R Under development (unstable) (2024-06-17 r86768 ucrt) -- "Unsuffered Consequences"
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> attach(asNamespace("epigrowthfit"))
> library(tools)
> options(warn = 2L, error = if (interactive()) recover)
> example("egf", package = "epigrowthfit"); o.1 <- m1; o.2 <- m2

egf> ## Simulate 'N' incidence time series exhibiting exponential growth
egf> set.seed(180149L)

egf> N <- 10L

egf> f <- function(time, r, c0) {
egf+     lambda <- diff(exp(log(c0) + r * time))
egf+     c(NA, rpois(lambda, lambda))
egf+ }

egf> time <- seq.int(0, 40, 1)

egf> r <- rlnorm(N, -3.2, 0.2)

egf> c0 <- rlnorm(N, 6, 0.2)

egf> data_ts <-
egf+     data.frame(country = gl(N, length(time), labels = LETTERS[1:N]),
egf+                time = rep.int(time, N),
egf+                x = unlist(Map(f, time = list(time), r = r, c0 = c0)))

egf> rm(f, time)

egf> ## Define fitting windows (here, two per time series)
egf> data_windows <-
egf+     data.frame(country = gl(N, 1L, 2L * N, labels = LETTERS[1:N]),
egf+                wave = gl(2L, 10L),
egf+                start = c(sample(seq.int(0, 5, 1), N, TRUE),
egf+                          sample(seq.int(20, 25, 1), N, TRUE)),
egf+                end = c(sample(seq.int(15, 20, 1), N, TRUE),
egf+                        sample(seq.int(35, 40, 1), N, TRUE)))

egf> ## Estimate the generative model
egf> m1 <-
egf+     egf(model = egf_model(curve = "exponential", family = "pois"),
egf+         formula_ts = cbind(time, x) ~ country,
egf+         formula_windows = cbind(start, end) ~ country,
egf+         formula_parameters = ~(1 | country:wave),
egf+         data_ts = data_ts,
egf+         data_windows = data_windows,
egf+         se = TRUE)
computing a Hessian matrix ...

egf> ## Re-estimate the generative model with:
egf> ## * Gaussian prior on beta[1L]
egf> ## * LKJ prior on all random effect covariance matrices
egf> ##   (here there happens to be just one)
egf> ## * initial value of 'theta' set explicitly
egf> ## * theta[3L] fixed at initial value
egf> m2 <-
egf+     update(m1,
egf+            formula_priors = list(beta[1L] ~ Normal(mu = -3, sigma = 1),
egf+                                  Sigma ~ LKJ(eta = 2)),
egf+            init = list(theta = c(log(0.5), log(0.5), 0)),
egf+            map = list(theta = 3L))
computing a Hessian matrix ...
> 
> 
> ## egf_has_random ######################################################
> 
> e <- new.env()
> e[["data"]] <- list()
> o <- list(tmb_out = list(env = e))
> class(o) <- "egf"
> 
> e[["data"]][["Z"]] <- matrix(0, 3L, 3L)
> stopifnot( egf_has_random(o))
> 
> e[["data"]][["Z"]] <- matrix(0, 3L, 0L)
> stopifnot(!egf_has_random(o))
> 
> 
> ## egf_has_converged ###################################################
> 
> o <- list(optimizer_out = list(convergence = 0L),
+           value = 100,
+           gradient = runif(10L, -0.5, 0.5),
+           hessian = TRUE)
> class(o) <- c("egf", "list") # "list" only for dispatch to within.list
> 
> stopifnot(exprs = {
+ 	egf_has_converged(o)
+ 	!egf_has_converged(within(o, optimizer_out[["convergence"]] <- 1L))
+ 	!egf_has_converged(within(o, value <- NaN))
+ 	!egf_has_converged(within(o, gradient[1L] <- 10))
+ 	!egf_has_converged(within(o, hessian <- FALSE))
+ 	is.na(egf_has_converged(within(o, hessian <- NA)))
+ })
> 
> 
> ## egf_par_expand   ####################################################
> ## egf_par_condense ####################################################
> 
> t.2 <- o.2[["tmb_out"]]
> p.2 <- t.2[["env"]][["last.par.best"]]
> 
> p.2e <- egf_par_expand(t.2, p.2)
> p.2ec <- egf_par_condense(t.2, p.2e)
> 
> len <- c(table(factor(names(p.2), levels = c("beta", "theta", "b"))))
> 
> stopifnot(exprs = {
+ 	all.equal(p.2e, structure(c(p.2[1:4], theta = 0, p.2[-(1:4)]),
+ 	                          len = len + c(0L, 1L, 0L), names = NULL))
+ 	all.equal(p.2ec, structure(p.2, len = len, names = NULL))
+ })
> 
> 
> ## egf_report   ########################################################
> ## egf_adreport ########################################################
> 
> identical. <-
+ function(x, y, ...) {
+ 	x[["env"]] <- y[["env"]] <- NULL
+ 	identical(x, y, ...)
+ }
> 
> r.1 <- o.1[["tmb_out"]][["env"]][[".__egf__."]][["report"]]
> r.1. <- egf_report(o.1)
> stopifnot(identical.(r.1., r.1))
> 
> o.1[["tmb_out"]][["env"]][[".__egf__."]][["report"]] <- NULL
> r.1. <- egf_report(o.1)
> stopifnot(identical.(r.1., r.1))
> 
> r.1 <- o.1[["tmb_out"]][["env"]][[".__egf__."]][["adreport"]]
> r.1. <- egf_adreport(o.1)
> stopifnot(identical.(r.1., r.1))
> 
> o.1[["tmb_out"]][["env"]][[".__egf__."]][["adreport"]] <- NULL
> assertCondition(r.1. <- egf_adreport(o.1), "message")
computing a Hessian matrix ...
> stopifnot(identical.(r.1., r.1))
> 
> proc.time()
   user  system elapsed 
   5.75    0.23    5.95