R Under development (unstable) (2024-02-05 r85863 ucrt) -- "Unsuffered Consequences"
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> ## Test file ages
> library(pcalg)
> suppressWarnings(RNGversion("3.5.0"))
> (doExtras <- pcalg:::doExtras())
[1] FALSE
> 
> ## Known example where ges and ages output a different result
> bool3 <- TRUE
> set.seed(77)
> 
> p <- 8
> n <- 5000
> ## true DAG:
> vars <- c("Author", "Bar", "Ctrl", "Goal", paste0("V",5:8))
> gGtrue <- randomDAG(p, prob = 0.3, V = vars)
> data = rmvDAG(n, gGtrue)
> 
> ## Estimate the aggregated PDAG with ages
> ages.fit <- ages(data = data)
> 
> 
> ## Estimate the essential graph with ges
> score <- new("GaussL0penObsScore", data)
> ges.fit <- ges(score)
> 
> diff <- as(ges.fit$essgraph,"matrix") - as(ages.fit$essgraph,"matrix")
> 
> bool3 <- ( bool3 & (diff[6,2]==1) * (diff[8,2]==1) *(sum(abs( as(ges.fit$essgraph,"matrix") - as(ages.fit$essgraph,"matrix") ))==2) )
> stopifnot(bool3)
> 
> if (doExtras) {
+ ## Test 1: Need to make sure that the skeleton of ges and ages are the same
+ 
+ bool1 <- TRUE
+ for(i in 1:10){
+   
+   p <- 20
+   n <- 5000
+   ## true DAG:
+   gGtrue <- randomDAG(p, prob = 0.4)
+   data <- rmvDAG(n, gGtrue)
+   
+   
+   score <- new("GaussL0penObsScore", data=data, lambda = 200)
+   ges.fit <- ges(score = score, phase = c("forward", "backward"), iterate = F)
+   ages.fit <- ages(data = data, lambda_min = 200)
+   
+   
+   ges.mat <- as(ges.fit$essgraph,"matrix")
+   ages.mat <- as(ages.fit$essgraph,"matrix")
+   # 
+   # ages1mat2 <- ages1[[3]][[length(ages1[[3]])]]
+   
+   
+   bool1 <- ( bool1 & all((((ges.mat + t(ges.mat))!=0)*1)==(((ages.mat + t(ages.mat))!=0)*1)) )
+ }
+ stopifnot(bool1)
+ 
+ ## Test 2: Number of CPDAGs used and number of penalty parameter has to be the same
+ bool2 <- TRUE
+ for(i in 1:10){
+   
+   p <- 20
+   n <- 5000
+   ## true DAG:
+   gGtrue <- randomDAG(p, prob = 0.4)
+   data = rmvDAG(n, gGtrue)
+   
+   agesfit <- ages(data = data, lambda_min = 100)
+   
+   bool2 <- ( bool2 & (length(agesfit$CPDAGsList) == length(agesfit$lambda)) )
+ }
+ stopifnot(bool2)
+ 
+ }
> 
> proc.time()
   user  system elapsed 
   1.42    0.18    1.60