Intermediate year configuration #182
Replies: 3 comments
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Hi Martina, if I understand you issue correctly you just need to put in as data all catches and indices, using Some code to do what you want using example data that should be like yours, notice the library(spict)
#> Loading required package: TMB
#> Welcome to spict_v1.3.8
# Some data, last year fot index is NA
inp <- list(obsC = c(93.51, 212.444, 195.032, 382.712, 320.43, 402.467,
365.557, 606.084, 377.642, 318.836, 309.374, 389.02, 276.901,
254.251, 170.006, 97.181, 90.523, 176.532, 214.181, 228.672,
212.177, 231.179, 136.942, 212),
timeC = c(1999, 2000, 2001,
2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022),
obsI = c(1.78, 1.31, 0.91, 0.96, 0.88, 0.9, 0.87, 0.72, 0.57,
0.45, 0.42, 0.42, 0.49, 0.43, 0.4, 0.45, 0.55, 0.53, 0.58,
0.64, 0.66, 0.65, 0.61, NA),
timeI = c(1999, 2000, 2001,
2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021,
2022),
dteuler = 0.25)
inp$maninterval <- c(2024, 2025) ## Management period is the whole year 2024
inp$maneval <- 2025 ## Management evaluation is end of 2024 (same as beginning of 2025)
inp <- check.inp(inp)
#> Removing zero, negative, and NAs in I series 1
fit <- fit.spict(inp)
fit <- manage(fit)
#> Selected scenario(s): currentCatch, currentF, Fmsy, noF, reduceF25, increaseF25, msyHockeyStick, ices
sumspict.manage(fit)
#> SPiCT timeline:
#>
#> Observations Intermediate Management
#> 1999.00 - 2023.00 2023.00 - 2024.00 2024.00 - 2025.00
#> |-----------------------| ----------------------| ----------------------|
#>
#> Management evaluation: 2025.00
#>
#> Predicted catch for management period and states at management evaluation time:
#>
#> C B/Bmsy F/Fmsy
#> 1. Keep current catch 197.5 1.29 0.58
#> 2. Keep current F 215.3 1.28 0.63
#> 3. Fish at Fmsy 331.9 1.21 1.00
#> 4. No fishing 0.2 1.41 0.00
#> 5. Reduce F by 25% 163.1 1.31 0.48
#> 6. Increase F by 25% 266.4 1.25 0.79
#> 7. MSY hockey-stick rule 331.9 1.21 1.00
#> 8. ICES advice rule 274.8 1.24 0.82Created on 2023-10-19 with reprex v2.0.2 Hope this helps, |
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Dear Alex, thank you for your reply. However, this is not correctly representing my case. The starting point was avoiding introducing NA in obsI, and the consequent reestimation of the different parameters. In fact, "the decision not to re-evaluate the model parameters offers the advantage of ensuring that model forecasts remain in line with the comprehensive assessment model validated in the previous year. Additionally, it eliminates the possibility of introducing any bias or inaccurate model parameter estimates, particularly in situations where essential data might be absent, such as during survey failures." Given that, I had to use the 2021 validated assessment and forecast it. To give advice for 2022 I used the “fit.spict” function and I iteratively changed the I wanted to forecast to 2025 this "new" assessment, but I did not manage to do it in the proper way (as already explained above). Thank you again, |
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Hi Martina, You can try setting the intermediate year catch for 2022 and library(spict)
#> Loading required package: TMB
#> Welcome to spict_v1.3.8@0f4327
inp2 <- list(obsC = c(93.51, 212.444, 195.032, 382.712, 320.43, 402.467,
365.557, 606.084, 377.642, 318.836, 309.374, 389.02, 276.901,
254.251, 170.006, 97.181, 90.523, 176.532, 214.181, 228.672,
212.177, 231.179, 136.942),
timeC = c(1999, 2000, 2001,
2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012,
2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021),
obsI = c(1.78, 1.31, 0.91, 0.96, 0.88, 0.9, 0.87, 0.72, 0.57,
0.45, 0.42, 0.42, 0.49, 0.43, 0.4, 0.45, 0.55, 0.53, 0.58,
0.64, 0.66, 0.65, 0.61),
timeI = c(1999, 2000, 2001,
2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011,
2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021),
dteuler = 0.25)
inp2$maninterval <- c(2024, 2025) ## Management period is the whole year 2024
inp2$maneval <- 2025 ## Management evaluation is end of 2024 (same as beginning of 2025)
inp2 <- check.inp(inp2)
fit2 <- fit.spict(inp2)
fit2 <- manage(fit2, intermediatePeriodCatchList = list(obsC = c(212, NA),
timeC = c(2022, 2023),
dtc = c(1, 1)))
#> Selected scenario(s): currentCatch, currentF, Fmsy, noF, reduceF25, increaseF25, msyHockeyStick, ices
sumspict.manage(fit2)
#> SPiCT timeline:
#>
#> Observations Intermediate Management
#> 1999.00 - 2022.00 2022.00 - 2024.00 2024.00 - 2025.00
#> |-----------------------| ----------------------| ----------------------|
#>
#> Management evaluation: 2025.00
#>
#> Predicted catch for management period and states at management evaluation time:
#>
#> C B/Bmsy F/Fmsy
#> 1. Keep current catch 151.6 1.32 0.44
#> 2. Keep current F 158.1 1.38 0.44
#> 3. Fish at Fmsy 349.7 1.26 1.00
#> 4. No fishing 0.2 1.48 0.00
#> 5. Reduce F by 25% 119.4 1.40 0.33
#> 6. Increase F by 25% 196.3 1.36 0.55
#> 7. MSY hockey-stick rule 349.7 1.26 1.00
#> 8. ICES advice rule 274.6 1.31 0.77Created on 2023-10-20 with reprex v2.0.2 |
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Hi all!
I am wondering if there is any possible option to mimic through spict the situation that the stock I am assessing is facing.
In 2022 we missed the survey but we have the reported catches.
To obtain the forecast with different management scenarios, I tried to use the argument intermediatePeriodCatchList of the manage function. My idea was to set the intermediate period for 2022 and 2023 using for 2022 the real catches and letting 2023 estimate all the values based on fstatusquo of 2022, but this approach was not successful.
Do you have any suggestion on how to face this situation?
If the question was not clear please let me know.
Thank you in advance,
Martina
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