.Rhistory

setwd(./RawData)
setwd("./RawData")
getwd()
setwd("~/RawData")
setwd("C:/Users/farrm/Documents/GitHub/HMSNO/RawData")
setwd("C:/Users/farrm/Documents/GitHub/HMSNO/DataFormat")
library(readxl)
library(readxl)
read_excel("RawData/Data_Bwindi_nig_sylv_Nov18.xlsx", sheet = 1)
Bwindi_nig <- read_excel("RawData/Data_Bwindi_nig_sylv_Nov18.xlsx", sheet = 1)
install.packages("reshape")
library(reshape)
View(Bwindi_nig)
Bwindi_nig <- Bwindi_nig[,c(1:7,9,18,19)]
View(Bwindi_nig)
tst <- melt(Bwindi_nig, id = c("deployment ID", "Year", "elevation", "Distance"))
Bwindi_nig <- read_excel("RawData/Data_Bwindi_nig_sylv_Nov18.xlsx", sheet = 1)
Bwindi_nig <- Bwindi_nig[,c(1:7,9)]
tst <- melt(Bwindi_nig, id = c("deployment ID", "Year"))
View(Bwindi_nig)
tst <- melt(Bwindi_nig, id = c(1,8))
library(reshape2)
library(readxl)
Bwindi_nig <- read_excel("RawData/Data_Bwindi_nig_sylv_Nov18.xlsx", sheet = 1)
Bwindi_nig <- Bwindi_nig[,c(1:7,9)]
tst <- melt(Bwindi_nig, id = c(1,8))
View(tst)
tst <- melt(Bwindi_nig, id = c(8,1))
tst <- melt(Bwindi_nig, id = c(1,8))
View(tst)
library(dplyr)
tst <- tst %>% arrange(deployment ID, Year)
tst <- tst %>% arrange(`deployment ID`, Year)
View(tst)
tst <- grep("-", tst, NA)
tst <- melt(Bwindi_nig, id = c(1,8))
tst <- tst %>% arrange(`deployment ID`, Year)
tst <- gsub("-", NA, tst)
tst <- melt(Bwindi_nig, id = c(1,8))
tst <- tst %>% arrange(`deployment ID`, Year)
View(tst)
tst$value <- gsub("-", NA, tst$value)
setwd("C:/Users/farrm/Documents/GitHub/HMSNO/DataFormat")
library(readxl)
library(dplyr)
library(reshape2)
library(jagsUI)
Data <- NULL
files <- list.files(path = "./EditedData", pattern = "Data", full.names = TRUE)
for(i in 1:length(files)){
sheets <- excel_sheets(files[i])
for(j in 1:length(sheets)){
Temp <- read_excel(files[i], sheet = sheets[j])
Temp <- Temp %>% mutate(species = as.character(sheets[j]), park = as.factor(i)) %>%
select(park, `deployment ID`, Year, species, ls(Temp, pattern = "R"), elevation, edge)
Data <- bind_rows(Data, Temp)
}
}
#Convert to NAs
Data[,5:10] <- apply(Data[,5:10], 2, function(y) as.numeric(gsub("-", NA, y)))
#Convert to single vector format
Data2 <- melt(Data, id = c("park", "deployment ID", "Year", "species", "elevation", "edge"))
#Occupancy data
y <- Data2$value
#Indices
nyrs <- as.numeric(Data %>% summarize(n_distinct(Year)))
nsites <- as.numeric(Data %>% summarize(n_distinct(`deployment ID`)))
nspec <- as.numeric(Data %>% summarize (n_distinct(species)))
nobs <- length(y)
#Nested indices
yr <- as.numeric(as.factor(Data2$Year))
site <- as.numeric(as.factor(Data2$`deployment ID`))
spec <- as.numeric(as.factor(Data2$species))
#Data
jags.data <- list(y = y, nobs = nobs, nyrs = nyrs, nsites = nsites, nspec = nspec, yr = yr, site = site, spec = spec)
#Initial values
Nst <- array(NA, dim = c(nyrs, nsites, nspec))
Nst[1,,] <- 10
Sst <- array(5, dim = c(nyrs-1, nsites, nspec))
Gst <- array(2, dim = c(nyrs-1, nsites, nspec))
inits <- function()list(N=Nst, S=Sst, G=Gst)
#Parameters to save
params <- c("mu.a0", "tau.a0", "mu.b0", "tau.b0", "mu.o0", "tau.o0",
"mu.g0", "tau.g0", "mu.g1", "tau.g1",
"alpha0", "beta0", "omega0", "gamma0")
#MCMC settings
ni <- 50000
na <- 1000
nb <- 41000
nc <- 3
nt <- 3
#Parameters to save
params <- c("mu.a0", "tau.a0", "mu.b0", "tau.b0", "mu.o0", "tau.o0",
"mu.g0", "tau.g0",
"alpha0", "beta0", "omega0", "gamma0")
#Run JAGS model
out <- jagsUI(jags.data, inits, params, model.file = "../MSdyn.txt",
n.adapt = na, n.chains = nc, n.iter = ni, n.burnin = nb, n.thin = nt,
parallel = TRUE)
out
traceplot
traceplot(out)
#Run JAGS model
out <- jagsUI(jags.data, inits, params, model.file = "../MSdyn.txt",
n.adapt = na, n.chains = nc, n.iter = ni, n.burnin = nb, n.thin = nt,
parallel = TRUE)
out
traceplot(out)
?update
out1 <- jagsUI::update(ou, params, n.adpat=na, n.iter = 9000, n.thin = 3)
out1 <- update(ou, params, n.adpat=na, n.iter = 9000, n.thin = 3)
library(jagsUI)
cor(Data2$elevation, Data2$edge)
plot(Data2$elevation, Data2$edge)
hist(Data2$elevation)
sd(Data2$elevation)
sd(Data2$edge)
hist(Data2$edge)
Data
#Covariates
elevation <- Data$elevation
edge <- Data$edge
length(elevation)
nsites
#Covariates
elevation <- Data %>% group_by(`deployment ID`) %>% select(elevation)
elevation
Data %>% filter(`deployment ID` == `CT-BIF-1-01`)
Data %>% filter(`deployment ID` == "CT-BIF-1-01")
#Covariates
elevation <- Data %>% group_by(`deployment ID`) %>% select(elevation, edge) %>% unique()
elevation
nsites
View(elevation)
View(elevation)
setwd("~/Users/farrm/Documents/GitHub/Monarchs/DataFormatting/MonarchData/eButterfly")
data <- read.csv("all_records_2019_02_26_10_00_11.csv", header = TRUE)
table(Genus)
table(data$Genus)
library(dplyr)
data %>% filter(Genus == "Danaus") %>% summarize(n_distinct(Species))
data %>% filter(Genus == "Danaus") %>% group_by(Species) %>% summarize(n())
data %>% filter(Genus == "Danaus" & Species == "plexippus") %>% summarize(n())
data2 <- data %>% filter(Genus == "Danaus" & Species == "plexippus")
View(data2)
write.csv(data2, file = "eButterfly_Monarch.csv")
library(lme4)
library(dplyr)
#data
wq<-read.csv("Probereading_all_1st.csv")
setwd("~/Users/farrm/Downloads")
#data
wq<-read.csv("Probereading_all_1st.csv")
setwd("C:/Users/farrm/Documents/GitHub/HMSNO/DataFormat")
library(readxl)
install.packages("tidyverse")
library(readxl)
library(dplyr)
library(reshape2)
install.packages("jagsUI")
library(jagsUI)
Data <- NULL
files <- list.files(path = "./EditedData", pattern = "Data", full.names = TRUE)
for(i in 1:length(files)){
sheets <- excel_sheets(files[i])
for(j in 1:length(sheets)){
Temp <- read_excel(files[i], sheet = sheets[j])
Temp <- Temp %>% mutate(species = as.character(sheets[j]), park = as.factor(i)) %>%
select(park, `deployment ID`, Year, species, ls(Temp, pattern = "R"), elevation, edge, density)
Data <- bind_rows(Data, Temp)
}
}
View(Data)
Data$elevation
mean(Data$elevation)
sd(Data$elevation)
scale(seq(1:10, by = 1))
scale(seq(1, 10, by = 1))
scale(seq(11, 20, by = 1))
spde <- inla.spde2.matern(mesh, constr=TRUE,
B.tau = cbind(0,1),
B.kappa = cbind(log(sqrt(8)/2000), 0),
theta.prior.mean = 0,
theta.prior.prec = 0.001)