/
development_space.R
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development_space.R
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devtools::load_all()
devtools::document()
main <- "~/pc"
setup_production(main = main)
portalcast(main = main)
#, models = "ESSS")
evaluate_casts(main = main)
jrw <- jags_RW(main = main)
jl <- jags_logistic(main = main)
jlc <- jags_logistic_covariates(main = main)
jlo <- jags_logistic_competition(main = main)
jlcc <- jags_logistic_competition_covariates(main = main)
dataset = "dm_controls"
settings = directory_settings()
control_runjags = runjags_control(silent_jags = FALSE, adapt = 1e3, burnin = 1e3, sample = 1e3)
quiet = FALSE
verbose = TRUE
dataset = NULL
cast_id = 3000
cast_groups = NULL
dataset = NULL
model = NULL
end_moon = NULL
species = NULL
start_moon = 217
rodents_table <- read_rodents_table(main = main,
dataset = dataset,
settings = settings)
covariates <- read_covariates(main = main,
settings = settings)
metadata <- read_metadata(main = main,
settings = settings)
start_moon <- metadata$time$start_moon
end_moon <- metadata$time$end_moon
true_count_lead <- length(metadata$time$rodent_cast_moons)
confidence_level <- metadata$confidence_level
dataset_controls <- metadata$dataset_controls[[dataset]]
species <- species_from_table(rodents_tab = rodents_table,
total = TRUE,
nadot = TRUE)
nspecies <- length(species)
mods <- named_null_list(species)
casts <- named_null_list(species)
cast_tab <- data.frame()
moon_in <- which(rodents_table$newmoonnumber >= start_moon & rodents_table$newmoonnumber <= end_moon)
moon <- rodents_table[moon_in, "newmoonnumber"]
count <- rodents_table[moon_in, species]
warm_rain_three_months <- covariates$warm_precip_3_month
ndvi_twelve_months <- covariates$ndvi_12_month
warm_rain_three_months <- warm_rain_three_months[weather$newmoonnumber >= start_moon & weather$newmoonnumber <= max(rodents_table$newmoonnumber)]
ndvi_twelve_months <- ndvi_twelve_months[ndvi$newmoonnumber >= start_moon & ndvi$newmoonnumber <= max(rodents_table$newmoonnumber)]
diff_log_count <- diff(log(count))
n <- length(count)
plot(diff_log_count, type = 'l')
plot((count)[-n], diff_log_count)
plot(density(diff_log_count, na.rm = TRUE))
plot(log10(warm_rain_three_months[-n] + 0.1), diff_log_count)
past_rain <- log10(warm_rain_three_months[-n] + 0.1)
nas <- is.na(diff_log_count)
diff_log_count_nnas <- diff_log_count[!nas]
past_rain_nnas <- past_rain[!nas]
sm <- (lm(diff_log_count_nnas ~ past_rain_nnas))
pred_r <- predict(sm, newdata = list(past_rain_nnas = past_rain))
plot(ndvi_twelve_months)
h<-
summarize_rodent_data(path = file.path(main, settings$subs$resources), clean = TRUE, level = "Plot",
type = "Rodents", plots = "Longterm", unknowns = FALSE,
shape = "crosstab", time = "newmoon", output = "abundance",
na_drop = FALSE, zero_drop = FALSE, min_traps = 1,
min_plots = 24, effort = FALSE, download_if_missing = TRUE,
quiet = FALSE, include_unsampled = FALSE)
h <- h[h$treatment == "control" & h$newmoonnumber >= start_moon & h$newmoonnumber <= max(rodents_table$newmoonnumber), ]
nmoons <- length(217:558)
maxval <- numeric(nmoons)
nvals <- numeric(nmoons)
for(i in 1:nmoons){
maxval[i] <- max(h$DM[h$newmoonnumber == (217:558)[i]], na.rm = TRUE)
nvals[i] <- length(na.omit(h$DM[h$newmoonnumber == (217:558)[i]]))
}
estK <- maxval * nvals
nas <- is.na(estK)
estK_nnas <- estK[!nas]
ndvi_twelve_months_nnas <- ndvi_twelve_months[!nas]
mm <- lm(estK_nnas ~ ndvi_twelve_months_nnas)
summary(mm)
plot(ndvi_twelve_months, estK)
abline(mm)
pred_K <- predict(mm, newdata = list(ndvi_twelve_months_nnas = ndvi_twelve_months))
r <- rep(0.1, n)
K <- 35
n <- length(count)
X <- numeric(n)
X[1] <- count[1]
for (i in 2:(n)) {
X[i] <- X[i-1] * exp(pred_r[i-1] * (1 - (X[i - 1] / pred_K[i-1])))
}
plot(start_moon:558, count, type ='l')
points(217:558, X, type = 'l', col = 2)
plot(X, count, ylim = c(0, 40), xlim = c(0, 40))
n <- length(count)
X <- numeric(n)
X[1] <- count[1]
for (i in 2:(n)) {
X[i] <- count[i-1] * exp(pred_r[i-1] * (1 - (count[i - 1] / pred_K[i-1])))
}
log_past_count <- log(past_count + 0.1)
mean_log_past_count <- mean(log_past_count)
sd_log_past_count <- max(c(sd(log_past_count), 0.01))
var_log_past_count <- sd_log_past_count^2
precision_log_past_count <- 1/(var_log_past_count)
log_bonus_max_past_count <- max(log(past_count * 1.2))
diff_count[1] <- log_past_count[2] - log_past_count[1]
diff_time[1] <- past_moon[2] - past_moon[1]
diff_log_past_count[1] <- diff_count[1] / diff_time[1]
for (i in 2:(past_N - 1)) {
diff_count[i] <- log_past_count[i + 1] - log_past_count[i]
diff_time[i] <- past_moon[i + 1] - past_moon[i]
diff_log_past_count[i] <- diff_count[i] / diff_time[i]
}
sd_diff_log_past_count <- max(c(sd(diff_log_past_count), 0.01))
var_diff_log_past_count <- sd_diff_log_past_count^2
precision_diff_log_past_count <- 1/(var_diff_log_past_count)
rate <- 0.1
shape <- precision_diff_log_past_count * rate
mu ~ dnorm(mean_log_past_count, precision_log_past_count);
tau ~ dgamma(shape, rate);
r ~ dnorm(0, 10);
log_K ~ dnorm(log_bonus_max_past_count, 1 / (0.1 * log_bonus_max_past_count))
K <- exp(log_K)
# initial state
X[1] <- mu;
pred_count[1] <- max(c(exp(X[1]) - 0.1, 0.00001));
count[1] ~ dpois(max(c(exp(X[1]) - 0.1, 0.00001)))
# through time
for (i in 2:N) {
# Process model
predX[i] <- X[i-1] * exp(r * (1 - (X[i - 1] / K)));
checkX[i] ~ dnorm(predX[i], tau);
X[i] <- min(c(checkX[i], log(ntraps[i] + 1)));
pred_count[i] <- max(c(exp(X[i]) - 0.1, 0.00001));
# observation model
count[i] ~ dpois(max(c(exp(X[i]) - 0.1, 0.00001)))
}
i <- 1
s <- species[i]
ss <- gsub("NA.", "NA", s)
messageq(" -", ss, quiet = !verbose)
moon_in <- which(rodents_table$newmoonnumber >= start_moon & rodents_table$newmoonnumber <= end_moon)
past_moon_in <- which(rodents_table$newmoonnumber < start_moon)
moon <- rodents_table[moon_in, "newmoonnumber"]
moon <- c(moon, metadata$time$rodent_cast_moons)
past_moon <- rodents_table[past_moon_in, "newmoonnumber"]
ntraps <- rodents_table[moon_in, "ntraps"]
na_traps <- which(is.na(ntraps) == TRUE)
ntraps[na_traps] <- 0
cast_ntraps <- rep(max(ntraps), true_count_lead)
ntraps <- c(ntraps, cast_ntraps)
past_ntraps <- rodents_table[past_moon_in, "ntraps"]
species_in <- which(colnames(rodents_table) == s)
count <- rodents_table[moon_in, species_in]
cast_count <- rep(NA, true_count_lead)
count <- c(count, cast_count)
past_count <- rodents_table[past_moon_in, species_in]
no_count <- which(is.na(past_count) == TRUE)
if (length(no_count) > 0) {
past_moon <- past_moon[-no_count]
past_count <- past_count[-no_count]
past_ntraps <- past_ntraps[-no_count]
}
weather <- weather(level = "newmoon", fill = TRUE, horizon = 90, path = file.path(main, settings$subs$resources))
ndvi <- ndvi(level = "newmoon", fill = TRUE, path = file.path(main, settings$subs$resources))
warm_rain_three_months <- weather$warm_precip
ndvi_twelve_months <- filter(ndvi$ndvi, rep(1, 12), sides = 1)
past_warm_rain_three_months <- warm_rain_three_months[weather$newmoonnumber < start_moon]
past_ndvi_twelve_months <- ndvi_twelve_months[ndvi$newmoonnumber < start_moon]
warm_rain_three_months <- warm_rain_three_months[weather$newmoonnumber >= start_moon & weather$newmoonnumber <= 558]
ndvi_twelve_months <- ndvi_twelve_months[ndvi$newmoonnumber >= start_moon & ndvi$newmoonnumber <= 558]
plot(warm_rain_three_months, count)
names(data)
past_count
count
data <- list(count = count,
ntraps = ntraps,
N = length(count),
moon = moon,
past_moon = past_moon,
past_count = past_count,
past_ntraps = past_ntraps,
past_N = length(past_count))
monitor <- c("mu", "tau", "r", "K")
past_N <- data$past_N
past_count <- data$past_count
past_moon <- data$past_moon
log_past_count <- log(past_count + 0.1)
mean_log_past_count <- mean(log_past_count)
sd_log_past_count <- max(c(sd(log_past_count) * sqrt(2), 0.01))
diff_log_past_count <- rep(NA, past_N - 1)
log_bonus_max_past_count <- max(log(past_count * 1.2))
for (i in 1:(past_N - 1)) {
diff_count <- log_past_count[i + 1] - log_past_count[i]
diff_time <- past_moon[i + 1] - past_moon[i]
diff_log_past_count[i] <- diff_count / diff_time
}
sd_diff_log_past_count <- max(c(sd(diff_log_past_count) * sqrt(2), 0.01))
var_diff_log_past_count <- sd_diff_log_past_count^2
precision_diff_log_past_count <- 1/(var_diff_log_past_count)
rate <- 0.1
shape <- precision_diff_log_past_count * rate
function(chain = chain){
list(.RNG.name = sample(rngs, 1),
.RNG.seed = sample(1:1e+06, 1),
mu = rnorm(1, mean_log_past_count, sd_log_past_count),
tau = rgamma(1, shape = shape, rate = rate),
r = rnorm(1, 0, sqrt(1/10)),
log_K = log(rnorm(1, log_bonus_max_past_count, 0.1 * sqrt(log_bonus_max_past_count) )))
}
}
jags_model <- "model {
# priors
log_past_count <- log(past_count + 0.1)
mean_log_past_count <- mean(log_past_count)
sd_log_past_count <- max(c(sd(log_past_count) * sqrt(2), 0.01))
var_log_past_count <- sd_log_past_count^2
precision_log_past_count <- 1/(var_log_past_count)
log_bonus_max_past_count <- max(log(past_count * 1.2))
diff_count[1] <- log_past_count[2] - log_past_count[1]
diff_time[1] <- past_moon[2] - past_moon[1]
diff_log_past_count[1] <- diff_count[1] / diff_time[1]
for (i in 2:(past_N - 1)) {
diff_count[i] <- log_past_count[i + 1] - log_past_count[i]
diff_time[i] <- past_moon[i + 1] - past_moon[i]
diff_log_past_count[i] <- diff_count[i] / diff_time[i]
}
sd_diff_log_past_count <- max(c(sd(diff_log_past_count) * sqrt(2), 0.01))
var_diff_log_past_count <- sd_diff_log_past_count^2
precision_diff_log_past_count <- 1/(var_diff_log_past_count)
rate <- 0.1
shape <- precision_diff_log_past_count * rate
mu ~ dnorm(mean_log_past_count, precision_log_past_count);
tau ~ dgamma(shape, rate);
r ~ dnorm(0, 10);
log_K ~ dnorm(log_bonus_max_past_count, 1 / (0.1 * log_bonus_max_past_count))
K <- exp(log_K)
# initial state
X[1] <- mu;
pred_count[1] <- max(c(exp(X[1]) - 0.1, 0.00001));
count[1] ~ dpois(max(c(exp(X[1]) - 0.1, 0.00001))) T(0, ntraps[1]);
# through time
for (i in 2:N) {
# Process model
predX[i] <- X[i-1] * exp(r * (1 - (X[i - 1] / K)));
checkX[i] ~ dnorm(predX[i], tau);
X[i] <- min(c(checkX[i], log(ntraps[i] + 1)));
pred_count[i] <- max(c(exp(X[i]) - 0.1, 0.00001));
# observation model
count[i] ~ dpois(max(c(exp(X[i]) - 0.1, 0.00001))) T(0, ntraps[i]);
}
}"