findOptimum: Optimization functions.
In eeholmes/DM: Dynamic Model
Description
Usage
Arguments
Details
Value
Description
Because the likelihood surfaces for these problems often has
a severe bananna shape with a poorly defined maximum finding
an optimum is often non-trivial If an optimum is given in the
input file I use that as an initial value for nlm otherwise,
I found that nlm often gets stuck in a local min. So here I
have iterated between a genetic optimization algorithm and nlm.
This works more often but still at times misses the optimum.
This is adhoc.
The covariates are log-transformed and centered if input$centerMS=TRUE or input$centerFlow=TRUE.
Usage
1
findOptimum(dat, input, silent = FALSE)
Arguments
dat
data from the A & P file
input
a list with the other values needed for a DM run.
silent
(TRUE/FALSE)
Details
In SRFunctions(), bev-holt is defined as
S/(S*exp(-p[2])+exp(-p[1]))*exp(p[3]*logMS)*exp(p[4]*logFlow)
In DM (writeBUGSmodel.R),
R = [S/( (S/exp(logCap)) + (1/prod) )] exp(marineInd*logMS) exp(flowCoef*logFlow)
so
p[1] = log(prod), constrained to be positive
p[2] = log(cap), constrained to be positive
p[3] = msCoef,
p[4] = flowCoef
Value
A list. $estimate parameters at the minimum sum of squared residuals. The parameters are prod, cap, msCoef, flowCoef. $value is the sum of squared residuals at the minimum.
eeholmes/DM documentation built on May 26, 2019, 3:36 p.m.
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Description Usage Arguments Details Value
Description
Because the likelihood surfaces for these problems often has a severe bananna shape with a poorly defined maximum finding an optimum is often non-trivial If an optimum is given in the input file I use that as an initial value for nlm otherwise, I found that nlm often gets stuck in a local min. So here I have iterated between a genetic optimization algorithm and nlm. This works more often but still at times misses the optimum. This is adhoc.
The covariates are log-transformed and centered if input$centerMS=TRUE or input$centerFlow=TRUE.
Usage
1 | findOptimum(dat, input, silent = FALSE)
|
Arguments
dat |
data from the A & P file |
input |
a list with the other values needed for a DM run. |
silent |
(TRUE/FALSE) |
Details
In SRFunctions(), bev-holt is defined as S/(S*exp(-p[2])+exp(-p[1]))*exp(p[3]*logMS)*exp(p[4]*logFlow) In DM (writeBUGSmodel.R), R = [S/( (S/exp(logCap)) + (1/prod) )] exp(marineInd*logMS) exp(flowCoef*logFlow) so p[1] = log(prod), constrained to be positive p[2] = log(cap), constrained to be positive p[3] = msCoef, p[4] = flowCoef
Value
A list. $estimate parameters at the minimum sum of squared residuals. The parameters are prod, cap, msCoef, flowCoef. $value is the sum of squared residuals at the minimum.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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