Description Usage Arguments Details Value Author(s) References See Also Examples
Performs a Monte Carlo search for optimal initial conditions iterative maximization algorithms (MARSSkem
and MARSSoptim
). This is a utility function in the MARSS-package
.
1 | MARSSmcinit(MLEobj)
|
MLEobj |
An object of class |
It is recommended that initial parameter values be set using MARSSmcinit()
, particularly if the model is not a good fit to the data. This requires more compuation time, but reduces the chance of the algorithm terminating at a local maximum and not reaching the true MLEs.
Options for MARSSmcinit()
may be set using MLEobj$control
, as follows:
MLEobj$control$numInits
Number of random initial value draws.
MLEobj$control$numInitSteps
Maximum number of EM iterations for each random initial value draw.
MLEobj$control$boundsInits
Length 6 list. Each component is a length 2 vector of bounds on the uniform distributions from which initial values will be drawn (for A, B, U, and Z). For R and Q, variance-covariance matrices are generated from a wishart distribution with df=bound[1] and S=diag(bound[2],m). Note, random initial conditions are only used for parameters that are not fixed.
The default values for these are given in MARSSsettings.R
and listed in MARSS
.
A list with 8 matrices Z, A, R, B, U, Q, x0, V0, specifying initial values for parameters for iteration 1 of the EM algorithm. Note the output is the initial values for a marssMODEL in marss form.
Eli Holmes and Eric Ward, NOAA, Seattle, USA.
eli(dot)holmes(at)noaa(dot)gov, eric(dot)ward(at)noaa(dot)gov
The user guide: Holmes, E. E., E. J. Ward, and M. D. Scheuerell (2012) Analysis of multivariate time-series using the MARSS package. NOAA Fisheries, Northwest Fisheries Science
Center, 2725 Montlake Blvd E., Seattle, WA 98112 Type RShowDoc("UserGuide",package="MARSS")
to open a copy.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ## Not run:
#Note doing a Monte-Carlo search takes a long, long time
dat = t(harborSeal)
dat = dat[c(2,nrow(dat)),]
fit1=MARSS(dat, control=list(MCInit=TRUE))
fit1
#Show the inits that were used
fit1$start
#Try fewer initial start locations
#and different mean variance (0.1 instead of 1) for R and Q
cntl.list = list(MCInit=TRUE, numInits=10,
numInitSteps = 10,
boundsInits=list(Q=c(1,0.1),R=c(1,0.1)))
fit2=MARSS(dat, control=cntl.list)
fit2
#Show the inits that were used
fit2$start
#ignore the values for Z,B, and V0; those parameters are fixed
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.