pecan.ma.RdRuns heirarchical meta-analysis of plant trait data
pecan.ma(trait.data, prior.distns, taupriors, j.iter, outdir, random = FALSE, overdispersed = TRUE)
| trait.data | list of dataframes, one per trait for which data is available, generated by call to query.traits() |
|---|---|
| prior.distns | dataframe of prior distributions generated by call to query.priors() |
| taupriors | priors on variance parameters, can be scaled as needed with data mean |
| j.iter | number of mcmc samples |
| outdir | output directory |
| random | use random effects, FALSE by default |
| overdispersed | TRUE by default, if set to FALSE, data mean will be used as starting point for MCMC chains (use with caution) |
four chains with 5000 total samples from posterior
pecan.ma runs a heirarchical Bayes meta-analytical model.
This model combines prior information with data from studies on the particular species or group of interest.
Data that is incorporated into the meta-analysis include the mean (Y), sample size (n),
and precision (obs.prec).
Where a set of data includes more than one level of treatment, comes from more than one site,
or comes from both field and greenhouse studies, these variables are included as random (treatment, site)
or fixed (greenhouse) effects.
The pecan.ma function writes a model for each specific data set and prior using the write.ma.model()
function to modify the ma.model.template.bug generic model.
# NOT RUN { prior.distns <- query.priors('ebifarm.c4crop', c('SLA', 'c2n_leaf')) trait.data <- query.traits('938', c('SLA', 'c2n_leaf')) pecan.ma(prior.distns, trait.data, 25000) # }