April 23, 2015
install package from Github
Only needs to be done the first time
Define species groups
library(PEcAn.allometry) ## define the Plant Functional Types you want to fit based on USDA acronyms and USFS species codes (spcd) ## can involve one or many species ## multiple PFTs can be fit at once by putting them in a list ## Note that the name used in the PFT list defines the name that the code will match against when making predictions for new individuals pfts = list(FAGR = data.frame(spcd=531,acronym="FAGR")) ## example of a PFT with multiple species (LH = late hardwood) ## note that if you're just using Jenkins the acronym column is optional pfts = list(LH = data.frame(spcd = c(531,318),acronym=c("FAGR","ACSA3")))
Run the Bayesian allometry model
Note the side effects of this function are that it will create two files in your working directory for every PFT and component pair, a pdf file of diagnostics and a RData file saving the full output. These files will be named based on the PFT and the component.
The return from this function will be a summary table of statistics. The function will actually run two variants of the model, a "global" modelthat fits a single equation to all equations and a 'hierarchical' model that accounts for the variability among equations. This function also print out DIC statistics on which fit was better (lowest score wins): DIC is the hierarchical model, DICg is the global model.
allom.stats = AllomAve(pfts,ngibbs=500)
If you want to run with a response variable other than the default (e.g. components = 6; stem biomass), look up the relevant component IDs in data(allom.components). The default component is 3 (total aboveground biomass). Note that if you specify multiple PFTs (as a list) and multiple components (as a vector) then AllomAve will generate allometries for all PFT x component combinations
allom.stats = AllomAve(pfts,ngibbs=500,components=c(3,6))
Predict for individual trees
allom.fit = load.allom(getwd()) dbh = 1:50 pred = allom.predict(allom.fit,dbh = dbh,pft = "LH",component = 3,use = "Bg",interval = "prediction") conf = allom.predict(allom.fit,dbh = dbh,pft = "LH",component = 3,use = "Bg",interval = "confidence") PI = apply(pred,2,quantile,c(0.025,0.5,0.975),na.rm=TRUE) CI = apply(conf,2,quantile,c(0.025,0.5,0.975),na.rm=TRUE) plot(dbh,CI[2,],type='l',lwd=3,ylim=range(PI),ylab="Biomass (kg)") lines(dbh,CI[1,],lty=2,col="blue") lines(dbh,CI[3,],lty=2,col="blue") lines(dbh,PI[1,],lty=3,col="red") lines(dbh,PI[3,],lty=3,col="red")
Predict for a stand
## simulated DBH's dbh = rpois(100,20) hist(dbh) stand = allom.predict(allom.fit,dbh = dbh,pft = "LH",component = 3,use = "Bg",interval = "prediction") AGB = apply(stand,1,sum) hist(AGB)