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3.3_plotexport.R
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source("0_County_subset.R")
source("2.1_Run_Fit.R")
source("2.2_Basic_Diagnostics_Function.R")
source("3.1_Random_Walk_Fit.R")
source("3.2_Historical_Random_Walk.R")
par(mfrow=c(2,2))
#Califormia San Diego, aegypti plot
i = 4
model <- Random_Walk_Fit(county.name = counties[i],
spp = "aegypti",
data.set = data.fit,
n.adapt = 15000)
out <- Run_Fit(model,
variable.names = c("tau_proc", "x"),
n.iter = 20000)
cat("\n\nIterations Complete for", counties[i], "\n", i, "/", length(counties), "counties complete\n\n")
# plot model diagnostics, remove burnin
#out <- diagnostics(out$params, out$predict, 5000)
# convert to matrix
state <- as.matrix(out$predict)
params <- as.matrix(out$params)
# remove spaces from counties[i]
county.name <- gsub(" ", "", counties[i], fixed = TRUE)
file.name <- paste("aegypti_", county.name, "_Precip_Tmax_RH.RData", sep = "")
dir <- 'RandomWalk_Out'
save(params, predict, file = file.path(dir, file.name))
# confidence intervals
ci.state <- apply(state, 2, quantile, c(0.025,0.5,0.975))
time <- 1:ncol(ci.state)
# get county of interest and create a "year-month" column
county.sub <- data.fit %>%
filter(state_county == counties[i])
for(i in 1:nrow(county.sub)){
if(county.sub$month[i]==10){
county.sub$new.month[i] <- 91
} else if(county.sub$month[i]==11){
county.sub$new.month[i] <- 92
} else if(county.sub$month[i]==12){
county.sub$new.month[i] <- 93
} else {
county.sub$new.month[i] <- county.sub$month[i]
}
}
county.sub <- county.sub %>%
unite("year_month", year, month, sep = "-", remove = FALSE) %>%
unite("year_month_seq", year, new.month, sep = "-")
# aggregate counts for each month, as they are separated by trap type
y.aegypti <- aggregate(county.sub$num_aegypti_collected, by = list(county.sub$year_month_seq), FUN = sum)[,2]
# vector of all dates in each time series
year.month = unique(county.sub$year_month)
N.months = length(year.month)
# indexing vector, only puts 7 dates on the axis
at <- seq(1, N.months, length.out = 7)
plot(time, ci.state[2,], main = paste("Aedes aegypti", "\n", "California: San Diego County"), pch="", xaxt = "n", xlab = "Year - Month", ylab = "Individual Ades aegypti")
ciEnvelope(time, ci.state[1,], ci.state[3,], col = "lightblue")
points(time, y.aegypti, pch = 16)
axis(1, at = at, labels = year.month[at])
#Florida Polk, aegypti plot
i = 10
model <- Random_Walk_Fit(county.name = counties[i],
spp = "aegypti",
data.set = data.fit,
n.adapt = 15000)
out <- Run_Fit(model,
variable.names = c("tau_proc", "x"),
n.iter = 20000)
cat("\n\nIterations Complete for", counties[i], "\n", i, "/", length(counties), "counties complete\n\n")
# plot model diagnostics, remove burnin
# out <- diagnostics(out$params, out$predict, 5000)
# convert to matrix
state <- as.matrix(out$predict)
params <- as.matrix(out$params)
# remove spaces from counties[i]
county.name <- gsub(" ", "", counties[i], fixed = TRUE)
file.name <- paste("aegypti_", county.name, "_Precip_Tmax_RH.RData", sep = "")
dir <- 'RandomWalk_Out'
save(params, predict, file = file.path(dir, file.name))
# confidence intervals
ci.state <- apply(state, 2, quantile, c(0.025,0.5,0.975))
time <- 1:ncol(ci.state)
# get county of interest and create a "year-month" column
county.sub <- data.fit %>%
filter(state_county == counties[i])
for(i in 1:nrow(county.sub)){
if(county.sub$month[i]==10){
county.sub$new.month[i] <- 91
} else if(county.sub$month[i]==11){
county.sub$new.month[i] <- 92
} else if(county.sub$month[i]==12){
county.sub$new.month[i] <- 93
} else {
county.sub$new.month[i] <- county.sub$month[i]
}
}
county.sub <- county.sub %>%
unite("year_month", year, month, sep = "-", remove = FALSE) %>%
unite("year_month_seq", year, new.month, sep = "-")
# aggregate counts for each month, as they are separated by trap type
y.aegypti <- aggregate(county.sub$num_aegypti_collected, by = list(county.sub$year_month_seq), FUN = sum)[,2]
# vector of all dates in each time series
year.month = unique(county.sub$year_month)
N.months = length(year.month)
# indexing vector, only puts 7 dates on the axis
at <- seq(1, N.months, length.out = 7)
plot(time, ci.state[2,], main = paste("Aedes aegypti", "\n", "Florida: Polk County"), pch="", xaxt = "n", xlab = "Year - Month", ylab = "Individual Ades aegypti")
ciEnvelope(time, ci.state[1,], ci.state[3,], col = "lightblue")
points(time, y.aegypti, pch = 16)
axis(1, at = at, labels = year.month[at])
#CA orange, albopictus
i = 2
model <- Random_Walk_Fit(county.name = counties[i],
spp = "albo",
data.set = data.fit,
n.adapt = 15000)
out <- Run_Fit(model,
variable.names = c("tau_proc", "x"),
n.iter = 20000)
cat("\n\nIterations Complete for", counties[i], "\n", i, "/", length(counties), "counties complete\n\n")
# plot model diagnostics, remove burnin
#out <- diagnostics(out$params, out$predict, 5000)
# convert to matrix
state <- as.matrix(out$predict)
params <- as.matrix(out$params)
# remove spaces from counties[i]
county.name <- gsub(" ", "", counties[i], fixed = TRUE)
file.name <- paste("albopictus_", county.name, "_Precip_Tmax_RH.RData", sep = "")
dir <- 'RandomWalk_Out'
save(params, predict, file = file.path(dir, file.name))
# confidence intervals
ci.state <- apply(state, 2, quantile, c(0.025,0.5,0.975))
time <- 1:ncol(ci.state)
# get county of interest and create a "year-month" column
county.sub <- data.fit %>%
filter(state_county == counties[i])
for(i in 1:nrow(county.sub)){
if(county.sub$month[i]==10){
county.sub$new.month[i] <- 91
} else if(county.sub$month[i]==11){
county.sub$new.month[i] <- 92
} else if(county.sub$month[i]==12){
county.sub$new.month[i] <- 93
} else {
county.sub$new.month[i] <- county.sub$month[i]
}
}
county.sub <- county.sub %>%
unite("year_month", year, month, sep = "-", remove = FALSE) %>%
unite("year_month_seq", year, new.month, sep = "-")
# aggregate counts for each month, as they are separated by trap type
y.albo <- aggregate(county.sub$num_albopictus_collected, by = list(county.sub$year_month_seq), FUN = sum)[,2]
# vector of all dates in each time series
year.month = unique(county.sub$year_month)
N.months = length(year.month)
# indexing vector, only puts 7 dates on the axis
at <- seq(1, N.months, length.out = 7)
plot(time, ci.state[2,], main = paste("Aedes albopictus", "\n", "California: San Diego County"),pch="", xaxt= "n", xlab = "Year - Month", ylab = "Individual Ades albopictus")
ciEnvelope(time, ci.state[1,], ci.state[3,], col = "lightblue")
points(time, y.albo, pch = 16)
axis(1, at = at, labels = year.month[at])
#Fl Hillsborough, albopictus
i = 5
model <- Random_Walk_Fit(county.name = counties[i],
spp = "albo",
data.set = data.fit,
n.adapt = 15000)
out <- Run_Fit(model,
variable.names = c("tau_proc", "x"),
n.iter = 20000)
cat("\n\nIterations Complete for", counties[i], "\n", i, "/", length(counties), "counties complete\n\n")
# plot model diagnostics, remove burnin
#out <- diagnostics(out$params, out$predict, 5000)
# convert to matrix
state <- as.matrix(out$predict)
params <- as.matrix(out$params)
# remove spaces from counties[i]
county.name <- gsub(" ", "", counties[i], fixed = TRUE)
file.name <- paste("albopictus_", county.name, "_Precip_Tmax_RH.RData", sep = "")
dir <- 'RandomWalk_Out'
save(params, predict, file = file.path(dir, file.name))
# confidence intervals
ci.state <- apply(state, 2, quantile, c(0.025,0.5,0.975))
time <- 1:ncol(ci.state)
# get county of interest and create a "year-month" column
county.sub <- data.fit %>%
filter(state_county == counties[i])
for(i in 1:nrow(county.sub)){
if(county.sub$month[i]==10){
county.sub$new.month[i] <- 91
} else if(county.sub$month[i]==11){
county.sub$new.month[i] <- 92
} else if(county.sub$month[i]==12){
county.sub$new.month[i] <- 93
} else {
county.sub$new.month[i] <- county.sub$month[i]
}
}
county.sub <- county.sub %>%
unite("year_month", year, month, sep = "-", remove = FALSE) %>%
unite("year_month_seq", year, new.month, sep = "-")
# aggregate counts for each month, as they are separated by trap type
y.albo <- aggregate(county.sub$num_albopictus_collected, by = list(county.sub$year_month_seq), FUN = sum)[,2]
# vector of all dates in each time series
year.month = unique(county.sub$year_month)
N.months = length(year.month)
# indexing vector, only puts 7 dates on the axis
at <- seq(1, N.months, length.out = 7)
plot(time, ci.state[2,], main = paste("Aedes albopictus", "\n", "Florida: Polk County"),pch="", xaxt= "n", xlab = "Year - Month", ylab = "Individual Ades albopictus")
ciEnvelope(time, ci.state[1,], ci.state[3,], col = "lightblue")
points(time, y.albo, pch = 16)
axis(1, at = at, labels = year.month[at])