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03_fit_one_ssr_final.R
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# 03_fit_one_ssr_final.R
# Author: Benjamin R. Goldstein
# Date: 2/23/2022
### Main and helper functions for model fitting.
# This file contains a many functions used in the iterative fitting of
# the models. The main function called by Script 4 is fit_one_ssr(), at
# the bottom of the script, which executes forward AIC on all 6 model
# types for a specified species-subregion dataset.
if (packageVersion("nimbleEcology") < "0.4.0") {
warning(paste0("In script 03: The version of nimbleEcology you're using doesn't have the N-mixture",
" extensions we need. Installing development version from GitHub."))
devtools::install_github(repo = "nimble-dev/nimbleEcology", ref = "dNmixture_exts")
}
library(furrr)
library(glmmTMB)
library(lme4)
library(tidyverse)
library(nimbleEcology)
library(unmarked)
library(parallel)
library(numDeriv)
source("model_code.R")
# Create a data frame giving the data for a species and subregion.
make_dat_df <- function(species, subregion, include_ll = FALSE) {
# Gather data
this_obs <- ca_obs %>% filter(center == subregion,
name_clean == species)
this_cl <- ca_checklists %>%
filter(center == subregion) %>%
left_join(this_obs[, c("SAMPLING.EVENT.IDENTIFIER", "total_count")],
by = "SAMPLING.EVENT.IDENTIFIER") %>%
mutate(total_count = ifelse(!is.na(total_count), total_count, 0))
loc_ids <- this_cl %>%
count(gx, gy) %>%
arrange(-n) %>%
mutate(locID = row_number())
this_cl <- this_cl %>% left_join(
loc_ids[, c("gx", "gy", "locID")],
by = c("gx", "gy")
) %>%
arrange(locID)
if (include_ll) {
dat_df <- this_cl %>%
mutate(#lon = scale(LONGITUDE),
#lat = scale(LATITUDE),
yday = scale(lubridate::yday(OBSERVATION.DATE)),
tod = scale(as.numeric(TIME.OBSERVATIONS.STARTED)),
duration = scale(DURATION.MINUTES),
# distance = scale(EFFORT.DISTANCE.KM),
protocol = as.factor(PROTOCOL.CODE),
num_observers = scale(NUMBER.OBSERVERS),
year = as.factor(year),
elevation = scale(elevation),
# precip = scale(precip),
# tmax = scale(tmax),
pct_veg_comb = scale(pct_veg_comb),
pct_veg = scale(pct_veg),
pct_water = scale(pct_water),
pct_urban = scale(pct_urban),
pct_ag = scale(pct_ag),
pct_tree = scale(pct_tree)
) %>%
mutate(tod_sq = tod^2,
yday_sq = yday^2) %>%
select(total_count, locID, yday, yday_sq, tod, tod_sq, duration,
protocol, num_observers, year, elevation,
pct_veg, pct_water, pct_tree, pct_ag, gx, gy)
} else {
dat_df <- this_cl %>%
mutate(#lon = scale(LONGITUDE),
#lat = scale(LATITUDE),
yday = scale(lubridate::yday(OBSERVATION.DATE)),
tod = scale(as.numeric(TIME.OBSERVATIONS.STARTED)),
duration = scale(DURATION.MINUTES),
# distance = scale(EFFORT.DISTANCE.KM),
protocol = as.factor(PROTOCOL.CODE),
num_observers = scale(NUMBER.OBSERVERS),
year = as.factor(year),
elevation = scale(elevation),
# precip = scale(precip),
# tmax = scale(tmax),
pct_veg_comb = scale(pct_veg_comb),
pct_veg = scale(pct_veg),
pct_water = scale(pct_water),
pct_urban = scale(pct_urban),
pct_ag = scale(pct_ag),
pct_tree = scale(pct_tree)
) %>%
mutate(tod_sq = tod^2,
yday_sq = yday^2) %>%
select(total_count, locID, yday, yday_sq, tod, tod_sq, duration,
protocol, num_observers, elevation,
pct_veg, pct_water, pct_tree, pct_ag)
}
dat_df
}
# Convert a column in a dataframe to wide form (each row = 1 location). Used
# for refitting in unmarked
get_var_wide <- function(df, col) {
df <- df[, c("locID", col)]
colnames(df)[2] <- "target_col"
df <- df %>%
group_by(locID) %>%
mutate(id = paste0("X", str_pad(1:n(), 3, pad = "0"))) %>%
spread(key = id, value = target_col) %>%
arrange(locID) %>%
ungroup() %>%
dplyr::select(-locID) %>%
as.matrix()
}
# Get AIC from nimble optim object
AIC_optim <- function(optim_fit) {
if (!is.list(optim_fit)) return(Inf)
return(2 * length(optim_fit$par) - 2 * optim_fit$value)
}
# Get AIC from a fit unmarked model
AIC_unmarked <- function(umfit) {
SEs <- c(summary(best_fit)$det$`Std. Error`,
summary(best_fit)$state$`Std. Error`)
if (any(is.na(SEs)) || any(is.nan(SEs))) return(Inf)
return(umfit@AIC)
}
# Get AIC from a (potentially) fit glmmTMB object
AIC_tmb <- function(tmb_fit) {
if (!is.list(tmb_fit)) return(Inf) # catch NAs
if (is.na(AIC(tmb_fit))) return(Inf)
AIC(tmb_fit)
}
# Make a GLMM formula from a row of a model specification matrix
glmm_formula_from_row <- function(mrow, accept_det_covs, accept_abd_covs) {
names(mrow)[names(mrow) == "tod*tod"] <- "tod_sq"
names(mrow)[names(mrow) == "yday*yday"] <- "yday_sq"
if (sum(mrow) > 0) {
f <- paste0("total_count ~ ", paste(names(mrow)[which(mrow == 1)], collapse = " + "),
paste0(" + ", paste0(accept_det_covs, collapse = "+"), " + ",
paste0(accept_abd_covs, collapse = "+"), " + (1|locID)"))
} else f <- paste0("total_count ~ ", paste0(accept_det_covs, collapse = "+"), " + ",
paste0(accept_abd_covs, collapse = "+"), " + (1|locID)")
as.formula(f)
}
# Make an unmarked formula from a row of a model specification matrix
glmm_formula_from_row <- function(mrow, accept_det_covs, accept_abd_covs) {
names(mrow)[names(mrow) == "tod*tod"] <- "tod_sq"
names(mrow)[names(mrow) == "yday*yday"] <- "yday_sq"
if (sum(mrow) > 0) {
f <- paste0("total_count ~ ", paste(names(mrow)[which(mrow == 1)], collapse = " + "),
paste0(" + ", paste0(accept_det_covs, collapse = "+"), " + ",
paste0(accept_abd_covs, collapse = "+"), " + (1|locID)"))
} else f <- paste0("total_count ~ ", paste0(accept_det_covs, collapse = "+"), " + ",
paste0(accept_abd_covs, collapse = "+"), " + (1|locID)")
as.formula(f)
}
# Turn a row from a model specification matrix into a set of NIMBLE model nodes
# for optimizing a specific set of covariates
row_to_nodes <- function(this_row, mixture, dat_df,
accept_det_names, accept_abd_names) {
terms <- names(this_row)
int_inds <- which(grepl("\\*", terms))
for (i in int_inds) {
if (this_row[i] == 1) {
sp <- unlist(str_split(terms[i], "-"))
type <- sp[2]
first_orders <- unlist(str_split(sp[1], "\\*"))
this_row[grepl(first_orders[1], terms) &
grepl(type, terms) & !grepl("\\*", terms)] <- 1
this_row[grepl(first_orders[2], terms) &
grepl(type, terms) & !grepl("\\*", terms)] <- 1
}
}
abd_terms <- terms[grepl("abd", terms)]
det_terms <- terms[grepl("det", terms)]
intercepts <- c("p_coeffs[1]", "abund_coeffs[1]")
this_target_nodes <-
c(intercepts, # Always intercepts
if (mixture %in% c("B-NB", "BB-NB")) "ltheta",
if (mixture %in% c("BB-P", "BB-NB")) "log_s",
paste0("p_coeffs[", 2:(1 + length(accept_det_names)), "]"), # accept det covariates
paste0("abund_coeffs[", 2:(1 + length(accept_abd_names)), "]"), # accept abd covariates
paste0("abund_coeffs[", (2 + length(accept_abd_names)):(length(abd_terms) + length(accept_abd_names) + 1), "]")[
which(this_row[names(this_row) %in% abd_terms] == 1)
],
paste0("p_coeffs[", (2 + length(accept_det_names)):(length(det_terms) + 1 + length(accept_det_names)), "]")[
which(this_row[names(this_row) %in% det_terms] == 1)
])
return(this_target_nodes)
}
# A version of nimble::setAndCalculate that returns large negative number if NaN
# is produced so that optimization doesn't break
noNanSetAndCalculate <- nimbleFunction(
name = 'noNanSetAndCalculate',
setup = function(model, targetNodes) {
targetNodesAsScalar <- model$expandNodeNames(targetNodes, returnScalarComponents = TRUE)
calcNodes <- model$getDependencies(targetNodes)
},
run = function(targetValues = double(1)) {
values(model, targetNodesAsScalar) <<- targetValues
lp <- calculate(model, calcNodes)
returnType(double())
if (!is.nan(lp) & lp != -Inf) return(lp)
else return(-1e6)
}
)
# Function to optimize a compiled N-mixture model. Takes arguments to set
# all parameters to 0, not just the ones being optimized, to "reset" the model
Cnmix_model_optim <- function(Cnmix_model, nmix_model, this_target_nodes,
all_zero_nodes, zero_assignments) {
nimble::values(nmix_model, all_zero_nodes) <- zero_assignments
nimble::values(Cnmix_model, all_zero_nodes) <- zero_assignments
sc <- noNanSetAndCalculate(model = nmix_model,
targetNodes = this_target_nodes)
Csc <- suppressMessages(compileNimble(sc))
fitct <- 0
lower <- rep(-Inf, length(this_target_nodes))
lower[this_target_nodes == "p_coeffs[1]"] <- logit(0.001)
lower[this_target_nodes %in% c("ltheta", "log_s")] <- -10
upper <- rep(Inf, length(this_target_nodes))
# upper[this_target_nodes == "p_coeffs[1]"] <- logit(0.999)
upper[this_target_nodes %in% c("ltheta", "log_s")] <- 10
while (fitct < 5) {
fitct <- fitct + 1
this_fit <- tryCatch(
optim(nimble::values(Cnmix_model, this_target_nodes),
Csc$run, hessian = TRUE, method = "L-BFGS-B",
lower = lower, upper = upper,
control = list(fnscale = -1, maxit = 100000, trace = 0)),
error = function(err) {
return(NA)
}
)
if (is.list(this_fit)) {
fitct <- Inf
} else {
nimble::values(Cnmix_model, all_zero_nodes) <- zero_assignments
nimble::values(nmix_model, all_zero_nodes) <- zero_assignments
nimble::values(Cnmix_model, this_target_nodes) <-
nimble::values(Cnmix_model, this_target_nodes) +
rnorm(n = length(this_target_nodes), sd = 0.5)
}
}
if (!is.na(this_fit) &&
!any(this_fit$par[this_target_nodes %in% c("ltheta", "log_s")] == -10)) {
tryCatch(
this_fit <- optim(this_fit$par,
Csc$run, hessian = TRUE, method = "L-BFGS-B",
lower = lower, upper = upper,
control = list(fnscale = -1, maxit = 100000, trace = 0)),
error = function(e) {NA}
)
}
return(this_fit)
}
# setAndCalculate reparameterized for use in uncertainty quantification
scReparam <- function(x, Csc, two_ints = TRUE, logit_p = NA,
ltheta = NULL, log_s = NULL,
this_target_nodes) {
if (two_ints) {
par <- x
par[1] <- logit(exp( (x[1] + x[2]) / 2 ))
par[2] <- (x[1] - x[2]) / 2
} else {
par <- c(NA, x)
par[1] <- logit_p # logit(p)
par[2] <- x[1] - log(expit(logit_p)) # log(p*lam) - log(p)
}
# Add fixed ltheta and log_s back in to par, if provided
if (!is.null(ltheta)) {
par <- c(par[1:2], ltheta, par[3:length(par)])
}
if (!is.null(log_s)) {
if ("ltheta" %in% this_target_nodes) {
par <- c(par[1:3], log_s, par[4:length(par)])
} else {
par <- c(par[1:2], log_s, par[3:length(par)])
}
}
Csc$run(par)
}
# Get standard errors for parameters from fitted Cnmix_model object
Cnmix_model_SEs <- function(Cnmix_model, nmix_model,
this_target_nodes, fit_values,
all_zero_nodes, zero_assignments) {
nimble::values(Cnmix_model, all_zero_nodes) <- zero_assignments
nimble::values(nmix_model, all_zero_nodes) <- zero_assignments
sc <- noNanSetAndCalculate(model = nmix_model,
targetNodes = this_target_nodes)
Csc <- suppressMessages(compileNimble(sc))
logit_p <- NULL
two_ints <- TRUE
ltheta <- NULL
log_s <- NULL
if (expit(fit_values[1]) >= 0.00125) {
x <- fit_values
x[1] <- log(expit(fit_values[1])) + fit_values[2] # log(p) + log(lam)
x[2] <- log(expit(fit_values[1])) - fit_values[2] # log(p) - log(lam)
} else {
x <- fit_values
x[1] <- log(expit(fit_values[1])) + fit_values[2] # log(p) + log(lam)
x <- x[-2]
logit_p <- fit_values[1] # store logit p
two_ints <- F
}
# If ltheta was estimated at boundary, give it a fixed value
if ("ltheta" %in% this_target_nodes &&
fit_values[this_target_nodes == "ltheta"] %in% c(10, -10)) {
ltheta <- fit_values[this_target_nodes == "ltheta"]
x <- x[-which(this_target_nodes == "ltheta")]
}
# same for log_s
if ("log_s" %in% this_target_nodes &&
fit_values[this_target_nodes == "log_s"] %in% c(10, -10)) {
log_s <- fit_values[this_target_nodes == "log_s"]
x <- x[-which(this_target_nodes == "log_s")]
}
hess <- -hessian(scReparam, x, Csc = Csc,
two_ints = two_ints,
logit_p = logit_p,
ltheta = ltheta,
log_s = log_s,
this_target_nodes = this_target_nodes)
SEs <- sqrt(diag(solve(hess)))
if (!is.null(logit_p)) {
SEs <- c(SEs[1], NA, SEs[2:length(SEs)])
}
if (!is.null(ltheta)) {
SEs <- c(SEs[1:2], NA, SEs[3:length(SEs)])
}
if (!is.null(log_s)) {
if ("ltheta" %in% this_target_nodes) {
SEs <- c(SEs[1:3], NA, SEs[4:length(SEs)])
} else {
SEs <- c(SEs[1:2], NA, SEs[3:length(SEs)])
}
}
return(SEs)
}
# Run forward AIC for GLMMs.
# dat_df: a data frame as returned by make_dat_df
# covs_tbl: describes which parameters in dat_df are being considered for fwd AIC
# fam: distribution for GLMM (nbinom2 or poisson)
# modelset should be rows
GLMM_fwd_AIC <- function(dat_df, covs_tbl, fam,
species = NA, subregion = NA,
verbose = F, parallelize = TRUE) {
start_time <- Sys.time()
accept_det_names <- clean_vec_names(c("duration", "num_observers"), dat_df)
accept_abd_names <- c("elevation")
models_tried <- as.data.frame(matrix(
0, ncol = nrow(covs_tbl), nrow = 1
))
colnames(models_tried) <- covs_tbl$param
null_formula <- glmm_formula_from_row(models_tried[1,1:nrow(covs_tbl)],
accept_det_names, accept_abd_names)
models_tried$AIC <- AIC_tmb(glmmTMB(null_formula, data = dat_df, family = fam))
best_row <- models_tried[1,1:nrow(covs_tbl)]
best_index <- 1
done <- FALSE
nmodel <- 0
# First order terms loop
while (!done) {
last_best <- best_index
covs_to_test <- which(!covs_tbl$accepted & !covs_tbl$interaction)
if (length(covs_to_test) > 0) {
if (parallelize) {
plan(multicore)
parAICs <- future_map_dbl(covs_to_test, function(x) {
this_row <- best_row
this_row[x] <- 1
this_formula <- glmm_formula_from_row(this_row, accept_det_names,
accept_abd_names)
this_fit <- glmmTMB(this_formula, data = dat_df, family = fam)
return(AIC_tmb(this_fit))
}) %>% unlist()
} else {
parAICs <- lapply(covs_to_test, function(x) {
nmodel <<- nmodel + 1
if (verbose) cat("\nModel ", nmodel)
this_row <- best_row
this_row[x] <- 1
this_formula <- glmm_formula_from_row(this_row, accept_det_names,
accept_abd_names)
this_fit <- glmmTMB(this_formula, data = dat_df, family = fam)
return(AIC_tmb(this_fit))
}) %>% unlist()
}
for (i in 1:length(parAICs)) {
this_row <- best_row
this_row[covs_to_test[i]] <- 1
this_row$AIC <- parAICs[i]
models_tried <- rbind(models_tried, this_row)
}
best_index <- which.min(models_tried$AIC)
if (best_index == last_best) {
done <- TRUE
} else {
best_row <- models_tried[best_index, 1:nrow(covs_tbl)]
covs_tbl$accepted <- as.logical(best_row)
}
} else {
done <- TRUE
}
}
done <- FALSE
# Second order terms loop
while (!done) {
last_best <- best_index
covs_to_test <- which(!covs_tbl$accepted & covs_tbl$interaction)
if (length(covs_to_test) > 0) {
if (parallelize) {
plan(multicore)
parAICs <- future_map_dbl(covs_to_test, function(x) {
this_row <- best_row
this_row[x] <- 1
this_formula <- glmm_formula_from_row(this_row, accept_det_names,
accept_abd_names)
this_fit <- glmmTMB(this_formula, data = dat_df, family = fam)
return(AIC_tmb(this_fit))
}) %>% unlist()
} else {
parAICs <- lapply(covs_to_test, function(x) {
nmodel <<- nmodel + 1
if (verbose) cat("\nModel ", nmodel)
this_row <- best_row
this_row[x] <- 1
this_formula <- glmm_formula_from_row(this_row, accept_det_names,
accept_abd_names)
this_fit <- glmmTMB(this_formula, data = dat_df, family = fam)
return(AIC_tmb(this_fit))
}) %>% unlist()
}
for (i in 1:length(parAICs)) {
this_row <- best_row
this_row[covs_to_test[i]] <- 1
this_row$AIC <- parAICs[i]
models_tried <- rbind(models_tried, this_row)
}
best_index <- which.min(models_tried$AIC)
if (best_index == last_best) {
done <- TRUE
} else {
best_row <- models_tried[best_index, 1:nrow(covs_tbl)]
covs_tbl$accepted <- as.logical(best_row)
}
} else {
done <- TRUE
}
}
best_fit <- glmmTMB(glmm_formula_from_row(best_row, accept_det_names, accept_abd_names),
data = dat_df, family = fam)
coeffs_df <- summary(best_fit)$coefficients$cond %>% as.data.frame() %>%
mutate(param = rownames(summary(best_fit)$coefficients$cond)) %>%
mutate(est = Estimate, se = `Std. Error`) %>%
select(param, est, se)
rownames(coeffs_df) <- NULL
if (identical(fam, nbinom1) || identical(fam, nbinom2)) {
sigma <- summary(best_fit)$sigma
} else {
sigma <- NA
}
end_time <- Sys.time()
notes <- list()
best_model_rtn_info <- list(
species = species,
subregion = subregion,
fam = fam,
AIC = AIC_tmb(best_fit),
models_tried = models_tried,
fit = best_fit,
best_row = best_row,
coefficients = coeffs_df,
dispersion_param = sigma,
var_locID = summary(best_fit)$varcor$cond$locID[[1]],
time_taken = start_time - end_time,
notes = notes
)
best_model_rtn_info
}
# Function to produce a NIMBLE model for the appropriate N-mixture, relevant data
make_nmix_model <- function(dat_df, vars, mixture, K) {
accept_det_names <- clean_vec_names(c("duration", "num_observers"), dat_df)
accept_abd_names <- c("elevation")
det_var_final_order <- c(accept_det_names, gsub("-det", "", vars[grepl("-det", vars)]))
abd_var_final_order <- c(accept_abd_names, gsub("-abd", "", vars[grepl("-abd", vars)]))
index_start <- numeric(max(dat_df$locID))
index_end <- numeric(max(dat_df$locID))
index_start[1] <- 1
index_end[max(dat_df$locID)] <- nrow(dat_df)
current_site <- 1
for (i in 1:nrow(dat_df)) {
this_site <- dat_df$locID[i]
if (this_site != current_site) {
index_end[current_site] <- i - 1
index_start[this_site] <- i
current_site <- this_site
}
}
nSite1Obs <- sum(index_start == index_end)
# Make the detection data matrix incl. interaction columns
det_1st_mtx <- dat_df[, c(accept_det_names,
gsub("-det", "",
vars[grepl("det", vars) & !grepl("\\*", vars)]))]
abd_1st_mtx <- dat_df[, c(accept_abd_names,
gsub("-abd", "",
vars[grepl("abd", vars) & !grepl("\\*", vars)]))]
int_names <- vars[grepl("det", vars) & grepl("\\*", vars)] %>%
gsub(pattern="-det", replacement="")
int_col_list <- list()
if (length(int_names) > 0 ) {
for (i in 1:length(int_names)) {
depends <- strsplit(int_names[i], "\\*")[[1]]
int_col_list[[i]] <-
dat_df[, depends[1]] * dat_df[, depends[2]]
}
int_mtx <- do.call(cbind, int_col_list)
colnames(int_mtx) <- int_names
p_dat <- cbind(det_1st_mtx, int_mtx)
} else {
p_dat <- det_1st_mtx
}
abund_dat <- abd_1st_mtx
p_dat <- p_dat[, det_var_final_order]
abund_dat <- abund_dat[, abd_var_final_order]
use_code <- switch(mixture,
`B-P` = BP_nmix_code_ebd,
`B-NB` = BNB_nmix_code_ebd,
`BB-P` = BBP_nmix_code_ebd,
`BB-NB` = BBNB_nmix_code_ebd)
nmix_model <- nimbleModel(
code = use_code,
data = list(abund_dat = abund_dat,
p_dat = p_dat,
K = K,
y = as.numeric(dat_df$total_count)),
constants = list(
nobs = nrow(dat_df),
npcov = ncol(p_dat) + 1,
nlcov = ncol(abund_dat) + 1,
nsite = length(index_start),
index_start = index_start,
index_end = index_end,
nSite1Obs = nSite1Obs),
inits = list(
p_coeffs = c(logit(0.1), rep(0, ncol(p_dat))),
abund_coeffs =
c(log(max(dat_df$total_count)),
rep(0, ncol(abund_dat))),
ltheta = 0, log_s = 0
), calculate = FALSE)
Cnmix_model <- compileNimble(nmix_model)
return(list(
nmix_model = nmix_model,
Cnmix_model = Cnmix_model
))
}
# This function takes the standard parameters that are a priori accepted
# and makes sure they're not too correlated. If two are, the latter one is
# dropped. This is more of an issue for the automatically accepted ones because
# otherwise it'd largely be taken care of by stepwise AIC.
clean_vec_names <- function(accept_names, dat_df) {
combos <- as.data.frame(t(combn(accept_names, 2)))
dropped <- data.frame(param = accept_names, dropped = F)
for (i in 1:nrow(combos)) {
if (!dropped$dropped[dropped$param == combos$V1[i]] &&
!dropped$dropped[dropped$param == combos$V2[i]]) {
if (abs(cor(dat_df[, combos$V1[i]], dat_df[, combos$V2[i]])) > 0.9) {
dropped$dropped[dropped$param == combos$V2[i]] <- TRUE
}
}
}
return(as.character(dropped$param[!dropped$dropped]))
}
# Run forward AIC for N-mixture models.
Nmix_fwd_AIC <- function(dat_df, covs_tbl, mixture, K, species = NA,
subregion = NA, verbose = F, nCores, clearComp = T) {
on.exit({
if (exists("in.cl")) stopCluster(in.cl)
})
start_time <- Sys.time()
models_tried <- as.data.frame(matrix(
0, ncol = nrow(covs_tbl), nrow = 1
))
colnames(models_tried) <- paste(covs_tbl$param, covs_tbl$type, sep = "-")
models_tried$AIC <- NA
accept_det_names <- clean_vec_names(c("duration", "num_observers"), dat_df)
accept_abd_names <- c("elevation") #clean_vec_names(c("elevation"), dat_df)
if (nCores > 0) {
cluster.env <- new.env()
cluster.env$dat_df <- dat_df
cluster.env$mixture <- mixture
cluster.env$models_tried <- models_tried
cluster.env$K <- K
cluster.env$accept_det_names <- accept_det_names
cluster.env$accept_abd_names <- accept_abd_names
cluster.env$verbose <- verbose
in.cl <- makeCluster(nCores)
capture <- clusterEvalQ(in.cl, source("03_fit_one_ssr_final.R"))
clusterExport(in.cl,
varlist = list("dat_df", "models_tried", "mixture", "K",
"accept_det_names", "accept_abd_names", "verbose"),
envir = cluster.env)
}
start_time <- Sys.time()
if (!(mixture %in% c("B-P", "B-NB", "BB-P", "BB-NB"))) {
stop("Unrecognized mixture ", mixture, ".")
}
# Make the model object
if (nCores > 0) {
capture <- clusterEvalQ(in.cl, {
modellist <-
make_nmix_model(dat_df, vars = colnames(models_tried)[-ncol(models_tried)],
mixture, K)
nmix_model <- modellist$nmix_model
Cnmix_model <- modellist$Cnmix_model
Cnmix_model$calculate()
all_zero_nodes <- c(
nmix_model$expandNodeNames("abund_coeffs"),
nmix_model$expandNodeNames("p_coeffs"),
if (mixture %in% c("B-NB", "BB-NB")) "ltheta",
if (mixture %in% c("BB-P", "BB-NB")) "log_s"
)
zero_assignments <- nimble::values(Cnmix_model, all_zero_nodes)
})
} else {
modellist <-
make_nmix_model(dat_df, vars = colnames(models_tried)[-ncol(models_tried)],
mixture, K)
nmix_model <- modellist$nmix_model
Cnmix_model <- modellist$Cnmix_model
Cnmix_model$calculate()
all_zero_nodes <- c(
nmix_model$expandNodeNames("abund_coeffs"),
nmix_model$expandNodeNames("p_coeffs"),
if (mixture %in% c("B-NB", "BB-NB")) "ltheta",
if (mixture %in% c("BB-P", "BB-NB")) "log_s"
)
zero_assignments <- nimble::values(Cnmix_model, all_zero_nodes)
}
if (verbose) cat("Beginning selection loop.")
models_tried[1,] <- c(rep(0, nrow(covs_tbl)),
NA)
null_nodes <- row_to_nodes(models_tried[1,1:nrow(covs_tbl)],
mixture = mixture, dat_df = dat_df,
accept_det_names, accept_abd_names)
if (nCores > 0) {
null_fit <- parLapply(in.cl, list(null_nodes), function(x) {
Cnmix_model_optim(Cnmix_model, nmix_model,
x, all_zero_nodes,
zero_assignments)
})[[1]]
} else {
null_fit <- Cnmix_model_optim(Cnmix_model, nmix_model,
null_nodes, all_zero_nodes,
zero_assignments)
}
models_tried$AIC[1] <- AIC_optim(null_fit)
best_row <- models_tried[1,]
best_fit <- null_fit
done <- FALSE
interactions <- FALSE
ct <- 1
round <- 0
while (!done) {
round <- round + 1
last_best <- best_row
covs_to_test <- covs_tbl %>%
filter(!accepted) %>%
filter(grepl(pattern = "\\*", x = param) == interactions)
if (nrow(covs_to_test) == 0) {
if (interactions) done <- TRUE
else interactions <- TRUE
} else {
to_test_list <- split(covs_to_test, seq(nrow(covs_to_test)))
for (i in 1:length(to_test_list)) {
ct <- ct + 1
models_tried[ct,] <- c(as.numeric(
covs_tbl$accepted |
(covs_tbl$param == to_test_list[[i]]$param &
covs_tbl$type == to_test_list[[i]]$type)),
NA)
}
if (nCores > 0) {
models_tried$AIC[(ct - length(to_test_list) + 1):ct] <-
parLapply(cl = in.cl, X = to_test_list, fun = function(x) {
if (verbose) cat(paste0("\nTesting round ", round, " add ", x$param))
this_row <- c(as.numeric(
covs_tbl$accepted |
covs_tbl$param == x$param), NA)
names(this_row) <-
c(paste(covs_tbl$param, covs_tbl$type, sep = "-"), "AIC")
this_nodes <- row_to_nodes(this_row, mixture = mixture,
dat_df = dat_df,
accept_det_names, accept_abd_names)
this_fit <- Cnmix_model_optim(Cnmix_model, nmix_model, this_nodes,
all_zero_nodes, zero_assignments)
AIC_optim(this_fit)
}) %>% unlist()
} else {
models_tried$AIC[(ct - length(to_test_list) + 1):ct] <-
lapply(to_test_list, function(x) {
if (verbose) cat(paste0("\nTesting round ", round, " add ", x$param, " ", x$type))
this_row <- c(as.numeric(
covs_tbl$accepted |
covs_tbl$param == x$param), NA)
names(this_row) <-
c(paste(covs_tbl$param, covs_tbl$type, sep = "-"), "AIC")
this_nodes <- row_to_nodes(this_row, mixture = mixture,
dat_df = dat_df,
accept_det_names, accept_abd_names)
this_fit <- Cnmix_model_optim(Cnmix_model, nmix_model, this_nodes,
all_zero_nodes, zero_assignments)
AIC_optim(this_fit)
}) %>% unlist()
}
best_row <- models_tried[which.min(models_tried$AIC),]
if (last_best$AIC == best_row$AIC) {
if (interactions) done <- TRUE
else interactions <- TRUE
} else {
covs_tbl$accepted[as.logical(best_row[1:nrow(covs_tbl)])] <- TRUE
if (verbose) {
cat(paste0("\nAccepted: ",
paste(covs_tbl$param[covs_tbl$accepted],
collapse = " "),
". Best AIC: ", best_row$AIC))
}
}
}
}
### Done with forward AIC
best_row <- models_tried[which.min(models_tried$AIC), ]
best_nodes <- row_to_nodes(best_row, mixture, dat_df,
accept_det_names, accept_abd_names)
if (nCores > 0) {
best_fit <- parLapply(in.cl, list(best_nodes), function(x) {
Cnmix_model_optim(Cnmix_model, nmix_model,
x, all_zero_nodes,
zero_assignments)
})[[1]]
} else {
best_fit <- Cnmix_model_optim(Cnmix_model, nmix_model,
best_nodes,
all_zero_nodes, zero_assignments)
}
terms <- names(best_row)
int_inds <- which(grepl("\\*", terms))
for (i in int_inds) {
if (best_row[i] == 1) {
sp <- unlist(str_split(terms[i], "-"))
type <- sp[2]
first_orders <- unlist(str_split(sp[1], "\\*"))
best_row[grepl(first_orders[1], terms) &
grepl(type, terms) & !grepl("\\*", terms)] <- 1
best_row[grepl(first_orders[2], terms) &
grepl(type, terms) & !grepl("\\*", terms)] <- 1
}
}
abd_terms <- terms[grepl("abd", terms)]
det_terms <- terms[grepl("det", terms)]
params_chosen <-
c("log(p * lambda)", "log(p / lambda)", # Always intercepts
if (mixture %in% c("B-NB", "BB-NB")) "ltheta",
if (mixture %in% c("BB-P", "BB-NB")) "log_s",
accept_det_names,
accept_abd_names,
abd_terms[
which(best_row[colnames(best_row) %in% abd_terms] == 1)
],
det_terms[
which(best_row[colnames(best_row) %in% det_terms] == 1)
])
notes <- list()
tryCatch({
if (nCores > 0) {
ses <- parLapply(in.cl, list(best_nodes), x2 = best_fit$par,
function(x, x2) {
Cnmix_model_SEs(Cnmix_model, nmix_model,
x, x2,
all_zero_nodes, zero_assignments)
})[[1]]
} else {
ses <- Cnmix_model_SEs(Cnmix_model, nmix_model,
best_nodes,
best_fit$par,
all_zero_nodes, zero_assignments)
}
}, error = function(err) {} )
if (!exists("ses")) {
ses <- rep(NA, length(params_chosen))
}
if (length(ses) == length(best_fit$par)) {
coeffs_df <- data.frame(
param = params_chosen,
est = c(log(expit(best_fit$par[1])) + best_fit$par[2],
log(expit(best_fit$par[1])) - best_fit$par[2],
best_fit$par[3:length(best_fit$par)]),
se = ses
)
} else if (length(ses) + 1 == length(best_fit$par)) {
coeffs_df <- data.frame(
param = params_chosen,
est = c(log(expit(best_fit$par[1])) + best_fit$par[2],
log(expit(best_fit$par[1])) - best_fit$par[2],
best_fit$par[3:length(best_fit$par)]),
se = c(ses[1], NA, ses[2:length(ses)])
)
}
end_time <- Sys.time()
best_model_rtn_info <- list(
species = species,
subregion = subregion,
mixture = mixture,
AIC = AIC_optim(best_fit),
models_tried = models_tried,
best_row = best_row,
fit = best_fit,
coefficients = coeffs_df,
K = K,
time_taken = start_time - end_time,
notes = notes
)
if (clearComp && nCores == 0) nimble:::clearCompiled(nmix_model)
best_model_rtn_info
}
# Run forward AIC for all six model flavors on the specified SSR.
fit_one_ssr <- function(subregion, species, checklists, obs, nCores,
overwrite = c("GLMM_Nbin", "GLMM_Pois",
"Nmix_BP", "Nmix_BNB",
"Nmix_BBP", "Nmix_BBNB"),
verbose = TRUE, glmm_only = FALSE,
path_prefix = "output/onemodel_oneyear/") {
if (!(paste0(subregion, species)) %in%
unique(paste0(obs$center, obs$name_clean))) {
warning("No data for target ssr ", subregion, " ", species, ". Skipping.")
return(NA)
}
dat_df <- make_dat_df(species, subregion)
both_covs <- c("pct_water", "pct_tree", "pct_veg", "pct_ag")
both_covs <- both_covs[unlist(map_lgl(both_covs, function(x) length(unique(unlist(dat_df[,x]))) > 1))]
det_only_covs <- c("yday", "yday*yday", "tod", "tod*tod")
nmix_covs_tbl <- data.frame(
param = c(both_covs, both_covs, det_only_covs),
accepted = F,
type = c(rep("abd", length(both_covs)),
rep("det", length(c(both_covs, det_only_covs)))),
interaction = grepl("\\*", c(both_covs, both_covs, det_only_covs))
)
glmm_covs_tbl <- nmix_covs_tbl %>%
select(-type) %>%
distinct()
if (!file.exists(paste0(path_prefix, "GLMM_Pois_",
subregion, "_", species, ".RDS")) ||
"GLMM_Pois" %in% overwrite) {
if (verbose) cat("\n\n===== Poisson GLMM", species, subregion, "=====")
glmm_pois_result <- GLMM_fwd_AIC(dat_df = dat_df,