resde (version 1.1)

resde-package: resde - Parameter estimation in reducible SDE models.

Description

The main functions for model fitting are sdemodel() and sdefit(). First, specify the model structure in sdemodel(), including the variable transformation, any re-parameterizations, initial condition, and the presence or not of process, measurement, and initial condition noise. Then, fit the model with sdefit(), indicating the data to be used and starting parameter values for the iterations. For hierarchical models, one must also indicate which are the global and local parameters, and if fixed locals or a mixed effects method should be used.

Some auxilliary functions include the Box-Cox transformation bc(), and the unified transformation unitran().

For detailed usage see the vignette: vignette("resde-vignette", package="resde").

Author

Maintainer: Oscar Garcia (ORCID)

References

Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem". Computational Statistics 34(1), 23-46. tools:::Rd_expr_doi("10.1007/s00180-018-0837-4")

See Also

Useful links:

• https://github.com/ogarciav/resde/

• Report bugs at https://github.com/ogarciav/resde/issues

Examples

# Richards model  dH^c = b(a^c - H^c) dt + s dW for tree heights
tree1 <- subset(Loblolly, Seed == Seed[1]) # first tree
m <- sdemodel(~x^c, beta0=~b*a^c, beta1=~-b, mum=0) # no measurement error
sdefit(m, x="height", t="age", data=tree1, start=c(a=70, b=0.1, c=0.5))