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Population time series with confidence intervals

Source: R/tidy.R
tidy_sdrep.Rd

Returns annual recruitment, abundance, biomass, spawning biomass, and average F and M, with confidence limits and clearly labelled SE scales. Estimates and limits are reported in the units of each population quantity.

Usage

tidy_sdrep(fit, interval = 0.95)

Arguments

fit

A fitted TAM object as returned by fit_tam().

interval

Confidence level in (0, 1); default 0.95.

Value

A named list of data frames (one per series), each with columns:

  • year, est, lwr, upr, se, se_scale, is_proj.

Details

Assumptions:

  • All ADREPORTED series used here have length equal to length(fit$dat$years).

  • Estimates are on the log scale and are transformed with exp via trans_est().

  • List element names are cleaned by removing a leading "log_" prefix.

The interval is constructed as est ± z * se with z = qnorm(1 - (1 - interval) / 2). Estimates and confidence limits are then exponentiated. The se column is the standard error on the log scale, labelled by se_scale = "log". A small log-scale SE approximates the coefficient of variation (CV): for example, 0.10 is approximately 10% relative uncertainty. This approximation becomes less accurate for large SEs; use the confidence limits to describe uncertainty on the reported scale.

See also

trans_est(), tidy_rep(), tidy_pop()

Examples

fit <- fit_tam(
  cod_obs,
  years = 1983:2024,
  ages = 2:14,
  N_settings = list(process = "iid", init = "exp"),
  F_settings = list(process = "rw", mu_form = NULL),
  M_settings = list(process = "off", mu_supplied = ~ I(0.3)),
  catch_settings = list(sd_form = ~ 1),
  index_settings = list(sd_form = ~ 1, q_form = ~ q_block),
  silent = TRUE
)
trends <- tidy_sdrep(fit, interval = 0.9)
names(trends)
#> [1] "F_full"      "recruitment" "abundance"   "biomass"     "ssb"        
#> [6] "F_bar"       "M_bar"      
head(trends$ssb)
#>   year      est      lwr      upr        se se_scale is_proj   unit
#> 1 1983 374324.2 310913.6 450667.3 0.1128411      log   FALSE tonnes
#> 2 1984 382659.2 319265.9 458639.8 0.1101134      log   FALSE tonnes
#> 3 1985 386083.0 317756.7 469101.4 0.1184096      log   FALSE tonnes
#> 4 1986 468682.0 379381.5 579002.4 0.1285113      log   FALSE tonnes
#> 5 1987 407237.1 332071.2 499417.1 0.1240512      log   FALSE tonnes
#> 6 1988 474914.6 382909.6 589026.6 0.1309152      log   FALSE tonnes