ORQGLMstd: Ordered Quantile Transformation GLM Based Standardization...

In FESta: Fishing Effort Standardization

Ordered Quantile Transformation GLM Based Standardization (ORQGLMstd)

Description

Fisheries catch and CPUE data are often highly skewed, heavy-tailed, or non-normal, which can violate the assumptions of conventional Gaussian models. The Ordered Quantile (ORQ) transformation addresses this by mapping observed CPUE values to an approximately standard normal distribution while preserving rank order.

After transformation, a Gaussian GLM is fitted using the specified explanatory variables. Standardized predictions are generated for each level of the index variable and back-transformed to the original CPUE scale using the inverse ORQ transformation.

Usage

ORQGLMstd(data, year_col, catch_col, effort_col, fixed_effects, maxit = 100)

Arguments

data	A data frame containing the columns of year, catch, effort and fixed effects. See the example dataset ORQGLMstd_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")).
maxit	Maximum number of iterations allowed during GLM fitting. Default 100.

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 effort column has zero values then the corresponding rows will be removed for doing the CPUE calculation.

References

Peterson, R.A. (2021). Finding Optimal Normalizing Transformations via bestNormalize. The R Journal, 13(1), 310-329.

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("ORQGLMstd_dataset")
result<-ORQGLMstd(
  data  = ORQGLMstd_dataset,
  year_col="Year",
  catch_col      = "Catch",
  effort_col     = "Effort",
  fixed_effects  = c("Year", "Gear"),
  maxit          = 100
)
print(result)

## End(Not run)

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