# Create Bayesian network Create Bayesian network (grain objects (graphical independence network)). ```r grain(x, ...) ## S3 method for class 'cpt_spec' grain(x, control = list(), smooth = 0, compile = TRUE, details = 0, ...) ## S3 method for class 'CPTspec' grain(x, control = list(), smooth = 0, compile = TRUE, details = 0, ...) ## S3 method for class 'pot_spec' grain(x, control = list(), smooth = 0, compile = TRUE, details = 0, ...) ## S3 method for class 'igraph' grain( x, control = list(), smooth = 0, compile = TRUE, details = 0, data = NULL, ... ) ## S3 method for class 'dModel' grain( x, control = list(), smooth = 0, compile = TRUE, details = 0, data = NULL, ... ) ``` ## Arguments - `x`: An argument to build an independence network from. Typically a list of conditional probability tables, a DAG or an undirected graph. In the two latter cases, data must also be provided. - `...`: Additional arguments, currently not used. - `control`: A list defining controls, see 'details' below. - `smooth`: A (usually small) number to add to the counts of a table if the grain is built from a graph plus a dataset. - `compile`: Should network be compiled. - `details`: Debugging information. - `data`: An optional data set (currently must be an array/table) ## Returns An object of class "grain" ## Details If 'smooth' is non-zero then entries of 'values' which a zero are replaced by the value of 'smooth' - BEFORE any normalization takes place. ## Note A change from earlier versions of this package is that grain objects are now compiled upon creation. ## Examples ```r ## Create network from conditional probability tables CPTs: yn <- c("yes", "no") a <- cpt(~asia, values=c(1,99), levels=yn) t.a <- cpt(~tub + asia, values=c(5,95,1,99), levels=yn) s <- cpt(~smoke, values=c(5,5), levels=yn) l.s <- cpt(~lung + smoke, values=c(1,9,1,99), levels=yn) b.s <- cpt(~bronc + smoke, values=c(6,4,3,7), levels=yn) e.lt <- cpt(~either + lung + tub, values=c(1,0,1,0,1,0,0,1), levels=yn) x.e <- cpt(~xray + either, values=c(98,2,5,95), levels=yn) d.be <- cpt(~dysp + bronc + either, values=c(9,1,7,3,8,2,1,9), levels=yn) cpt_list <- list(a, t.a, s, l.s, b.s, e.lt, x.e, d.be) chest_cpt <- compileCPT(cpt_list) ## Alternative: chest_cpt <- compileCPT(a, t.a, s, l.s, b.s, e.lt, x.e, d.be) chest_bn <- grain(chest_cpt) ## Create network from data and graph specification. data(lizard, package="gRbase") ## From a DAG: height <- species -> diam daG <- dag(~species + height:species + diam:species) ## From an undirected graph UG : [height:species][diam:species] uG <- ug(~height:species + diam:species) liz_ug <- grain(uG, data=lizard) liz_dag <- grain(daG, data=lizard) ``` ## References Søren Højsgaard (2012). Graphical Independence Networks with the gRain Package for R. Journal of Statistical Software, 46(10), 1-26. [https://www.jstatsoft.org/v46/i10/](https://www.jstatsoft.org/v46/i10/). ## See Also `cptable`, `compile.grain`, `propagate.grain`, `setFinding`, `setEvidence`, `getFinding`, `pFinding`, `retractFinding`, `extract_cpt`, `extract_pot`, `compileCPT`, `compilePOT` ## Author(s) Søren Højsgaard, sorenh@math.aau.dk

grain-main function