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
Arguments
- allom
- object (usually generated by query.allom.data) 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
- nrep
- number of MCMC replicates
- form
functional form of the allometry: 'power' vs 'exp'
- dmin
minimum dbh of interest
- dmax
maximum dbh of interest
Value
returns MCMC chain and ONE instance of 'data' note: in many cases the estimates are multiply imputed