Module to fit a common power-law allometric model to a mixture of raw data and allometric equations in a Heirarchical Bayes framework with multiple imputation of the allometric data

allom.BayesFit(allom, nrep = 10000, form = "power", dmin = 0.1,
  dmax = 500)



- object (usually generated by which needs to be a list with two entries: 'field' - contains a list, each entry for which is a data frame with 'x' and 'y'. Can be NULL 'parm' - a single data frame with the following components:

  • n sample size

  • a eqn coefficient

  • b eqn coefficient

  • c eqn coefficient

  • d eqn coefficient

  • e eqn coefficient

  • se standard error

  • eqn sample size

  • Xmin smallest tree sampled (cm)

  • Xmax largest tree sampled (cm)

  • Xcor units correction on X

  • Ycor units correction on Y

  • Xtype type of measurement on the X

  • spp - USFS species code


- number of MCMC replicates


functional form of the allometry: 'power' vs 'exp'


minimum dbh of interest


maximum dbh of interest


returns MCMC chain and ONE instance of 'data' note: in many cases the estimates are multiply imputed


dependencies: requires MCMCpack and mvtnorm

note: runs 1 chain, but multiple chains can be simulated by multiple function calls