sudo apt-get install jags
library(phdBayes)
model = StandardBayesian(d, "testmodel", c('subjtype','corpustype'))
SaveOriginalData(model)
CreateAndRunModel(model, list("n.chains" = 2, "sample"=70000, "adapt" = 10000, "burnin" = 9000, "thin" = 1, "method" = "parallel"))
model@datalist = LoadResults(model)
PrintSvg(model)
library(phdBayes)
model = StandardBayesian(yamlpath="/root/", dumppath="/root/")
model@datalist = LoadResults(model,lang="fi")
Here's an example script for using on a remote server:
library(stats)
library(methods)
library(phdBayes)
model = StandardBayesian(yamlpath="./",dumppath="./")
model@datalist = LoadResults(model,lang="fi")
saveRDS(model@datalist, "path/to/datalist.rds")
The plots produced by ggmcmc require a huge amount of memory, so it is probably a good idea to just use pdfs or svgs instead of actual ggplot objects.
For this purpose, the function SavePdf
can be used, e.g.:
general <- ReadData("general_new")
SavePdf(general,"std.all",2,5)
SavePdf(general,"std.interact")
SavePdf(general,"general_new", "funct")
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