updated package following CRAN submission

man/bsc-compute-asia.Rd

@@ -15,7 +15,6 @@
      }}
 
 \examples{
-\dontrun{
 ## This data set was generated using the following code:
 library(bnlearn) #for the dataset
 library(abn) #for the cache of score function
@@ -44,18 +43,7 @@ bsc.compute.asia <- buildscorecache(data.df = asia,
                                  data.dists = dist.asia,
                                  max.parents = 2)
 
-mcmc.out.asia <- mcmcabn(score.cache = bsc.compute.asia,
-                  score = "mlik",
-                  data.dists = dist.asia,
-                  max.parents = 2,
-                  mcmc.scheme = c(1000,99,1000),
-                  seed = 42,
-                  verbose = FALSE,
-                  start.dag = "random",
-                  prob.rev = 0.03,
-                  prob.mbr = 0.03,
-                  prior.choice = 2)
-}
+
 }
 
 \keyword{datasets}

man/dist-asia.Rd

@@ -15,7 +15,6 @@
      }}
 
 \examples{
-\dontrun{
 ## This data set was generated using the following code:
 library(bnlearn) #for the dataset
 
@@ -39,6 +38,5 @@ dist.asia <- list(Asia = "binomial",
             XRay = "binomial",
             Dyspnea = "binomial")
 }
-}
 
 \keyword{datasets}

man/mcmc-out-asia.Rd

@@ -15,7 +15,7 @@
      }}
 
 \examples{
-\dontrun{
+\donttest{
 ## This data set was generated using the following code:
 library(bnlearn) #for the dataset
 library(abn) #for the cache of scores computing function

man/mcmc.Rd

@@ -91,16 +91,23 @@ Scutari, M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journ
 
 
 \examples{
-\dontrun{
 ## Example from the asia dataset from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010)
 
+# the number of MCMC run is delibaretelly chosen too small (computing time)
+# no thinning (usually not recommended)
+# no burn-in (usually not recommended,
+# even if not supported by any theoretical arguments)
+
 data("mcmc_run_asia")
 
-mcmc.out.asia <- mcmcabn(score.cache = bsc.compute.asia,
+# let us run: 0.03 REV, 0.03 MBR, 0.94 MC3 MCMC jumps
+# with a random DAG as starting point
+
+mcmc.out.asia.small <- mcmcabn(score.cache = bsc.compute.asia,
                   score = "mlik",
                   data.dists = dist.asia,
                   max.parents = 2,
-                  mcmc.scheme = c(1000,99,10000),
+                  mcmc.scheme = c(100,0,0),
                   seed = 42,
                   verbose = FALSE,
                   start.dag = "random",
@@ -108,5 +115,66 @@ mcmc.out.asia <- mcmcabn(score.cache = bsc.compute.asia,
                   prob.mbr = 0.03,
                   prior.choice = 2)
 
-summary(mcmc.out.asia)
-}}
+summary(mcmc.out.asia.small)
+
+# Uniquelly with MC3 moves
+
+mcmc.out.asia.small <- mcmcabn(score.cache = bsc.compute.asia,
+                  score = "mlik",
+                  data.dists = dist.asia,
+                  max.parents = 2,
+                  mcmc.scheme = c(100,0,0),
+                  seed = 42,
+                  verbose = FALSE,
+                  start.dag = "random",
+                  prob.rev = 0,
+                  prob.mbr = 0,
+                  prior.choice = 2)
+
+summary(mcmc.out.asia.small)
+
+#let us define a starting DAG (empty matrix = no arcs)
+startDag <- matrix(data = 0,nrow = 8,ncol = 8)
+#name it
+colnames(startDag) <- rownames(startDag) <- names(dist.asia)
+# Additionally, let us use the non informative prior
+
+mcmc.out.asia.small <- mcmcabn(score.cache = bsc.compute.asia,
+                  score = "mlik",
+                  data.dists = dist.asia,
+                  max.parents = 2,
+                  mcmc.scheme = c(100,0,0),
+                  seed = 42,
+                  verbose = FALSE,
+                  start.dag = startDag,
+                  prob.rev = 0,
+                  prob.mbr = 0,
+                  prior.choice = 1)
+
+summary(mcmc.out.asia.small)
+
+# let us define our very own prior
+# we know that there should be a link between Smoking and LungCancer nodes
+
+# empty matrix
+priorDag <- matrix(data = 0,nrow = 8,ncol = 8)
+# name it
+colnames(priorDag) <- rownames(priorDag) <- names(dist.asia)
+# parent = smoking; child = LungCancer
+priorDag["LungCancer","Smoking"] <- 1
+
+mcmc.out.asia.small <- mcmcabn(score.cache = bsc.compute.asia,
+                               score = "mlik",
+                               data.dists = dist.asia,
+                               max.parents = 2,
+                               mcmc.scheme = c(100,0,0),
+                               seed = 42,
+                               verbose = FALSE,
+                               start.dag = startDag,
+                               prob.rev = 0,
+                               prob.mbr = 0,
+                               prior.choice = 3,
+                               prior.dag = priorDag)
+
+summary(mcmc.out.asia.small)
+}

man/mcmc_run_asia.Rd

@@ -19,7 +19,7 @@
      }}
 
 \examples{
-\dontrun{
+\donttest{
 ## This data set was generated using the following code:
 library(bnlearn) #for the dataset
 library(abn) #for the cache of score function

man/plot.Rd

@@ -42,10 +42,12 @@ Scutari, M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journ
 
 
 \examples{
-\dontrun{
 ## Example from the asia dataset from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010)
 data("mcmc_run_asia")
 
 #plot the mcmc run
 plot(mcmc.out.asia)
-}}
+
+#plot cumulative max score
+plot(mcmc.out.asia, max.score = TRUE)
+}

man/print.Rd

@@ -30,10 +30,7 @@ There exists a \code{\link{summary}} S3 function that displays more details.
 \author{Gilles Kratzer}
 
 \examples{
-\dontrun{
 ## Example from the asia dataset from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010)
-data("mcmc_run_asia")
 
-#print the MCMC run
 print(mcmc.out.asia)
-}}
+}

man/summary.Rd

@@ -36,10 +36,7 @@ Scutari, M. (2010). Learning Bayesian Networks with the bnlearn R Package. Journ
 
 
 \examples{
-\dontrun{
 ## Example from the asia dataset from Lauritzen and Spiegelhalter (1988) provided by Scutari (2010)
-data("mcmc_run_asia")
-
 #summary the MCMC run
 summary(mcmc.out.asia)
-}}
+}