estimate_effort(),
estimate_catch_rate() and
estimate_total_catch() are each one function, but behind
each is more than one estimator. This page explains which one runs, what
decides it, and how to see afterwards which one was used.
Two things decide it, both fixed when the design is built:
survey_type (creel_design) -> how effort is estimated, and how totals are built
interview_type (add_interviews) -> how the catch rate is estimatedNeither is chosen by looking at the data. The estimator follows from how the data were collected, which the design records, so the same data attached to the same design always get the same estimator.
survey_type |
Effort | Catch rate | Total catch |
|---|---|---|---|
"instantaneous" |
counts × period length, summed over the sampled days, or expanded to
every day of the stratum with target |
by interview_type (below) |
per stratum, effort × rate, summed over strata |
"aerial" |
counts × hours open × angler ratio ÷ visibility | by interview_type |
per stratum, effort × rate, summed over strata |
"bus_route" |
Horvitz-Thompson: each interviewed trip’s effort over its inclusion probability | ratio of the Horvitz-Thompson catch and effort totals | Horvitz-Thompson catch total |
"ice" |
as bus route, with every site’s p_site = 1 |
as bus route | as bus route |
"camera" |
not estimate_effort(): est_effort_camera()
calibrates counts against interview effort |
by interview_type |
refused |
Sectioned instantaneous designs (add_sections())
estimate each section separately and add a lake total.
Whether the effort covers only the days counted or every day in the
season is a separate choice, target, not a different
estimator. The default, "sampled_days", is the effort on
the days counted; "period_total" expands each stratum to
all its days. On the season data used below, that is 3,066 angler-hours
against about 9,994. Totals follow the effort they are given, so a catch
total on the default target is the catch on the sampled days.
vignette("effort-pipeline") explains the expansion, and the
expanded targets are not yet available for bus-route or ice designs.
The pages that explain each estimator in full:
vignette("effort-pipeline");vignette("catch-pipeline");vignette("bus-route-surveys"),
vignette("ice-fishing"), and the equations in
vignette("bus-route-equations");vignette("aerial-surveys"), and the model-based
alternative, estimate_effort_aerial_glmm(), in
vignette("aerial-glmm");vignette("camera-surveys").The survey type says what a count is, and so what turns it into effort.
target, the sampled days are then expanded to every day of
the stratum.p_site = 1).estimate_effort() refuses a camera design for that
reason and names est_effort_camera(), which converts counts
to effort with a ratio taken from days that have both counts and
interviews.An access interview meets a party as it leaves, so the trip is complete. A roving interview meets a party while it is still fishing. The two sample trips differently, and so they need different estimators (Pollock et al. 1997):
n_anglers). Without a party size the
rate is per party-hour, and each party counts once. Trips shorter than
half an hour are left out by default: their rates are mostly noise and
inflate the variance.Why each of these is right, and when, is the subject of
vignette("catch-pipeline").
Bus-route and ice designs have their own rate: catch over effort,
each a Horvitz-Thompson total. interview_type does not
change it.
A total is a rate times an effort, built stratum by stratum and summed, with the standard error from Goodman’s (1960) exact variance of a product. Bus-route and ice totals are different: the catch is weighted by its inclusion probability directly (Jones and Pollock 2012, eq. 19.5), with no product.
Camera designs have no total. Their counts are arrivals, so a rate
per angler-hour times a camera count would not be a catch, and
estimate_total_catch() refuses rather than report one.
Every estimate records how it was made. method names the
estimator and, for rates and the totals built from them,
estimator names the rate estimator underneath. On the
season data, with access and then roving interviews:
design <- creel_design(example_season_calendar, date = date, strata = day_type)
design <- add_counts(design, example_season_counts,
period_length_col = daylight_hours
)
access <- add_interviews(design, example_season_interviews,
catch = catch_total, harvest = catch_kept, effort = hours_fished,
trip_status = trip_status, n_anglers = n_anglers
)
#> ℹ 153 interviews have zero catch.
#> Zero catch is usual; check it is not missing data recorded as 0.
#> ℹ Added 231 interviews: 172 complete (74%), 59 incomplete (26%)
roving <- add_interviews(design, example_roving_interviews,
catch = catch_total, harvest = catch_kept, effort = hours_fished,
trip_status = trip_status, trip_duration = hours_fished,
n_anglers = n_anglers, interview_type = "roving"
)
#> ℹ 133 interviews have zero catch.
#> Zero catch is usual; check it is not missing data recorded as 0.
#> ℹ Added 212 interviews: 0 complete (0%), 212 incomplete (100%)
access_rate <- estimate_catch_rate(access)
#> ℹ Using complete trips for CPUE estimation
#> (n=172, 74.5% of 231 interviews) [default]
roving_rate <- estimate_catch_rate(roving)
#> ℹ Roving design: using all 212 interviews for CPUE via MOR [auto]
#> (0 complete, 212 incomplete)
#> Override with `use_trips = 'complete'` for access-point estimation.
#> Warning: ! MOR truncation: 29 trips excluded (13.7%)
#> ℹ Trips < 0.5 hours excluded to prevent unstable variance
#> ! High truncation rate may indicate data quality issues
#> ℹ Consider reviewing trip duration data for errors
#> ℹ Repeats on this design are recorded, not printed: see `get_warnings()`.
c(access = access_rate$method, roving = roving_rate$method)
#> access roving
#> "ratio-of-means-cpue" "mean-of-ratios-cpue"
c(access = access_rate$estimator, roving = roving_rate$estimator)
#> access roving
#> "ratio-of-means" "mor"The two messages say the same thing as it happens: complete trips for
the access design, and all trips by mean of ratios for the roving one,
marked [auto] because the interview type chose it. The
roving rate also warns that 29 trips under half an hour were left out.
That is expected here: a roving clerk meets many parties early in their
trips, and the warning fires whenever 10% or more are removed, so that a
data error that shortens trips is noticed.
The defaults follow the design, but the rate estimator can be named:
estimator: "ratio-of-means",
"mor" (mean of ratios), or "mortr" (mean of
ratios with trips under truncate_at hours removed);use_trips: "complete" or
"all"; the rate functions also take
"incomplete", and "diagnostic", which
estimates from complete and incomplete trips separately and compares
them. A total accepts only "complete" and
"all";truncate_at: the shortest trip, in hours, a mean of
ratios keeps.Naming either estimator or use_trips turns
off the roving default, so the roving design then gets what was asked
for. Some combinations are refused because they are not an estimator: a
ratio of means on incomplete trips, which the length bias of incomplete
trips makes wrong, and a mean-of-ratios total on a bus-route or ice
design, which has no such form.
The variance method (variance = "taylor",
"bootstrap" or "jackknife") and the product
variance of a total (product_variance = "goodman" or
"first_order") are separate choices. They change the
standard error, not the estimator;
vignette("replicate-designs") and
vignette("catch-pipeline") explain them.