| resde-package {resde} | R Documentation |
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(s)
Maintainer: Oscar Garcia garcia@dasometrics.net (ORCID)
References
Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem". Computational Statistics 34(1), 23-46. doi:10.1007/s00180-018-0837-4
See Also
Useful links:
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))