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building_up_the_model.R
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building_up_the_model.R
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library(Hmsc)
library(corrplot)
library(ape)
library(tidyverse)
library(readxl)
library(readr)
phylo = ape::read.tree("data/Tronderlag/Ctree (1).tre")
Tr = read.csv("data/Tronderlag/traits.csv")
TOVE_Points_2006_2020 <- read_excel("data/TOVE_Points_2006_2020.xlsx")
Habitat_data_TOVE_routes <- read_delim("data/Tronderlag/Habitat_data_TOVE_routes.csv",
delim = ";", escape_double = FALSE, trim_ws = TRUE)
str(Habitat_data_TOVE_routes)
Habitat_data_TOVE_routes$YEAR=as.numeric(Habitat_data_TOVE_routes$Aar)
Habitat_data_TOVE_routes$PointID=as.numeric(Habitat_data_TOVE_routes$PktNr)
Habitat_data_TOVE_routes$RouteID=as.numeric(Habitat_data_TOVE_routes$RuteNr)
TOVE_Points_2006_2020=TOVE_Points_2006_2020 %>%
left_join(.,Habitat_data_TOVE_routes, by=c("RouteID"="RouteID", "PointID"="PointID"))
#convert to sites on left and species on top
dim(TOVE_Points_2006_2020)
TOVE_points<-TOVE_Points_2006_2020 %>%
select(RouteID, YEAR.x, PointID, ScientificName, FK_Vegetasjonstype1, FK_Vegetasjonstype2, FK_Vegetasjonstype3) %>%
mutate(YEAR=YEAR.x) %>%
select(!YEAR.x)
TOVE_points_data=TOVE_points %>%
mutate(count=1) %>%
group_by(RouteID, PointID, YEAR, ScientificName,FK_Vegetasjonstype1,FK_Vegetasjonstype2,FK_Vegetasjonstype3) %>%
summarise(count=sum(count)) %>%
pivot_wider(id_cols=c(RouteID,PointID, YEAR,FK_Vegetasjonstype1,FK_Vegetasjonstype2,FK_Vegetasjonstype3),
names_from=ScientificName,
values_from=count,
values_fill = 0)
TOVE_points_data_na=
TOVE_points_data %>%
drop_na()
#species occ
Y = as.matrix(TOVE_points_data_na[,7:222])
# Variable selection
studyDesign = TOVE_points_data_na[,c("RouteID","YEAR", "PointID")]
studyDesign = data.frame(apply(studyDesign,2,as.factor))
str(studyDesign)
studyDesign$RouteID=as.factor(studyDesign$RouteID)
studyDesign$YEAR=as.factor(studyDesign$YEAR)
studyDesign$PointID=as.factor(studyDesign$PointID)
# Variable structuring
RouteID = HmscRandomLevel(units = studyDesign$RouteID)
YEAR = HmscRandomLevel(units = studyDesign$YEAR)
PointID=HmscRandomLevel(units=studyDesign$PointID)
ranlevels = list(RouteID = RouteID, YEAR = YEAR, PointID=PointID)
ranlevels
cov = TOVE_points_data_na[,4:6 ]
X = as.data.frame(cov[,c("FK_Vegetasjonstype1","FK_Vegetasjonstype2",
"FK_Vegetasjonstype3")])
XFormula = ~FK_Vegetasjonstype1+FK_Vegetasjonstype2+FK_Vegetasjonstype3
## Phylogeny
phylo
phylo$tip.label<-gsub("_"," ", phylo$tip.label)
phylo$tip.label[c(2,15,16,18)]
tips2drop=c("Corvus corone","Parus cristatus",
"Parus montanus", "Parus caeruleus")
phylo=drop.tip(phylo, tips2drop)
C<-ape::cophenetic.phylo(phylo)
#Traits
colnames(Tr)
Tr$Species=gsub("_", " ", Tr$Species)
splist=Tr$Species[Tr$Species%in% colnames(TOVE_points_data_na[,7:222])]
TOVE_points_data_na %>%
ungroup() %>%
select(splist)->Y2
#Y2
Y = as.matrix(Y2)
Y.pa = Y
Y.pa[Y>1] = 1
Tr=Tr %>%
filter(Species%in% colnames(Y2))
rownames(Tr)<-Tr[,1]
Tr = as.data.frame(Tr[,-1])
dim(Tr)
TrFormula = ~Migration +
Mass +
Urb +
Br+
Co+
Op+
Ma+
We
Tr=Tr[ order(row.names(Tr)), ]
Y=Y[,order(colnames(Y))]
dim(Tr)
dim(Y)
dim(C)
is.ultrametric.phylo(phylo)# TRUE
simul <- Hmsc(Y=Y,
XData = X,
XFormula = XFormula,
TrData = Tr,
TrFormula = TrFormula ,
phyloTree = phylo,
studyDesign = studyDesign,
ranLevels = ranlevels,
distr = "poisson")
simul2 <- Hmsc(Y=Y.pa,
XData = X,
XFormula = XFormula,
TrData = Tr,
TrFormula = TrFormula ,
phyloTree = phylo,
studyDesign = studyDesign,
ranLevels = ranlevels,
distr = "probit")
simul2
thin = 1
samples = 50
nChains = 2
transient = 50
mod_HMSC = sampleMcmc(simul,
samples = samples,
thin = thin,
transient = transient,
nChains = nChains,
nParallel = nChains)
mod2_HMSC = sampleMcmc(simul2,
samples = samples,
thin = thin,
transient = transient,
nChains = nChains,
nParallel = nChains)
save(mod_HMSC,file="mod_HMSC.Rdata")
save(mod2_HMSC,file="mod2_HMSC.Rdata")
postBeta = getPostEstimate(mod2_HMSC, parName = "Beta")
par(mar=c(5,11,2.5,0))
plotBeta(mod2_HMSC,
post = postBeta,
plotTree = F,
spNamesNumbers = c(T,F))
par(mar=c(5,11,2.5,0))
postGamma = getPostEstimate(mod2_HMSC, parName = "Gamma")
plotGamma(mod_HMSC, post = postGamma, supportLevel = 0.2)