Modeling parameters in stock synthesis
Download
1 / 12

Modeling Parameters in Stock Synthesis - PowerPoint PPT Presentation


  • 106 Views
  • Uploaded on

Modeling Parameters in Stock Synthesis. Modeling population processes 2009 IATTC workshop. Outline. General framework Bounds and priors Temporal variation Relationship among parameters. General framework. All parameter inputs have 14 or 7 elements

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Modeling Parameters in Stock Synthesis' - branden-long


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Modeling parameters in stock synthesis

Modeling Parameters in Stock Synthesis

Modeling population processes2009 IATTC workshop


Outline
Outline

  • General framework

  • Bounds and priors

  • Temporal variation

  • Relationship among parameters


General framework
General framework

  • All parameter inputs have 14 or 7 elements

  • First 7: bounds, init value, prior info, phase

  • Next 7: advanced options for time variation

  • Conditional inputs depending on options

#_LO HI INIT PRIOR PR_type SD PHASE env-var use_dev dev_minyr dev_maxyr dev_stddev Block Block_Fxn # Label

0.05 0.15 0.1 0.1 0 0.8 -3 0 0 0 0 0.5 0 0 # NatM_p_1_Fem_GP_1

-3 3 0 0 0 0.8 -3 0 0 0 0 0.5 0 0 # NatM_p_2_Fem_GP_1

10 45 36.0 36.0 0 10 2 0 0 0 0 0.5 0 0 # L_at_Amin_Fem_GP_1

40 90 70.0 70.0 0 10 2 0 0 0 0 0.5 0 0 # L_at_Amax_Fem_GP_1

0.05 0.25 0.15 0.15 0 0.8 3 0 0 0 0 0.5 0 0 # VonBert_K_Fem_GP_1

0.05 0.25 0.1 0.1 0 0.8 -3 0 0 0 0 0.5 0 0 # CV_young_Fem_GP_1

-3 3 0.25 0.25 0 0.8 -3 0 0 0 0 0.5 0 0 # CV_old_Fem_GP_1

-3 3 0 0 0 0.8 -3 0 0 0 0 0.5 0 0 # NatM_p_1_Mal_GP_1

-3 3 0 0 0 0.8 -3 0 0 0 0 0.5 0 0 # NatM_p_2_Mal_GP_1

-3 3 0 0 0 0.8 -3 0 0 0 0 0.5 0 0 # L_at_Amin_Mal_GP_1

-3 3 0 0 0 0.8 -2 0 0 0 0 0.5 0 0 # L_at_Amax_Mal_GP_1

-3 3 0 0 0 0.8 -3 0 0 0 0 0.5 0 0 # VonBert_K_Mal_GP_1

-3 3 0 0 0 0.8 -3 0 0 0 0 0.5 0 0 # CV_young_Mal_GP_1

-3 3 0.25 0.25 0 0.8 -3 0 0 0 0 0.5 0 0 # CV_old_Mal_GP_1

-3 3 2.0e-06 2.0e-06 0 0.8 -3 0 0 0 0 0.5 0 0 # Wtlen_1_Fem

-3 4 3.0 3.0 0 0.8 -3 0 0 0 0 0.5 0 0 # Wtlen_2_Fem

50 60 55 55 0 0.8 -3 0 0 0 0 0.5 0 0 # Mat50%_Fem

-3 3 -0.25 -0.25 0 0.8 -3 0 0 0 0 0.5 0 0 # Mat_slope_Fem

-3 3 1 1 0 0.8 -3 0 0 0 0 0.5 0 0 # Eg/gm_inter_Fem


Bounds and priors
Bounds and priors

  • All parameters bounded

  • Prior options: uniform, normal, lognormal, symmetric and non-symmetric beta


Soft bounds
Soft bounds

  • Optional penalty (set in starter file) applied to all parameters

  • Keeps ADMB from getting stuck on bounds

  • Acts along with user-specified priors

  • Equivalent to symmetric beta with shape parameter = 0.001


Temporal variation
Temporal variation

Deviations (N std. dev. pars.)

Blocks (1 par. per block)

Random walk (N -1 std. dev. pars.)

Trend (3 pars.)


Temporal variation blocks
Temporal variation: blocks

  • Requires conditional input for extra parameters lines (same as other variation types)

  • Fixed time intervals specified in control file

  • Additional parameters may be:

    • Multiplicative offset from base value

    • Additive offset from base value

    • Replace base value for interval of years

    • May have random walk from one block to next


Temporal variation deviations
Temporal variation: deviations

  • Defined by

    • Type (base+dev or base∙edev)

    • Start and end years for

    • Normal distribution penalty

  • Not zero-centered

Temporal variation: random walk

  • Similar to deviations, but one fewer parameter

  • Parameters represent differences

  • Normal distribution penalty


Temporal variation trends
Temporal variation: trends

  • Only 3 parameters

  • Smooth alternative to blocks for cases that don’t support many parameters

  • Final value may be offset from base or new value


Parameter as function of covariate
Parameter as function of covariate

  • Environmental variable: Ey

    • Pary = base+link∙Ey or base∙eEy

    • May be combined with other options (i.e. deviations around environmental index)

  • Covariate relationship to be used in future versions of SS for density dependence:

    • Mortality parameters as a function of biomass


Keeping time varying parameters within bounds
Keeping time-varying parameters within bounds

Options:

  • time varying parameters unconstrained by bounds on base parameter

  • logistic transformation to keep adjusted parameter value within bounds of base


Offsets from other parameters
Offsets from other parameters

  • Parameters for males often treated as offsets from females

    • growth

    • mortality

    • selectivity

  • Additive or multiplicative options

  • Makes hypothesis testing easy

  • To be covered in more detail in upcoming sessions of IATTC workshop