This vignette explains why tidycreel’s effort estimates work the way they do. It is written for creel biologists who know their surveys but have not had to think carefully about sampling theory. By the end you will understand three things:
Each concept is developed from first principles, illustrated with a
worked numeric example, and confirmed against
estimate_effort() output.
In an instantaneous creel survey, the observer selects a random time on a given day and records the number of anglers visible on the survey section. Each day × stratum combination is treated as one sampling unit. In survey-sampling language these units are called Primary Sampling Units (PSUs).
Consider a three-day survey with two day-type strata:
| Date | Day type | PSU |
|---|---|---|
| 2024-06-03 | weekday | PSU-1 |
| 2024-06-03 | weekend | PSU-2 |
| 2024-06-04 | weekday | PSU-3 |
| 2024-06-04 | weekend | PSU-4 |
| 2024-06-05 | weekday | PSU-5 |
| 2024-06-05 | weekend | PSU-6 |
Six PSUs in total — three per stratum. If the survey selected two weekday PSUs and two weekend PSUs, the sample would contain four PSUs out of six.
The population we want to estimate is the season total over all \(N_h\) PSUs in stratum \(h\). The sample gives us observations on \(n_h\) of those PSUs.
For a stratified random sample, the Horvitz–Thompson (HT) estimator of the population total is:
\[\hat{E} = \sum_{i \in s} \frac{y_i}{\pi_i}\]
When sampling is done without replacement and all PSUs in a stratum are equally likely to be chosen, the inclusion probability is \(\pi_i = n_h / N_h\). Substituting into the HT form and recognising that the sum over \(s_h\) covers \(n_h\) PSUs gives the familiar stratum-total estimator.
For stratum \(h\) with \(N_h\) total days and \(n_h\) sampled days:
\[\hat{E}_h = \frac{N_h}{n_h} \sum_{i \in s_h} y_i\]
The ratio \(N_h / n_h\) is the expansion factor: if only one in three days was sampled, each observed count represents three days worth of effort in the population.
Stratum: “weekday”. \(N = n = 3\) days sampled (all available days observed). Counts: \(y_1 = 150, \; y_2 = 230, \; y_3 = 190\) angler-hours.
\[\hat{E}_{\text{weekday}} = \frac{3}{3} \times (150 + 230 + 190) = 1 \times 570 = 570 \text{ angler-hours}\]
Confirming with R:
library(tidycreel)
calendar <- data.frame(
date = as.Date(c("2024-06-03", "2024-06-04", "2024-06-05")),
day_type = rep("weekday", 3)
)
design <- creel_design(calendar, date = date, strata = day_type)
# n_anglers holds angler-hours (count × open hours already combined)
counts <- data.frame(
date = as.Date(c("2024-06-03", "2024-06-04", "2024-06-05")),
day_type = rep("weekday", 3),
n_anglers = c(150L, 230L, 190L)
)
design <- add_counts(design, counts)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability
result <- estimate_effort(design)
result$estimates
#> # A tibble: 1 × 7
#> estimate se se_between se_within ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 570 69.3 69.3 0 272. 868. 3The estimate column (570) matches the by-hand result.
With a single count per day se_within is always 0 — there
is no within-day replication to estimate within-day variance.
When two observers (or the same observer on two circuits) count the same section twice on the same day — once in the morning and once in the afternoon — we have replicated measurements within each PSU. This within-day replication does something useful: it separates measurement noise from true day-to-day variation.
Without multiple counts per day, we cannot tell whether the variation across days is caused by real differences in angler activity or by random counting error. With replication we can decompose the total variance into two components:
\[\text{SE}^2(\hat{E}) = \text{SE}^2_{\text{between}} + \text{SE}^2_{\text{within}}\]
The between-day component measures how much estimated effort varies from one sampled PSU to the next. It is computed by treating the within-day mean \(\bar{y}_i\) (average of all counts on day \(i\)) as the PSU-level observation and applying the standard variance-of-a-total formula:
\[\text{SE}^2_{\text{between}} = \frac{N_h^2}{n_h} \cdot s^2_{\bar{y}}\]
In plain language: this is the variance you would report even with a single count per day, using the daily mean in place of a single count. It reflects how much effort differs from one day to the next.
The within-day component measures how much counts vary within a single day — that is, how much the morning count differs from the afternoon count. The Rasmussen (1994) formula is:
\[\text{SE}^2_{\text{within}} = \frac{N_h^2}{n_h^2} \sum_{i=1}^{n_h} \frac{s^2_{d,i}}{k_i}\]
Equivalently, using the pooled formulation implemented in tidycreel:
\[\text{SE}^2_{\text{within}} = \frac{N_h}{k_{\text{avg}}} \cdot s^2_{\text{within}}\]
where \(s^2_{\text{within}} = \frac{\sum_{i} \text{SS}_{d,i}}{n_h (k_{\text{avg}} - 1)}\) is the pooled within-day variance and \(\text{SS}_{d,i} = \sum_j (y_{ij} - \bar{y}_i)^2\) is the sum of squared deviations within day \(i\).
In plain language: this is how much counting error you are carrying around. A large within-day component means the morning and afternoon counts disagree substantially — the observer is seeing large swings in angler activity within a single day, or measurement error is high.
Four weekdays are sampled (\(N = n =
4\)). Each day receives two counts (morning am and
afternoon pm):
| Day | am count | pm count | Daily mean \(\bar{y}_i\) | \(\text{SS}_{d,i}\) |
|---|---|---|---|---|
| 1 | 10 | 20 | 15 | 50 |
| 2 | 20 | 30 | 25 | 50 |
| 3 | 30 | 40 | 35 | 50 |
| 4 | 40 | 50 | 45 | 50 |
Grand mean of daily means: \(\bar{\bar{y}} = (15 + 25 + 35 + 45)/4 = 30\)
Between-day SE (step by step):
Sample variance of daily means:
\[s^2_{\bar{y}} = \frac{(15-30)^2 + (25-30)^2 + (35-30)^2 + (45-30)^2}{4 - 1} = \frac{225 + 25 + 25 + 225}{3} = \frac{500}{3} \approx 166.67\]
Between-day variance (without finite-population correction, as in the
with-replacement design assumed by survey::svytotal()):
\[\text{SE}^2_{\text{between}} = \frac{N^2}{n} \cdot s^2_{\bar{y}} = \frac{16}{4} \times 166.67 = 4 \times 166.67 = 666.67\]
\[\text{SE}_{\text{between}} = \sqrt{666.67} \approx 25.82\]
Within-day SE (step by step):
\(k_{\text{avg}} = 2\); all \(\text{SS}_{d,i} = 50\):
\[s^2_{\text{within}} = \frac{\sum \text{SS}_{d,i}}{n \cdot (k_{\text{avg}} - 1)} = \frac{50 + 50 + 50 + 50}{4 \times (2 - 1)} = \frac{200}{4} = 50\]
\[\text{SE}^2_{\text{within}} = \frac{N}{k_{\text{avg}}} \cdot s^2_{\text{within}} = \frac{4}{2} \times 50 = 100\]
\[\text{SE}_{\text{within}} = \sqrt{100} = 10\]
Combined SE:
\[\text{SE} = \sqrt{666.67 + 100} = \sqrt{766.67} \approx 27.70\]
Confirming with R:
calendar_m <- data.frame(
date = as.Date(c("2024-06-03", "2024-06-04", "2024-06-05", "2024-06-06")),
day_type = rep("weekday", 4)
)
design_m <- creel_design(calendar_m, date = date, strata = day_type)
multi_counts <- data.frame(
date = as.Date(rep(
c("2024-06-03", "2024-06-04", "2024-06-05", "2024-06-06"),
each = 2
)),
day_type = rep("weekday", 8),
count_time = rep(c("am", "pm"), 4),
n_anglers = c(10L, 20L, 20L, 30L, 30L, 40L, 40L, 50L)
)
design_m <- add_counts(design_m, multi_counts, count_time_col = count_time)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability
result_m <- estimate_effort(design_m)
result_m$estimates
#> # A tibble: 1 × 7
#> estimate se se_between se_within ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 120 27.7 25.8 10 31.9 208. 4The R output confirms: se_between \(\approx 25.82\), se_within
\(= 10.00\), and se \(\approx 27.70\) — matching the by-hand
calculations exactly.
se_within matter?se_within is small when angler activity is stable
throughout the day. It is large when fishing activity follows a strong
temporal pattern — for example, an active morning bite followed by a
quiet midday and a busy evening. If
se_within / se > 0.5, within-day variation is the
dominant source of uncertainty, and collecting additional circuits per
day would improve precision more than adding more sampled days.
The progressive count is a distinct field method, not a variant of the instantaneous count. Instead of taking a snapshot at a random moment, the observer traverses the entire survey section and records every angler encountered during the traversal. One pass of the section is called a circuit.
Because the observer is moving rather than counting instantaneously, the estimator formula and its interpretation differ.
Let \(C_d\) be the number of anglers encountered during one circuit on day \(d\), and let \(T_d\) be the total hours the section is open on day \(d\).
\[\hat{E}_d = C_d \times T_d\]
Why does \(\tau\) cancel? Follow Hoenig et al. (1993, p. 725). The circuit is a sample of one \(\tau\)-hour block out of the day, so the argument runs in two steps.
First, for the single block that was sampled, the count expands over its own duration:
\[\hat{E}_{\text{block}} = C_d \times \tau\]
Second, the day is partitioned into \(K = T_d / \tau\) blocks of length \(\tau\), and the sampled block is drawn at random from them, so it is an unbiased estimate of the mean block. Scaling by the number of blocks gives the day:
\[\hat{E}_d = C_d \times \tau \times K = C_d \times \tau \times \frac{T_d}{\tau} = C_d \times T_d\]
\(\tau\) enters twice — once
expanding the block, once counting how many blocks there are — and
cancels between them. So the cancellation is a property of the method,
not an accident of the algebra: \(\tau\) has already done its work in
defining the blocks the count was scheduled within,
which is why generate_progressive_start() needs it and the
final arithmetic does not.
That also explains why \(\tau\) is
still required by add_counts(). It is not inert — it
determines whether the schedule that produced \(C_d\) was valid. Two consequences are
enforced: a shift shorter than one circuit (\(K < 1\)) is an error, since no circuit
completed; and \(\tau = T_d\) (\(K = 1\)) is the legitimate all-day-circuit
case of Robson (1961), not an edge case to reject.
Three weekdays; circuit time \(\tau = 2\) hours; section open \(T_d = 8\) hours each day. Counts: \(C_1 = 15\), \(C_2 = 20\), \(C_3 = 25\).
| Day | \(C_d\) | \(T_d\) | \(\hat{E}_d = C_d \times T_d\) |
|---|---|---|---|
| 1 | 15 | 8 | 120 |
| 2 | 20 | 8 | 160 |
| 3 | 25 | 8 | 200 |
Season total (N = n = 3): \(\hat{E} = (3/3) \times (120 + 160 + 200) = 480\) angler-hours.
Confirming with R (note: add_counts() stores the
expanded \(\hat{E}_d\) values in the
n_anglers column automatically):
calendar_p <- data.frame(
date = as.Date(c("2024-06-03", "2024-06-04", "2024-06-05")),
day_type = rep("weekday", 3)
)
design_p <- creel_design(calendar_p, date = date, strata = day_type)
prog_counts <- data.frame(
date = as.Date(c("2024-06-03", "2024-06-04", "2024-06-05")),
day_type = rep("weekday", 3),
n_anglers = c(15L, 20L, 25L),
shift_hours = rep(8, 3)
)
design_p <- add_counts(
design_p, prog_counts,
count_type = "progressive",
circuit_time = 2,
period_length_col = shift_hours
)
#> Warning in svydesign.default(ids = psu_formula, strata = strata_formula, : No
#> weights or probabilities supplied, assuming equal probability
# Per-day Ê_d values stored after C × T expansion
design_p$counts
#> date day_type n_anglers
#> 1 2024-06-03 weekday 120
#> 2 2024-06-04 weekday 160
#> 3 2024-06-05 weekday 200
result_p <- estimate_effort(design_p)
result_p$estimates
#> # A tibble: 1 × 7
#> estimate se se_between se_within ci_lower ci_upper n
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int>
#> 1 480 69.3 69.3 0 182. 778. 3The n_anglers column shows 120, 160, 200 — the
pre-expanded daily totals — and the season total is 480 angler-hours,
matching the by-hand calculation.
Three estimator pathways are available for instantaneous creel surveys:
| Scenario | Data collected | Key formula | se_within |
|---|---|---|---|
| Single count per day | One count per PSU | \(\hat{E}_h = (N_h/n_h)\sum y_i\) | Always 0 |
| Multiple counts per day | \(k \geq 2\) counts per PSU | \(\hat{E}_h = (N_h/n_h)\sum \bar{y}_i\) | Nonzero; measures within-day variance |
| Progressive count | One circuit per day | \(\hat{E}_d = C_d \times T_d\) | Always 0 |
Single count per day is the classical creel estimator. All variance is between-day variation; there is no within-day component to estimate.
Multiple counts per day (Rasmussen two-stage) adds a
within-day variance component. Use this design when angler activity
patterns within a day are uncertain or known to vary substantially.
Monitor the ratio se_within / se: a value above 0.5 means
that adding circuits per day improves precision more than adding sampled
days.
Progressive counts are appropriate when the section
is long enough that a spot-check count would miss part of the angler
population. The \(C \times T\) formula
recovers total angler-hours without needing \(\tau\) in the final estimate. Variance
structure is the same as for single-count instantaneous surveys. The
formula is unbiased only when the circuit start, start location and
travel direction were randomised and the observer outpaced the anglers;
none of this is checkable from the counts table, so see
vignette("progressive-count-surveys") for the field
conditions before relying on the estimate.
Hoenig, J. M., Robson, D. S., Jones, C. M., and Pollock, K. H. (1993). Scheduling counts in the instantaneous and progressive count methods for estimating sportfishing effort. North American Journal of Fisheries Management, 13, 723–736.
Pope, S. L., et al. (in press). Progressive count methods for creel surveys.
Rasmussen, P. W. (1994). Two-stage variance estimation for creel surveys with multiple counts per sampling unit. Cited in Su, Y.-S., and Liu, P. (2025), equations 17–18.
Su, Y.-S., and Liu, P. (2025). Flexible creel survey estimators. Canadian Journal of Fisheries and Aquatic Sciences, 82, 1–27.
For the API walkthrough showing these functions on your own data, see Flexible Count Estimation.