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Type 'q()' to quit R. > library(pcalg) > > source(system.file(package="Matrix", "test-tools-1.R", mustWork=TRUE)) Loading required package: tools > ##--> showProc.time(), assertError(), relErrV(), ... > cat("doExtras:", (doExtras <- pcalg:::doExtras()), "\n") doExtras: FALSE > > ################################################## > ## simulate graph; remove confounders > ################################################## > > ##' (utility) > ##' for (i in 1:p), > ##' vect[i] := new name of node i in the graph when series nodes are removed > conf2vec <- function(confs, p) { + stopifnot(p >= 1) # confs may be *empty* + vect <- sapply(1:p, function(r) r - sum(1 <= confs & confs <= r)) + vect[confs] <- NA_integer_ + vect + } > > > ##' random DAG with confounders removed > rDAG_noC <- function(p, prob, max.no.conf = 3, + V = c(LETTERS,letters)[1:p], verbose=TRUE) { + stopifnot(p == as.integer(p), length(p) >= 1, p >= 2, prob > 0, + !anyDuplicated(V)) + + g <- randomDAG(p, prob, V=V) ## true DAG + ## get the adjacency matrix + A <- as(g, "matrix") + A[A != 0] <- 1 + + ## first three confounders should be removed + ## confounders - vector of confounding nodes to be removed + n <- ncol(A) ## number of nodes + confounders <- numeric(max.no.conf) + counter <- 1L + for(k in 1:n) { + if (sum(A[k,]) > 1) { ## k has more than one child + confounders[counter] <- k ## mark node k; leave out later + counter <- counter +1L + if(counter > max.no.conf) + break + } + } + confounders <- confounders[is.c <- confounders > 0] + if(verbose) { + cat("confounders:\n"); print(confounders) + vect <- conf2vec(confounders, p) + cat("-> nodes after removing confounders:\n"); print(vect) + } + + ## define sufficient statistics (d-separation oracle) + trueC <- trueCov(g) + ## delete rows and columns belonging to confounder variables in confounders + C.1 <- if(any(is.c)) trueC[-confounders, -confounders] else trueC + list(g = g, trueC = trueC, trueCov.1 = C.1, confounders = confounders, p. = p - sum(is.c)) + } ## {rDAG_noC} > > chk_rDAG <- function(rD) { + stopifnot(is.list(rD), c("trueC", "trueCov.1", "p.","confounders") %in% names(rD)) + p <- nrow(rD$trueC) + p. <- rD$p. + conf <- rD$confounders + no.c <- sum(conf > 0) + stopifnot(is(rD$g, "graph")) + V <- rownames(rD$trueC) + stopifnot(p. == p - no.c, dim(rD$trueCov.1) == c(p., p.), + isSymmetric(rD$trueC), + isSymmetric(rD$trueCov.1), + identical(V, colnames(as(rD$g, "matrix"))), + identical(if(no.c) V[-conf] else V, rownames(rD$trueCov.1))) + } > > seed <- 547 ## Seed 547: Dsep Link wird benoetigt > set.seed(seed) > > ## will get slow if p > 14; these settings take already a few minutes > if (doExtras) { + p <- 8 + } else { + p <- 14 + } > r1 <- rDAG_noC(p, prob = 0.2) confounders: [1] 1 2 4 -> nodes after removing confounders: [1] NA NA 1 NA 2 3 4 5 6 7 8 9 10 11 > c(p = p, p. = r1$p.) p p. 14 11 > chk_rDAG(r1) > ### > > indepTest1 <- gaussCItest > > ## transform covariance matrix into a correlation matrix > p. <- r1$p. > suffStat1 <- list(C = cov2cor(r1$trueCov.1), n = 10^9) > > ## FCI+: neue Variante > showSys.time( + fciplus.fit <- fciPlus(suffStat1, indepTest = indepTest1, alpha = 0.99, p = p.) + ) 7 *-> 10 <-* 8 Sxz= and Szx= 8 *-> 10 <-* 7 Sxz= and Szx= 4 *-> 11 <-* 5 Sxz= and Szx= 4 *-> 11 <-* 7 Sxz= and Szx= 5 *-> 11 <-* 4 Sxz= and Szx= 5 *-> 11 <-* 7 Sxz= and Szx= 7 *-> 11 <-* 4 Sxz= and Szx= 7 *-> 11 <-* 5 Sxz= and Szx= Time user system elapsed Time 0.06 0.02 0.08 > > ## Fit FCI > showSys.time( + fci.fit <- fci(suffStat1, indepTest = indepTest1, alpha = 0.99, p = p., + rules = rep(TRUE, 10), verbose = FALSE) + ) Time user system elapsed Time 0.10 0.00 0.09 > > print.table(fciplus.fit@amat, zero.print = ".") 1 2 3 4 5 6 7 8 9 10 11 1 . . . . 1 . . . . . . 2 . . 1 1 . 1 . . 1 . . 3 . 1 . 1 . 1 . . 1 . . 4 . 1 1 . . 1 . . 1 . 2 5 1 . . . . . . 1 . . 2 6 . 1 1 1 . . . . 1 . . 7 . . . . . . . . . 2 2 8 . . . . 1 . . . . 2 . 9 . 1 1 1 . 1 . . . . . 10 . . . . . . 1 1 . . . 11 . . . 1 1 . 1 . . . . > ## MM{FIXME}: want to see node correct names !! > > if(!all(fciplus.fit@amat == fci.fit@amat)) + stop("Test fciPlus() differing from fci(): PAG not found correctly?") > showProc.time() Time (user system elapsed): 0.53 0.03 0.56 > > if(doExtras) { ## more tests + for(p in 2:20) { ## p = 10 + cat("\n\np = ", p, ":\n~~~~~~\n") + set.seed(p) + for(pr in c(0.75, 1.5) / p) { + seed <- 10*p + ceiling(2*p*pr) + cat("\nprob = ", pr, ", seed = ", seed,":\n") + set.seed(seed) + rr <- rDAG_noC(p, prob = pr) # quite sparse + chk_rDAG(rr) + + ## transform covariance matrix into a correlation matrix + p. <- rr$p. + suffStat1 <- list(C = cov2cor(rr$trueCov.1), n = 10^9) + + ## FCI+: neue Variante + showSys.time( + fciplus.fit <- fciPlus(suffStat1, indepTest = indepTest1, alpha = 0.99, p = p.) + ) + + ## Fit FCI + showSys.time( + fci.fit <- fci(suffStat1, indepTest = indepTest1, alpha = 0.99, p = p., + rules = rep(TRUE, 10), verbose = FALSE) + ) + + cat("Adjacency matrix:\n") + print.table(fciplus.fit@amat, zero.print = ".") + ## MM{FIXME}: want to see node correct names !! + + if(!all(fciplus.fit@amat == fci.fit@amat)) + stop("Test fciPlus() differing from fci(): PAG not found correctly?") + else cat("=================================================\n") + }# for different 'pr' + + }# for a set of 'p' + + } ## if (doExtras) > > proc.time() user system elapsed 1.79 0.28 2.06