Impute missing camera counts using GLM or GLMM

Fills outage rows in a camera count data frame using a per-stratum model. The GLM method (default, Hartill 2016) fits a Poisson GLM with strata_col (typically day_type) as the sole predictor. The GLMM method (Afrifa-Yamoah 2020) fits a negative binomial GLMM and requires the glmmTMB package (in Suggests).

Outage rows are identified as any row where status_col != "operational" AND count_col is NA. All rows are returned; imputed rows have .imputed = TRUE. The original status_col values (e.g., "battery_failure") are preserved in imputed rows for traceability.

Usage

impute_camera_counts(
  data,
  count_col,
  strata_col,
  status_col = "camera_status",
  method = "glm",
  site_col = NULL
)

Arguments

data

A data frame of camera count records. Must have at least one row and must contain the columns named by count_col, strata_col, and status_col.

count_col

Character scalar. Name of the integer count column (e.g., "ingress_count"). Outage rows have NA in this column.

strata_col

Character scalar. Name of the day-type stratum column (e.g., "day_type"). Used as the predictor in the per-stratum GLM/GLMM.

status_col

Character scalar. Name of the camera status column. Default "camera_status". Rows where this column is not "operational" and count_col is NA are treated as outages.

method

Character scalar. Imputation model: "glm" (default, Poisson GLM, no extra dependencies) or "glmm" (negative binomial GLMM via glmmTMB, requires glmmTMB in Suggests).

site_col

Character scalar or NULL. When method = "glmm" and site_col is not NULL, a random intercept (1 | site_col) is included in the GLMM formula. Default NULL.

Value

A data frame with the same rows and columns as data, plus a new logical column .imputed appended as the last column. Outage rows are filled in count_col with model-predicted counts (rounded to integer). The count_col storage mode is set to "integer" for schema compatibility with add_counts(). Row count equals nrow(data).

Details

References

Hartill, B.W., Cryer, M., and Morrison, M.A. 2020. Camera-based creel surveys: estimating fishing effort and catch rates from ingress-egress camera counts. Fisheries Research 231:105706. doi:10.1016/j.fishres.2020.105706

Afrifa-Yamoah, E., Mueller, U.A., Taylor, S.M., and Fisher, A. 2020. Missing data imputation of high-resolution temporal climate data series using an integrated framework of expectation maximisation and long short-term memory neural networks.

See also

est_effort_camera(), add_counts()

Other "Survey Design": add_catch(), add_counts(), add_interviews(), add_lengths(), add_sections(), as_hybrid_svydesign(), as_survey_design(), compute_angler_effort(), compute_effort(), creel_design(), creel_schema(), est_effort_camera(), prep_counts_boat_party(), prep_counts_daily_effort(), prep_interview_catch(), prep_interviews_trips(), validate_creel_schema()

Examples

if (FALSE) { # \dontrun{
library(tidycreel)
data(example_camera_counts)

# Impute missing counts using the default Poisson GLM
imputed <- impute_camera_counts(
  example_camera_counts,
  count_col  = "ingress_count",
  strata_col = "day_type"
)

# Inspect imputed rows
imputed[imputed$.imputed, ]

# Pass imputed data directly into a camera design
cal <- data.frame(
  date     = unique(example_camera_counts$date),
  day_type = unique(example_camera_counts[, c("date", "day_type")])[["day_type"]]
)
design <- creel_design(cal,
  date = date, strata = day_type,
  survey_type = "camera", camera_mode = "counter"
)
design <- add_counts(design, imputed)
} # }