Burnham model - Missing Year/Cohort in Design data list #7
Description
Hi!
I have an issue when I try to build a Burnham model with LD data.
(Please note that the registration on the Phidot forum are closed, hence why I posted my issue here. Feel free to move it to the appropriate place)
When I build the ddl object, there seems to miss every other year in the Cohort/Year colums. This issue follows when building models and cause the PIMS matrices to have an extra parameters for the cohort missing.
See below a reproducible example with a subset of my data :
library(RMark)
#> This is RMark 2.2.7
#> Documentation available at http://www.phidot.org/software/mark/rmark/RMarkDocumentation.zip
# Here is a subset of the first 10 years of the data
# The data have the format LDLDLD where L = capture/alive encounter
# and D = dead encounter. The column freq is this capture history
# frequency in the dataset
data <- tibble::tribble(
~ch, ~freq,
"11000000000000000000", 1L,
"10000000010000000000", 1L,
"10000000000000000000", 3L,
"00100000000100000000", 1L,
"00100000000000000100", 1L,
"00100000000000000000", 3L,
"00001000000000000000", 14L,
"00000010000100000000", 1L,
"00000010000000000100", 1L,
"00000010000000000000", 8L,
"00000000100001000000", 2L,
"00000000100000010000", 1L,
"00000000100000000100", 1L,
"00000000100000000000", 13L,
"00000000001000010000", 1L,
"00000000001000000000", 10L,
"00000000000011000000", 2L,
"00000000000010010000", 1L,
"00000000000010000100", 1L,
"00000000000010000000", 17L,
"00000000000000101000", 1L,
"00000000000000100100", 1L,
"00000000000000100000", 13L,
"00000000000000001000", 16L,
"00000000000000000011", 2L,
"00000000000000000010", 16L
)
# Process data into a Burnham life-dead model
data.proc <- process.data(
data = data,
model = "Burnham",
begin.time = 1960
)
# Create design data list
data.ddl <- make.design.data(data.proc,
remove.unused = T
)
# Where chekcing the desing data list, we notice that half
# the years are missing from the table. Upon running the model,
# this create a problem in the PIMS matrix
unique(data.ddl$S$Cohort)
#> [1] 0 2 4 6 8
data.ddl$S
#> par.index model.index group cohort age time occ.cohort Cohort Age Time
#> 1 1 1 1 1960 0 1960 1 0 0 0
#> 2 2 2 1 1960 1 1961 1 0 1 1
#> 3 3 3 1 1960 2 1962 1 0 2 2
#> 4 4 4 1 1960 3 1963 1 0 3 3
#> 5 5 5 1 1960 4 1964 1 0 4 4
#> 6 6 6 1 1960 5 1965 1 0 5 5
#> 7 7 7 1 1960 6 1966 1 0 6 6
#> 8 8 8 1 1960 7 1967 1 0 7 7
#> 9 9 9 1 1960 8 1968 1 0 8 8
#> 10 10 10 1 1960 9 1969 1 0 9 9
#> 20 20 20 1 1962 0 1962 3 2 0 2
#> 21 21 21 1 1962 1 1963 3 2 1 3
#> 22 22 22 1 1962 2 1964 3 2 2 4
#> 23 23 23 1 1962 3 1965 3 2 3 5
#> 24 24 24 1 1962 4 1966 3 2 4 6
#> 25 25 25 1 1962 5 1967 3 2 5 7
#> 26 26 26 1 1962 6 1968 3 2 6 8
#> 27 27 27 1 1962 7 1969 3 2 7 9
#> 35 35 35 1 1964 0 1964 5 4 0 4
#> 36 36 36 1 1964 1 1965 5 4 1 5
#> 37 37 37 1 1964 2 1966 5 4 2 6
#> 38 38 38 1 1964 3 1967 5 4 3 7
#> 39 39 39 1 1964 4 1968 5 4 4 8
#> 40 40 40 1 1964 5 1969 5 4 5 9
#> 46 46 46 1 1966 0 1966 7 6 0 6
#> 47 47 47 1 1966 1 1967 7 6 1 7
#> 48 48 48 1 1966 2 1968 7 6 2 8
#> 49 49 49 1 1966 3 1969 7 6 3 9
#> 53 53 53 1 1968 0 1968 9 8 0 8
#> 54 54 54 1 1968 1 1969 9 8 1 9Created on 2021-01-28 by the reprex package (v1.0.0)
Session info
sessioninfo::session_info()
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