=function(x=.x) seq(min(x$hc), max(x$hc), length=100))) %>%
#restrict predictions to actual range of observed HC
unnest(hc) %>%
dplyr::select(-data)
dat.ma.present <- dat.ma.present %>%
bind_rows(newdata.1)
i.end <- dat.ma.present %>% nrow()
set.seed(123)
mahc.present.addsector <- inla(Cover.ma ~ hc*depth*shelf + sector +
f(reef, model='iid')+
f(site, model='iid')+
f(transect, model='iid'),
data=dat.ma.present,
family="beta",
control.predictor=list(compute=TRUE, link=1),
control.compute=list(config = TRUE, dic=TRUE, cpo=TRUE, waic=TRUE))
dic.sector<- data.frame(model= "add.sector",
dic=mahc.present.addsector$dic$dic,
waic=mahc.present.addsector$waic$waic)
#******************************************************************************
#Depth*Shelf + latitude
#******************************************************************************
load(file=paste0(REA_PROCESSED_PATH, 'dat.ma.present.RData'))
load(file=paste0(REA_PROCESSED_PATH, 'dat.beta.RData'))
i.start <- 1 + dat.ma.present %>% nrow()
newdata.1 <- dat.beta %>%
filter(Cover.ma>0) %>% droplevels() %>%
group_by(shelf, depth) %>%
nest() %>%
mutate(hc = map(.x = data, .f=function(x=.x) seq(min(x$hc), max(x$hc), length=100))) %>%
#restrict predictions to actual range of observed HC
unnest(hc) %>%
dplyr::select(-data)
dat.ma.present <- dat.ma.present %>%
bind_rows(newdata.1)
i.end <- dat.ma.present %>% nrow()
set.seed(123)
mahc.present.addLat <- inla(Cover.ma ~ hc*depth*shelf + lat +
f(reef, model='iid')+
f(site, model='iid')+
f(transect, model='iid'),
data=dat.ma.present,
family="beta",
control.predictor=list(compute=TRUE, link=1),
control.compute=list(config = TRUE, dic=TRUE, cpo=TRUE, waic=TRUE))
dic.latitude<- data.frame(model= "add.latitude",
dic=mahc.present.addLat$dic$dic,
waic=mahc.present.addLat$waic$waic)
#******************************************************************************
#Depth*Shelf + region
#******************************************************************************
load(file=paste0(REA_PROCESSED_PATH, 'dat.ma.present.RData'))
load(file=paste0(REA_PROCESSED_PATH, 'dat.beta.RData'))
dat.beta<- dat.beta %>%
mutate(region=as.factor(case_when(sector %in% c("CA","IN","TO","CU","WH")~"Central",
sector %in% c("CG","PC","CL")~"North",
sector %in% c("PO","SW","CB")~"South")))
levels(dat.beta$region)
dat.ma.present<- dat.ma.present %>%
mutate(region=as.factor(case_when(sector %in% c("CA","IN","TO","CU","WH")~"Central",
sector %in% c("CG","PC","CL")~"North",
sector %in% c("PO","SW","CB")~"South")))
levels(dat.ma.present$region)
i.start <- 1 + dat.ma.present %>% nrow()
newdata.1 <- dat.beta %>%
filter(Cover.ma>0) %>% droplevels() %>%
group_by(shelf, depth) %>%
nest() %>%
mutate(hc = map(.x = data, .f=function(x=.x) seq(min(x$hc), max(x$hc), length=100))) %>%
#restrict predictions to actual range of observed HC
unnest(hc) %>%
dplyr::select(-data)
dat.ma.present <- dat.ma.present %>%
bind_rows(newdata.1)
i.end <- dat.ma.present %>% nrow()
set.seed(123)
mahc.present.addregion <- inla(Cover.ma ~ hc*depth*shelf + region +
f(reef, model='iid')+
f(site, model='iid')+
f(transect, model='iid'),
data=dat.ma.present,
family="beta",
control.predictor=list(compute=TRUE, link=1),
control.compute=list(config = TRUE, dic=TRUE, cpo=TRUE, waic=TRUE))
dic.region<- data.frame(model= "add.region",
dic=mahc.present.addregion$dic$dic,
waic=mahc.present.addregion$waic$waic)
#***********************************************************
# with sector as an interacting predictor *
#macroalgae~ hc)*Depth*Shelf*sector *
#***********************************************************
load(file=paste0(REA_PROCESSED_PATH, 'dat.ma.present.RData'))
load(file=paste0(REA_PROCESSED_PATH, 'dat.beta.RData'))
i.start <- 1 + dat.ma.present %>% nrow()
newdata.1 <- dat.beta %>%
filter(Cover.ma>0) %>% droplevels() %>%
group_by(sector, shelf, depth) %>%
nest() %>%
mutate(hc = map(.x = data, .f=function(x=.x) seq(min(x$hc), max(x$hc), length=100))) %>%
#restrict predictions to actual range of observed HC
unnest(hc) %>%
dplyr::select(-data)
dat.ma.present <- dat.ma.present %>%
bind_rows(newdata.1)
i.end <- dat.ma.present %>% nrow()
set.seed(123)
mahc.present.sector.interact <- inla(Cover.ma ~ hc*depth*shelf*sector +
f(reef, model='iid')+
f(site, model='iid')+
f(transect, model='iid'),
data=dat.ma.present,
family="beta",
control.predictor=list(compute=TRUE, link=1),
control.compute=list(config = TRUE, dic=TRUE, cpo=TRUE, waic=TRUE))
dic.sector.interact<- data.frame(model= "sector.interact",
dic=mahc.present.sector.interact$dic$dic,
waic=mahc.present.sector.interact$waic$waic)
#***********************************************************
# with sector as a random effect *
#macroalgae~ hc*Depth*Shelf *
#***********************************************************
load(file=paste0(REA_PROCESSED_PATH, 'dat.ma.present.RData'))
load(file=paste0(REA_PROCESSED_PATH, 'dat.beta.RData'))
i.start <- 1 + dat.ma.present %>% nrow()
newdata.1 <- dat.beta %>%
filter(Cover.ma>0) %>% droplevels() %>%
group_by(shelf, depth) %>%
nest() %>%
mutate(hc = map(.x = data, .f=function(x=.x) seq(min(x$hc), max(x$hc), length=100))) %>%
#restrict predictions to actual range of observed HC
unnest(hc) %>%
dplyr::select(-data)
dat.ma.present <- dat.ma.present %>%
bind_rows(newdata.1)
i.end <- dat.ma.present %>% nrow()
set.seed(123)
mahc.present.sector.random <- inla(Cover.ma ~ hc*depth*shelf +
f(sector, model='iid') +
f(reef, model='iid')+
f(site, model='iid')+
f(transect, model='iid'),
data=dat.ma.present,
family="beta",
control.predictor=list(compute=TRUE, link=1),
control.compute=list(config = TRUE, dic=TRUE, cpo=TRUE, waic=TRUE))
dic.sector.random<- data.frame(model= "sector.random",
dic=mahc.present.sector.random$dic$dic,
waic=mahc.present.sector.random$waic$waic)
#******************************************************
# Compare models
#******************************************************
compare.models<- bind_rows(dic.final,
dic.sector,
dic.latitude,
dic.region,
dic.sector.interact,
dic.sector.random
) %>%
arrange(waic) %>%
mutate(waic.diff=waic-first(waic))
#******************************************************
# Conclusion
#******************************************************
##Based on DIC and WAIC, the final model, which did not have a spatial interacting variable or spatial covariate, was the best model structure