MARSS (version 3.2)

MARSSparamCIs: Confidence Intervals for MARSS Parameters

Description

Computes confidence intervals for the maximum-likelihood estimates of MARSS model parameters. This is a base function in the MARSS-package.

Usage

MARSSparamCIs(MLEobj, method = "hessian", alpha = 0.05, nboot=1000, fun="MARSSkf")

Arguments

MLEobj

An object of class marssMLE. Must have a $par element containing the MLE parameter estimates.

method

Method for calculating the standard errors: "hessian", "parametric", and "innovations" implemented currently.

alpha

alpha level for the 1-alpha confidence intervals.

nboot

Number of bootstraps to use for "parametric" and "innovations" methods.

fun

The function to use to compute the log-likelihood for "hessian" method.

Value

• MARSSparamCIs returns the marssMLE object passed in, with additional components par.se, par.upCI, par.lowCI, par.CI.alpha, par.CI.method, par.CI.nboot and par.bias (if method is "parametric" or "innovations").

Details

Approximate confidence intervals (CIs) on the model parameters may be calculated from the Hessian matrix (the matrix of partial 2nd derivatives of the parameter estimates) or parametric or non-parametric (innovations) bootstrapping using nboot bootstraps. The Hessian CIs are based on the asymptotic normality of MLE parameters under a large-sample approximation. Bootstrap estimates of parameter bias are reported if method "parametric" or "innovations" is specified.

References

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.

See Also

MARSSboot MARSSinnovationsboot MARSShessian

Examples

dat = t(harborSealWA)
  dat = dat[2:4,]
  kem = MARSS(dat, model=list(Z=factor(c(1,1,1)), 
     R="diagonal and unequal"))
  kem.with.CIs.from.hessian = MARSSparamCIs(kem)
  kem.with.CIs.from.hessian