GLMMstd: Generalized Linear Mixed Model Based Standardization...

In FESta: Fishing Effort Standardization

Generalized Linear Mixed Model Based Standardization (GLMMstd)

Description

Fisheries catch and effort data frequently exhibit dependency structures arising from repeated observations of vessels, trips, areas, observers, or other sampling units. Generalized Linear Mixed Models (GLMMs) accommodate these dependencies by incorporating random effects in addition to fixed explanatory variables.

In this implementation, CPUE is calculated as:

CPUE_i=\frac{Catch_i}{Effort_i}

and transformed using a logarithmic transformation:

log(CPUE_i)

The model fitted is:

g(\mu_i) = X_i\beta + Z_i u

where g is the log link, \mu_i = E(CPUE_i), and CPUE follows a Gamma distribution.

Fixed effects typically include factors such as year, gear, season, or fishing area, while random effects may represent vessels, trips, observers, ports, or other grouping variables.

Standardized CPUE indices are obtained by predicting CPUE for each level of the selected index variable while averaging over random-effect variation.

Usage

GLMMstd(
  data,
  year_col,
  catch_col,
  effort_col,
  fixed_effects,
  random_effects,
  log_transform = TRUE
)

Arguments

data	A data frame containing the columns of year, catch, effort, fixed effects and random effects. See the example dataset GLMMstd_dataset.
year_col	Specify the year column name (eg. "Year").
catch_col	Specify the catch column name (eg. "Catch").
effort_col	Specify the effort column name (eg. "Effort").
fixed_effects	Specify the column names of the fixed effects in vector format (eg. c("Year","Gear")).
random_effects	Specify the column names of the random effects in vector format (eg. c("Vessel","Area")). Should not contain any fixed effect column name.
log_transform	Specify TRUE or FALSE. By default set TRUE. It ensures CPUE values will be shown after required log transformation of data.

Value

The output includes AIC and SBC/BIC values, a summary table containing Year, Total Catch, Nominal CPUE, and Standardized CPUE. In addition, two plots are produced: Nominal CPUE versus Total Catch and Standardized CPUE versus Total Catch.

Note

If catch column value of has zero value(s) then they will be replaced by minimum value of the catch column and further if effort column has zero values then the corresponding rows will be removed for doing the CPUE calculation.

References

Pinheiro, J.C., and Bates, D.M. (2000). Mixed-Effects Models in S and S-PLUS. Springer-Verlag, New York.

Maunder, M.N., and Punt, A.E. (2004). Standardizing catch and effort data: a review of recent approaches. Fisheries Research, 70, 141-159.

Acknowledgements: The authors sincerely thank the Director, ICAR–Central Marine Fisheries Research Institute (ICAR-CMFRI), Kochi, for providing the necessary facilities and institutional support. The authors also gratefully acknowledge the support provided by the Indian Council of Agricultural Research (ICAR), Department of Agricultural Research and Education (DARE), Government of India, through the ICAR-National Fellow Project.

Examples

## Not run: 
library(FESta)
data('GLMMstd_dataset')
result<-GLMMstd(
  data=GLMMstd_dataset,
  year_col="Year",
  catch_col = "Catch",
  effort_col = "Effort",
  fixed_effects = c("Year"),
  random_effects = c("Vessel"),
  log_transform = TRUE
)
print(result)

## End(Not run)

FESta documentation built on Aug. 20, 2026, 5:10 p.m.