THREE BIG THINGS THIS WEEK
The FIMS case study will be presented to the Scientific Review Group as additional research. Spoiler alert, depending on what you assume for selectivity, the end-year population dynamics are very different but the rest of the time series matches pretty well.
Bai is working on the comparisons of FIMS to other modeling frameworks fit to the data from the operating model used for the model comparison project. Her most recent addition was adding a FIMS run with random effects for recruitment deviations. Great work Bai!
Lots of side meetings are happening for FIMS. One of those was regarding the design of wrapper functions for random effects and other questions related to random effects.
FIMS ANNOUNCEMENTS
- Please look at the FIMS calendar on a regular basis. There are lots of meetings happening that you can attend but global announcements are no longer going out in an attempt to save your inboxes from overload. One of those such meetings is the Code Club happening this Friday.
- If you attended the FIMS Training on Monday, please fill out the survey on Google Cloud Workstations.
UPCOMING EVENTS
Friday, February 13
FIMS Code Club
Time: 12:00–15:00 E; 09:00–12:00 P; 08:00–11:00 AK; 06:00-09:00 H Location: Virtual Online: Google meet
PHOTO OF THE WEEK
In the dev-hake branch, we now have the ability to estimate parameters using a time-varying vector. Below is code to set the initial values and estimation type. We need to decide as a group what would be the best way for non-power users to interact with time-varying parameters.
# Resize the parameter vector
fleet_selectivity$inflection_point$resize(get_n_years(data_4_model))
# Set the initial value of each element of the vector to be one and
# estimate it as a fixed effect
for (ii in seq(get_n_years(data_4_model))) {
fleet_selectivity$inflection_point[ii]$value <- 1
fleet_selectivity$inflection_point[ii]$estimation_type$set("fixed_effects")
}