gjam (version 2.6.2)

gjamPriorTemplate: Prior coefficients for gjam analysis

Description

Constructs coefficient matrices for low and high limits on the uniform prior distribution for beta.

Usage

gjamPriorTemplate(formula, xdata, ydata, lo = NULL, hi = NULL)

Arguments

formula

object of class formula, starting with ~, matches the formula passed to gjam

xdata

n x Q observation by predictor data.frame

ydata

n x Q observation by response data.frame

lo

list of lower limits

hi

list of upper limits

Value

A list containing two matrices. lo is a Q x S matrix of lower coefficient limits. hi is a Q x S matrix of upper coefficient limits. Unless specied in lo, all values in lo = -Inf. Likewise, unless specied in hi, all values in hiBeta = -Inf.

Details

The prior distribution for a coefficient beta[q,s] for predictor q and response s, is dunif(lo[q,s], hi[q,s]). gjamPriorTemplate generates these matrices. The default values are (-Inf, Inf), i.e., all values in lo equal to -Inf and hi equal to Inf. These templates can be modified by changing specific values in lo and/or hi.

Alternatively, desired lower limits can be passed as the list lo, assigned to names in xdata (same limit for all species in ydata), in ydata (same limit for all predictors in xdata), or both, separating names in xdata and ydata by "_". The same convention is used for upper limits in hi.

These matrices are supplied in as list betaPrior, which is included in modelList passed to gjam. See examples and browseVignettes('gjam').

Note that the informative prior slows computation.

References

Clark, J.S., D. Nemergut, B. Seyednasrollah, P. Turner, and S. Zhang. 2017. Generalized joint attribute modeling for biodiversity analysis: Median-zero, multivariate, multifarious data. Ecological Monographs 87, 34-56.

See Also

gjam

Examples

# NOT RUN {
library(repmis)
source_data("https://github.com/jimclarkatduke/gjam/blob/master/forestTraits.RData?raw=True")

xdata       <- forestTraits$xdata
plotByTree  <- gjamReZero(forestTraits$treesDeZero) # re-zero
traitTypes  <- forestTraits$traitTypes
specByTrait <- forestTraits$specByTrait

tmp <- gjamSpec2Trait(pbys = plotByTree, sbyt = specByTrait, 
                      tTypes = traitTypes)
tTypes <- tmp$traitTypes
traity <- tmp$plotByCWM
censor <- tmp$censor

formula <- as.formula(~ temp + deficit)
lo <- list(temp_gmPerSeed = 0, temp_dioecious = 0 ) # positive effect on seed size, dioecy
b  <- gjamPriorTemplate(formula, xdata, ydata = traity, lo = lo)

ml <- list(ng=3000, burnin=1000, typeNames = tTypes, censor = censor, betaPrior = b)
out <- gjam(formula, xdata, ydata = traity, modelList = ml)

S   <- ncol(traity)
sc  <- rep('black',S)
sc[colnames(traity) <!-- %in% c('ring','drought')] <- 'blue' -->
pl  <- list(specColor = sc)           
gjamPlot(output = out, plotPars = pl)         
# }