update: print method summary, examples and dist.asia

NAMESPACE

@@ -2,7 +2,7 @@ export(mcmcabn,
 query,
 plot.mcmcabn,
 summary.mcmcabn,
-print.mcmcabn
+print.summary.mcmcabn
 )
        
        importFrom(gRbase, topoSortMAT)
@@ -19,4 +19,6 @@ print.mcmcabn
 #methods for class mcmcabn
 S3method(plot, mcmcabn)
 S3method(summary, mcmcabn)
-S3method(print, mcmcabn) 
+
+#method for class summary.mcmcabn
+S3method(print, summary.mcmcabn) 

NEWS.md

@@ -9,3 +9,4 @@
 ## mcmcabn 0.3:
 
 * update mcmcabn to make it compatible with constraints imported from the cache of scores. Heating parameter to increase or decrease acceptance probability.
+* new article

R/print-summary-mcmcabn.R

+############################################################################### summary.mcmcabn.R --- Author : Gilles Kratzer Last modified : 19.02.2019 :
+
+print.summary.mcmcabn <- function(x, ...) {
+
+    cat("MCMC summary:\n", sep = "")
+
+    cat("Number of burn-in steps: ", (x$object$burnin), "\n", sep = "")
+    cat("Number of MCMC steps: ", (x$object$iterations), "\n", sep = "")
+    cat("Thinning: ", (x$object$thinning), "\n\n", sep = "")
+
+    cat("Maximum score: ", max(x$object$scores), "\n", sep = "")
+    cat("Empirical mean: ", mean(x$object$scores), "\n", sep = "")
+    cat("Empirical standard deviation: ", format(sd(x$object$scores),...), "\n", sep = "")
+
+    cat("Quantiles of the posterior network score:\n")
+
+    print(x$quant, ...)
+
+    cat("\n\nGlobal acceptance rate: ", format( 1 - mean(x$object$rejection), ...), "\n", sep = "")
+
+    print(x$AR, ...)
+
+    cat("\n\nSample size adjusted for autocorrelation: ",
+        format( unname(effectiveSize(x$object$scores)), ...), "\n", sep = "")
+
+
+    cat("\nAutocorrelations by lag:\n")
+
+    print(x$acf, ...)
+
+}  #EOF

R/summary-mcmcabn.R

@@ -2,41 +2,17 @@
 
 summary.mcmcabn <- function(object, quantiles = c(0.025, 0.25, 0.5, 0.75, 0.975), lag.max = 10, ...) {
 
-    cat("MCMC summary:\n", sep = "")
-
-    cat("Number of burn-in steps: ", (object$burnin), "\n", sep = "")
-    cat("Number of MCMC steps: ", (object$iterations), "\n", sep = "")
-    cat("Thinning: ", (object$thinning), "\n\n", sep = "")
-
-    cat("Maximum score: ", max(object$scores), "\n", sep = "")
-    cat("Empirical mean: ", mean(object$scores), "\n", sep = "")
-    cat("Empirical standard deviation: ", format(sd(object$scores),...), "\n", sep = "")
-
-    cat("Quantiles of the posterior network score:\n")
-
     out1 <- matrix(data = quantile(x = object$scores, probs = quantiles), nrow = 1, ncol = length(quantiles), dimnames = list("BN score",
         quantiles))
 
-    print(out1, ...)
-
-    cat("\n\nGlobal acceptance rate: ", format( 1 - mean(object$rejection), ...), "\n", sep = "")
-
     out2 <- matrix(data = table(object$method, object$rejection), ncol = 2, dimnames = list(levels(factor(object$method)),
         c("Accepted", "Rejected")))
 
-    print(out2, ...)
-
-    cat("\n\nSample size adjusted for autocorrelation: ",
-        format( unname(effectiveSize(object$scores)), ...), "\n", sep = "")
-
-#    unname(acf(object$scores, lag.max = 10, plot = FALSE))
-
-
-    cat("\nAutocorrelations by lag:\n")
-
-    out2 <- matrix(data = acf(object$scores, lag.max = 10, plot = FALSE)$acf, nrow = 1, ncol = (lag.max + 1), dimnames = list("acf",
+    out3 <- matrix(data = acf(object$scores, lag.max = 10, plot = FALSE)$acf, nrow = 1, ncol = (lag.max + 1), dimnames = list("acf",
         acf(object$scores, lag.max = 10, plot = FALSE)$lag))
 
-    print(out2, ...)
+    ans <- list( object=object, quant=out1, AR=out2, acf=out3)
+    class( ans) <- "summary.mcmcabn"
+    return( ans)
 
 }  #EOF

man/bsc-compute-asia.Rd

@@ -29,16 +29,6 @@ colnames(asia) <- c("Asia",
   "XRay",
   "Dyspnea")
 
-## Defining the distribution list
-dist.asia <- list(Asia = "binomial",
-            Smoking = "binomial",
-            Tuberculosis = "binomial",
-            LungCancer = "binomial",
-            Bronchitis = "binomial",
-            Either = "binomial",
-            XRay = "binomial",
-            Dyspnea = "binomial")
-
 bsc.compute.asia <- buildscorecache(data.df = asia,
                                  data.dists = dist.asia,
                                  max.parents = 2)

man/mcmc-out-asia.Rd

@@ -20,30 +20,6 @@
 library(bnlearn) #for the dataset
 library(abn) #for the cache of scores computing function
 
-#renaming columns of the dataset
-colnames(asia) <- c("Asia",
-  "Smoking",
-  "Tuberculosis",
-  "LungCancer",
-  "Bronchitis",
-  "Either",
-  "XRay",
-  "Dyspnea")
-
-#lets define the distribution list
-dist.asia <- list(Asia = "binomial",
-            Smoking = "binomial",
-            Tuberculosis = "binomial",
-            LungCancer = "binomial",
-            Bronchitis = "binomial",
-            Either = "binomial",
-            XRay = "binomial",
-            Dyspnea = "binomial")
-
-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,

man/mcmc.Rd

@@ -197,8 +197,8 @@ 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 = 321,
+                  mcmc.scheme = c(150,0,0),
+                  seed = 41242,
                   verbose = FALSE,
                   start.dag = "random",
                   prob.rev = 0.03,

man/print-summary.Rd

+% print-summar.mcmcabn.Rd ---
+% Author           : Gilles Kratzer
+% Created on :       31.10.2019
+% Last modification :
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\name{print.summary.mcmcabn}
+\alias{print.summary.mcmcabn}
+\title{Methods for printing the summary of mcmcabn objects}
+
+\description{
+
+  Method for printing the summary of \code{mcmcabn} objects.
+
+}
+
+\usage{
+  \method{print}{summary.mcmcabn}(x, \dots)
+}
+
+\arguments{
+  \item{x}{an object of class \code{summary.mcmcabn}.}
+  \item{\dots}{additional arguments passed to \code{print}.}
+  }
+
+\details{
+There exists a \code{\link{summary}} S3 function that displays more details.
+}
+
+\author{Gilles Kratzer}
+
+\examples{
+## Example from the asia dataset from Lauritzen and Spiegelhalter (1988)
+## provided by Scutari (2010)
+
+summary(mcmc.out.asia)
+}

vignettes/mcmcabn-advanced.Rmd

@@ -172,6 +172,8 @@ tab <- apply(X = u.list.dag, MARGIN = 3, FUN = function(x){
 nb.dag <- 10
 scores.dags <- vector(length = nb.dag)
 num.arcs <- vector(length = nb.dag)
+shd <- vector(length = nb.dag)
+
 for(i in 1:nb.dag){
 
   dag <- u.list.dag[,,order(tab,decreasing = TRUE)[i]]
@@ -179,6 +181,7 @@ for(i in 1:nb.dag){
   fabn <- fitabn(dag.m = (dag),data.df = asia,data.dists = dist.asia,method = "bayes")
   scores.dags[i] <- fabn$mlik
   num.arcs[i] <- sum(dag)
+    shd[i] <- compareDag(ref = u.list.dag[,,order(tab,decreasing = TRUE)[1]],u.list.dag[,,order(tab,decreasing = TRUE)[i]])$`Hamming-distance`
 }
 
 ```
@@ -198,4 +201,6 @@ plot(x = 1:nb.dag,y = scores.dags,col="red", type = 'b', lwd=2, axes = FALSE, xl
 axis(4, col.axis = 'red')
 mtext("DAGs scores", side=4, line=1.5, col="red")
 axis(1, col.axis = 'black',at = 1:nb.dag,labels = num.arcs)
+axis(3, col.axis = 'orange',at = 1:nb.dag,labels = shd)
+mtext("Structural Hamming distances", side=3, line=2, col="orange")
 ```