makePostGrowthObjs: Make list of growth objects corresponding to posterior of Bayes fit

Description

Model takes a data-frame and fits a Bayesian model to the chosen "responseType" (sizeNext, incr, etc) using the chosen explanatory variable, and the package MCMCglmm; then creates the resulting list of growth objects necessary to get Bayes posteriors of IPM outputs. Note that priors are not yet implemented and growth methods for multiple covariates likewise.

Usage

makePostGrowthObjs(dataf, explanatoryVariables,responseType,
meanB = rep(0, 3), varB = rep(1e+10), burnin=3000,nitt = 50000)

Arguments

dataf

a dataframe with columns `size', and `sizeNext' (`size' is size at t, `sizeNext' is size at t+1); facultatively, dataf may include `covariate' (discrete or continuous) or other covariates

explanatoryVariables

a formula describing the desired explanatory variables (interactions, etc) in classical R style, i.e. separated by `+', `*', `:'. Possible covariates include `size', 'size2' (size^2), `size3' (size^3),`logsize' (log(size)), `logsize2' (log(siz

responseType

possible values include `sizeNext' (size in one discrete time step) and `incr' and `logincr'

meanB

vector defining the means of the priors of the coefficients for growth (should be the same length as desired coeff). CURRENTLY NOT IMPLEMENTED.

varB

vector defining the variance of the prior of the coeff for growth, as for the mean. CURRENTLY NOT IMPLEMENTED.

burnin

numeric defining burn in for MCMCglmm

nitt

numeric defining the number of iterations

Value

a list of growth objects

Details

Note that the use of priors is not yet implemented, and the function does not return output of Bayes analysis which might be useful for assessment; in development.

Examples

Run this code

dff <- generateData()
grlist <- makePostGrowthObjs(dataf=dff,
			explanatoryVariables="size+size2+covariate",
            responseType="sizeNext",
            meanB=rep(0,3),varB=rep(1e10),burnin=100,nitt = 500)
            
grlist[[1]]