# Prior coefficients for gjam analysis Constructs coefficient matrices for low and high limits on the uniform prior distribution for `beta`. ```r 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 ## Returns 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. ## Author(s) James S Clark, jimclark@duke.edu ## 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 ```r ## 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) pl <- list(specColor = sc) gjamPlot(output = out, plotPars = pl) ## End(Not run) ```

gjamPriorTemplate function