Incorporating observation data into models

weight and number columns

At least a weight or number column needs to be supplied. If a number column is present then each value will be compared to number of individuals in that group. If a weight column is present then each value will be compared to total biomass in that group.

The weight is suitable for total catches in kilos, otherwise the number column will generally be used.

year and step columns: Landings, abundance indices

If a year column is given, then catch/abundance will be aggregated by year. Gaps are allowed, if so then no comparisons will be made for that year/step.

The following observations will be compared against the total number of indivduals caught in years 1999, 2000, 2002, 2003:

g3_distribution_preview(read.table(header = TRUE, text="
year  number
1999    1000
2000    1002
2002    1004
2003    1008
"))

##        time
## length  1999 2000 2002 2003
##   0:Inf 1000 1002 1004 1008

If step is given, then it will restrict it to that step within the year (see ?g3a_time for how to define steps in a year).

In the following example, we will aggregate spring of 1999 & 2000, autumn of 2001. Any catch in spring of 2001, or other periods not mentioned, will be ignored:

g3_distribution_preview(read.table(header = TRUE, text="
year step number
1999    2   1020
2000    2   2040
2001    3   1902
"))

##        time
## length  1999-02 2000-02 2001-03
##   0:Inf    1020    2040    1902

length column: Length distribution data

Adding a length column will aggregate catch/abundance data by the same length bins as in the observation data.

For instance:

g3_distribution_preview(read.table(header = TRUE, text="
year  length number
1999  [0,10)  1023
1999 [10,20)  2938
1999 [20,30)  3948
1999 [30,40)  3855
2000  [0,10)  1023
2000 [10,20)  2938
# NB: No [10,30)
2000 [30,40)  3855
"))

##        time
## length  1999 2000
##   0:10  1023 1023
##   10:20 2938 2938
##   20:30 3948   NA
##   30:40 3855 3855

Note that unlike with the year & step columns, here gaps in data are preserved, in the preview output we see NA for the missing year & length combination. By default we will compare to 0 at this point, this behaviour is controlled with the missing_val parameter to ?g3l_catchdistribution.

Length aggregations do not have to be hand-crafted like we do above, a length column could be generated using dplyr::group_by() and cut():

# Generate an unaggregated length distribution
ldist.lln.raw <- data.frame(
    year = c(1999, 2000),
    length = sample(10:75, 100, replace = TRUE),
    number = 1,
    stringsAsFactors = FALSE)

# Group length into 10-long bins
ldist.lln.raw |> dplyr::group_by(
  year = year,
  length = cut(length, breaks = seq(10, 100, by = 10), right = FALSE)
) |> dplyr::summarise(number = sum(number), .groups = 'keep') -> ldist.lln.agg
ldist.lln.agg

## # A tibble: 14 × 3
## # Groups:   year, length [14]
##     year length  number
##    <dbl> <fct>    <dbl>
##  1  1999 [10,20)     11
##  2  1999 [20,30)      5
##  3  1999 [30,40)      7
##  4  1999 [40,50)     11
##  5  1999 [50,60)      8
##  6  1999 [60,70)      7
##  7  1999 [70,80)      1
##  8  2000 [10,20)      3
##  9  2000 [20,30)      6
## 10  2000 [30,40)     14
## 11  2000 [40,50)     10
## 12  2000 [50,60)      9
## 13  2000 [60,70)      6
## 14  2000 [70,80)      2

# NB: The last 2 bins are empty, but because cut() creates a factor column,
#     gadget3 knows about them even though they don't appear in the data.
g3_distribution_preview(ldist.lln.agg)

##         time
## length   1999 2000
##   10:20    11    3
##   20:30     5    6
##   30:40     7   14
##   40:50    11   10
##   50:60     8    9
##   60:70     7    6
##   70:80     1    2
##   80:90    NA   NA
##   90:100   NA   NA

gadget3 will also automatically read the aggregation attributes used by ?mfdb::mfdb_sample_count.

# Import data into a temporary database
library(mfdb)
mdb <- mfdb(tempfile(fileext=".duckdb"))
ldist.lln.raw$month <- 1
ldist.lln.raw$areacell <- 'all'  # NB: We have to have an areacell mapping for MFDB
mfdb_import_area(mdb, data.frame(name = c('all'), size = c(5)))
mfdb_import_survey(mdb, ldist.lln.raw)

# Use mfdb_sample_count to extract & group in the same manner as above
ldist.lln.agg <- mfdb_sample_count(mdb, c('length'), list(
    year=1999:2000,
    length = mfdb_interval("len", seq(10, 100, by = 10)) ))[[1]]
g3_distribution_preview(ldist.lln.agg, area_group = c(all=1))

mfdb_disconnect(mdb)

age column: Age-Length distribution data

Age-length aggregations can be performed by adding an age column in a very similar manner to the length column:

We can both group by individual age values:

g3_distribution_preview(read.table(header = TRUE, text="
year  age  length number
1999    1  [0,10)  1026
1999    1 [10,20)  2936
1999    1 [20,30)  3962
1999    1 [30,40)  3863
1999    2  [0,10)  1026
1999    2 [10,20)  2936
1999    2 [20,30)  3962
1999    2 [30,40)  3863
"))

## , , time = 1999
## 
##        age
## length  age1 age2
##   0:10  1026 1026
##   10:20 2936 2936
##   20:30 3962 3962
##   30:40 3863 3863

…or group ages together:

g3_distribution_preview(read.table(header = TRUE, text="
year  age    length number
1999  [1,1]    [0,10)  1026
1999  [1,1]   [10,20)  2936
1999  [1,1]   [20,30)  3962
1999  [1,1]   [30,40)  3863
1999  [2,4]  [0,10)  1011
# Missing [2,4] [10,20)
1999  [2,4] [20,30)  3946
1999  [2,4] [30,40)  3872
"))

## , , time = 1999
## 
##        age
## length   1:1  2:4
##   0:10  1026 1011
##   10:20 2936   NA
##   20:30 3962 3946
##   30:40 3863 3872

As before, gaps in data are preserved, and missing_val is used to decide what to do with them.

Again, gadget3 will also interpret aggregation generated by group_by(age = cut(...)) or ?mfdb::mfdb_sample_count.

area column

If a stock is divided up into multiple areas, then data can be broken down by area

area_names <- g3_areas(c('IXa', 'IXb', 'IXc'))

g3_distribution_preview(read.table(header = TRUE, text="
year    area    number
1999   IXa   1000
1999   IXb   4305
2000   IXa   7034
2000   IXb   2381
2001   IXb   3913
"), area_group = area_names)[length = '0:Inf',,]

##       area
## time    IXa  IXb
##   1999 1000 4305
##   2000 7034 2381
##   2001   NA 3913

As before, gaps in data are preserved, and missing_val is used to decide what to do with them.

However, if an area isn’t mentioned at all (note that IXc does not figure in the above data), then it won’t be compared.

stock column: Maturity stage distribution

If you have multiple stocks, for example because you have divided up your species into mature and immature substocks, you can use this division in likelihood components also:

st_imm <- g3_stock(c(species = 'fish', 'imm'), 1:10)
st_mat <- g3_stock(c(species = 'fish', 'mat'), 1:10)

g3_distribution_preview(read.table(header = TRUE, text="
year    stock    number
1999   fish_imm   1000
1999   fish_mat   4305
2000   fish_imm   7034
2000   fish_mat   2381
2001   fish_mat   3913
"), stocks = list(st_imm, st_mat))[length = '0:Inf',,]

##           time
## stock      1999 2000 2001
##   fish_imm 1000 7034   NA
##   fish_mat 4305 2381 3913

The stock names have to match what gadget3 uses, or an error will be generated.

As before, gaps in data are preserved, and missing_val is used to decide what to do with them.

fleet column

f_comm <- g3_fleet('comm')
f_surv <- g3_fleet('surv')

g3_distribution_preview(read.table(header = TRUE, text="
year    fleet    number
1999   f_comm   1000
1999   f_surv   4305
2000   f_comm   7034
2000   f_surv   2381
2001   f_surv   3913
"), fleets = list(f_comm, f_surv))[length = '0:Inf',,]

##         time
## fleet    1999 2000 2001
##   f_comm 1000 7034   NA
##   f_surv 4305 2381 3913

The stock names have to match what gadget3 uses, or an error will be generated.

As before, gaps in data are preserved, and missing_val is used to decide what to do with them.