View source: R/mastifFunctions.R
mastPriors | R Documentation |
Prior parameter values may be saved in a file by species or by genus. mastPriors
looks for a species-level prior first. If not found, it can substutitute a genus-level prior.
mastPriors(file, specNames, code, genus = 'NULL')
file |
|
specNames |
|
code |
|
genus |
|
The file
includes rows with genera, given in column "genus", or "species". Species rows also have an entry for genus, with the species code given in the column named code
. Additional columns are names of prior parameters, including:
priorDist
: mean parameter for dispersal kernel (m), related to kernel parameter u
as d <- pi*sqrt(u)/2
. The estimated values for these parameters are found in output$parameters$upars
and output$parameters$dpars
, where output
is an object fitted by mastif
.
minDist
: the lower bound for the mean parameter d
of the dispersal kernel (m).
maxDist
: the upper bound for the mean parameter d
of the dispersal kernel (m).
priorVDist
: variance on the mean parameter for dispersal kernel (m^2). For large values, the prior distribution of d
(and by variable change, u
) becomes dunif(d, minDist, maxDist)
.
minDiam
: below this diameter trees of unknown status are assumed immature (cm).
maxDiam
: above this diameter trees of unknown status are assumed mature (cm).
maxFec
: maximum seeds per tree per year
More detailed vignettes can be obtained with: browseVignettes('mastif')
A data.frame
with a row for each specNames
and columns for prior parameter values. Where file
contains species-level parameter values, they will be used. If a separate row in file
holds genus-level parameters, with the entry for code == 'NA'
, then genus-level parameters will be substituted. In other words, these genus rows are default values.
James S Clark, jimclark@duke.edu
Clark, J.S., C. Nunes, and B. Tomasek. 2019. Foodwebs based on unreliable foundations: spatio-temporal masting merged with consumer movement, storage, and diet. Ecological Monographs, e01381.
mastFillCensus
to fill tree census
mastif
for analysis
A more detailed vignette is can be obtained with:
browseVignettes('mastif')
website 'http://sites.nicholas.duke.edu/clarklab/code/'.
d <- "https://github.com/jimclarkatduke/mast/blob/master/pinusExample.rdata?raw=True" repmis::source_data(d) # prior parameter values pfile <- tempfile(fileext = '.txt') d <- "https://github.com/jimclarkatduke/mast/blob/master/priorParameters.txt?raw=True" download.file(d, destfile = pfile) specNames <- c("pinuEchi","pinuRigi","pinuStro","pinuTaed","pinuVirg") seedNames <- c(specNames, "pinuUNKN") priorTable <- mastPriors(file = pfile, specNames, code = 'code4', genus = 'pinus') inputs <- list( specNames = specNames, seedNames = seedNames, treeData = treeData, seedData = seedData, xytree = xytree, xytrap = xytrap, priorTable = priorTable, seedTraits = seedTraits) formulaRep <- as.formula( ~ diam ) formulaFec <- as.formula( ~ diam ) output <- mastif(inputs = inputs, formulaFec, formulaRep, ng = 1000, burnin = 400)
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