spatioTemporalIndices

Git page for the R-package spatioTemporalIndices.

This model uses catch-at-length and age-at-length observations to construct indices-at-length and indices-at-age, along with yearly covariance matrices that include uncertainty in both age-at-length and catch-at-length.

Installation

The model is installed by typing

devtools::install_github("NorskRegnesentral/spatioTemporalIndices/spatioTemporalIndices")

Quick example

Here is a quick example of how to generate indices-at-age with associated covariance structures. For the full R code to run a similar example, we refer to the folder testmore/NEAhadLengthAge. We use Norwegian haddock observations from the Barents Sea winter survey as our data.

Data

The length data must be in the format of a data frame with the following columns: haul ID, length group, time, distance trawled, latitude, longitude, and the number of fish caught. Note that each row represents one observed length group in a haul.

station   lengthGroup   startdatetime   distance  latitude longitude   catch
idHaul1   5         2018-02-02 11:10:46    0.89     73.34    18.13     0
idHaul1   10        2018-02-02 11:10:46    0.89     73.34    18.13     20
idHaul1   15        2018-02-02 11:10:46    0.89     73.34    18.13     52
idHaul1   20        2018-02-02 11:10:46    0.89     73.34    18.13     22

The age-at-length data must be in the format of a data frame with the following columns: haul ID, time, latitude, longitude, length of fish, and readability. Note that each row represents one observed fish. The station ID needs to match the ID given in the length data above.

station   startdatetime       latitude longitude length age readability
idHaul1   2018-02-02 11:10:46  73.34    18.13     32      3         1
idHaul1   2018-02-02 11:10:46  73.34    18.13     28      3         1
idHaul1   2018-02-02 11:10:46  73.34    18.13     17      1         1
idHaul1   2018-02-02 11:10:46  73.34    18.13     54      5         1

Confgurations

Set up configurations for catch-at-length model:

conf_l = defConf(years = 2018:2020, # years to use, 
                 maxLength = 75, 
                 minLength = 20, 
                 spatioTemporal =0 ,
                 spatial =1,
                 stratasystem = list(dsn="strata", layer = "Vintertoktet_nye_strata"),
                 applyALK = 1)

Set up configurations for age-at-length model. Note that the package spatioTemporalALK needs to be installed (https://github.com/NorskRegnesentral/spatioTemporalALK).

conf_alk = defConf_alk(maxAge = 10,
                       minAge = 3,
                       spatioTemporal = 2,
                       spatial =1)

For documentation of the configurations, see ?defConf and ?defConf_alk.

Set up prediction configurations:

confPred = defConfPred(conf=conf_l,cellsize = 20)

Fit model

Fit the model

run = fitModel(dat_l,conf_l, confPred,dat_alk,conf_alk)

Extract indices and covariance structures

The indices and their associated standard deviations can be accessed in the list of reported quantities:

run$rl$logAgeIndex
run$rlSd$logAgeIndex

The indices and corresponding covariance structures can be saved by

saveIndex(run,file = "index.txt", folder = "")

This will save the files index.txt and cov_index.Rda, containing the indices and a list with all yearly covariance matrices.

Use of index and covariance structures in assessment

For the use of the indices and covariance structures in the state space assessment model SAM, we refer to the SAM help file at http://www.nielsensweb.org/configurations.html.