Nothing
tests/test_gSquareBin.R
In pcalg: Methods for Graphical Models and Causal Inference
library(pcalg)
cat("doExtras:", (doExtras <- pcalg:::doExtras()), "\n")
##################################################
## Simple 2 Variables x1 -> x2
##################################################
set.seed(123)
## only 2 Variables
b0 <- 0
b1 <- 1
n <- 1000
x1 <- rbinom(n, 1, p = 1/2) ## == sample(c(0,1), n, replace=TRUE)
x2 <- rbinom(n, 1, p <- plogis(b0 + b1* x1))
res1 <- fisher.test(x1,x2)$p.value # 6.14 e-21
res2 <- gSquareBin(1,2,NULL, cbind(x1,x2)) # 3.74 e-21
(eq1 <- all.equal(c(res1, res2),
c(6.142e-21, 3.7401e-21), tol = 1e-5))
ok1 <- isTRUE(eq1)
##################################################
## chain of 3 variables: x1 -> x2 -> x3
##################################################
b0 <- 0
b1 <- 1
b2 <- 1
n <- 10000
set.seed(12)
x1 <- rbinom(n, 1, p=1/2)
x2 <- rbinom(n, 1, p2 <- plogis(b0 + b1* x1))
x3 <- rbinom(n, 1, p3 <- plogis(b0 + b2* x2))
##
dat <- cbind(x1,x2,x3)
##
system.time(
pv.2 <- c(gSquareBin(3,1, 2, dat),
gSquareBin(1,3, 2, dat),
gSquareBin(1,2, 3, dat),
gSquareBin(3,2, 1, dat))
)
(eq2 <- c(all.equal(pv.2, c(rep(0.89393365, 2), 0, 0), tol=1e-8),
all.equal(pv.2[1], pv.2[2], tol=1e-6)))
ok2 <- is.logical(eq2) && all(eq2)
ok3 <- ok4 <- ok5 <- ok6 <- TRUE
if (doExtras) {
suppressWarnings(RNGversion("3.5.0"))
##################################################
## collider: x1 -> x3 <- x2
##################################################
b0 <- 0
b1 <- 1.3
b2 <- 1.7
n <- 10000
set.seed(13)
x1 <- rbinom(n, 1, p=1/2)
x2 <- rbinom(n, 1, p=1/2)
x3 <- rbinom(n, 1, p3 <- plogis(b0 + b1*x1 + b2*x2))
##
dat <- cbind(x1,x2,x3)
##
system.time(
pv3 <- c(gSquareBin(1,2, 3, dat),
gSquareBin(1,2,NULL,dat),
gSquareBin(2,1, 3, dat),
gSquareBin(1,3, 2, dat))
)
(eq3 <- all.equal(pv3, c(0, 0.9054922, 0,0), tol=1e-7))
ok3 <- isTRUE(eq3)
##################################################
## add noise variables to collider model
##################################################
x4 <- sample(c(0,1),n, replace=TRUE)
x5 <- sample(c(0,1),n, replace=TRUE)
x6 <- sample(c(0,1),n, replace=TRUE)
dat <- cbind(x1,x2,x3,x4,x5,x6)
system.time(
pv4 <- c(gSquareBin(1,2, c(3,4,5,6), dat),
gSquareBin(1,3, c(2,4,5,6), dat),
gSquareBin(2,3, c(1,4,5,6), dat),
gSquareBin(2,4, c(1,3,5,6), dat),
gSquareBin(4,2, c(1,3,5,6), dat), # same
gSquareBin(3,4, c(1,2,5,6), dat),
##
gSquareBin(4,2, c(3,5,6), dat))
)
(eq4 <- c(all.equal(pv4, c(0, 0, 0, 0.0269101, 0.0269101, 0.7260011, 0.1121826),
tol = 2e-7),
all.equal(pv4[4], pv4[5], tol=1e-6)))
ok4 <- is.logical(eq4) && all(eq4)
##################################################
## rectangle model
##################################################
b0 <- 0
b1 <- 1
b2 <- 1
b3 <- 0.5
b4 <- 0.5
n <- 10000
set.seed(31)
x1 <- rbinom(n, size=1, prob = 1/2)# = sample(c(0,1), n, replace=TRUE)
x2 <- rbinom(n, size=1, prob = (p2 <- plogis(b0 + b1* x1)))
x3 <- rbinom(n, size=1, prob = (p3 <- plogis(b0 + b2* x1)))
x4 <- rbinom(n, size=1, prob = (p4 <- plogis(b0 + b3* x2 + b4* x3)))
##
dat <- cbind(x1,x2,x3,x4)
system.time(
pv5 <- c(gSquareBin(1,4,c(2,3),dat),
gSquareBin(1,4, 2, dat),
gSquareBin(1,4, 3, dat),
gSquareBin(1,4,NULL, dat),
gSquareBin(2,3, 4, dat),
gSquareBin(2,3, 1, dat),
gSquareBin(2,3,c(1,4),dat))
)
(eq5 <- all.equal(pv5, c(0.38484, 0.13601, 0.10974, 0, 0.00023, 0.56828, 0.43436), 4e-4))
ok5 <- isTRUE(eq5)
##################################################
## rectangle model -- extended
##################################################
x5 <- rbinom(n, size=1, prob = (p5 <- plogis(b0 + x2)))
x6 <- rbinom(n, size=1, prob = (p6 <- plogis(b0 + x3/2 - x4)))
x7 <- rbinom(n, size=1, prob = (p7 <- plogis(b0 + x5 - x6)))
x8 <- rbinom(n, size=1, prob = (p8 <- plogis(b0 - x3 + x6/2)))
x9 <- rbinom(n, size=1, prob = (p9 <- plogis(b0 + x2 - x7)))
##
dat <- cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9)
## do many tests:
p <- ncol(dat) # = 9
## all (x,y) pairs:
xy <- combn(p,2)
"%w/o%" <- function(x, y) x[!x %in% y] #-- x without y
ip <- 1:p
str(rest <- apply(xy, 2, function(e) ip %w/o% e)) # 7 x 36
set.seed(101)
in2 <- seq_len(ncol(xy))
args <- c(lapply(in2, function(i) list(x=xy[1,i], y=xy[2,i], S=rest[,i])),
lapply(in2, function(i) list(x=xy[1,i], y=xy[2,i], S=sample(rest[,i], size= p-2-1))),
lapply(in2, function(i) list(x=xy[1,i], y=xy[2,i], S=sample(rest[,i], size= sample(0:(p-2), 1)))),
lapply(in2, function(i) list(x=xy[1,i], y=xy[2,i], S=sample(rest[,i], size= sample(2:(p-2), 1)))))
ia <- TRUE
system.time(
pv6 <- vapply(args, function(L) do.call(gSquareBin, c(L, list(dm=dat))), 1)
)# if(doExtras) about one minute
if(FALSE)
dput(signif(zapsmall(pv6), 4))
pv6.ok <- c(0, 0, 0.6427, 0.4745, 0.9698, 0.4586, 0.5739, 0.6701, 0.1889,
0, 0, 0.6476, 0.00601, 0.3586, 0, 0, 0.08916, 0, 0.7642, 0, 0.04021,
0.4694, 0, 0.8799, 0.3966, 0.358, 0.01514, 0, 0.3615, 0.3244,
0, 0, 0.6647, 0.6839, 0, 0.4136, 0, 0, 0.7023, 0.303, 0.9189,
0.5501, 0.7896, 0.8239, 0.04095, 0, 0, 0.5531, 0.6374, 0.755,
0, 0, 0.629, 0, 0.8377, 0, 0.5749, 0.6346, 0, 0.9513, 0.3138,
0.6293, 0.000153, 0, 0.7855, 0.1503, 0, 0, 0.2033, 0.9071, 0,
0.2587, 0, 0, 1.08e-05, 0.0002067, 0.03795, 0.5501, 3.5e-06,
0.001827, 0.5683, 0, 0, 0.005468, 0.4085, 0.1476, 0, 0, 0.6822,
0, 0.7642, 0, 0.7184, 0.4596, 0, 0.8799, 0.02051, 0.03413, 0.01689,
0, 0.2786, 0.07084, 0, 0, 5.17e-05, 0.4915, 0, 0.5029, 0, 0,
0.1891, 0.09992, 0.9698, 0.3937, 5.94e-05, 0.5473, 0.3894, 0,
0, 0.2236, 2.3e-06, 0.784, 0, 0, 0.3504, 0, 0.7642, 0, 0.3572,
0.4259, 0, 0.8139, 0.3326, 0.01518, 0.01514, 0, 0.3541, 0.021,
0, 0, 0.6647, 0.6167, 0, 0.4136)
(eq6 <- all.equal(pv6, pv6.ok[ia], tol= 1e-4))
ok6 <- isTRUE(eq6)
}
if (!all(ok1,ok2,ok3,ok4,ok5,ok6))
stop("Test gSquareBin wrong: Some dependence was not estimated correctly!")
Try the pcalg package in your browser
Any scripts or data that you put into this service are public.
pcalg documentation built on May 29, 2024, 5:24 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
library(pcalg)
cat("doExtras:", (doExtras <- pcalg:::doExtras()), "\n")
##################################################
## Simple 2 Variables x1 -> x2
##################################################
set.seed(123)
## only 2 Variables
b0 <- 0
b1 <- 1
n <- 1000
x1 <- rbinom(n, 1, p = 1/2) ## == sample(c(0,1), n, replace=TRUE)
x2 <- rbinom(n, 1, p <- plogis(b0 + b1* x1))
res1 <- fisher.test(x1,x2)$p.value # 6.14 e-21
res2 <- gSquareBin(1,2,NULL, cbind(x1,x2)) # 3.74 e-21
(eq1 <- all.equal(c(res1, res2),
c(6.142e-21, 3.7401e-21), tol = 1e-5))
ok1 <- isTRUE(eq1)
##################################################
## chain of 3 variables: x1 -> x2 -> x3
##################################################
b0 <- 0
b1 <- 1
b2 <- 1
n <- 10000
set.seed(12)
x1 <- rbinom(n, 1, p=1/2)
x2 <- rbinom(n, 1, p2 <- plogis(b0 + b1* x1))
x3 <- rbinom(n, 1, p3 <- plogis(b0 + b2* x2))
##
dat <- cbind(x1,x2,x3)
##
system.time(
pv.2 <- c(gSquareBin(3,1, 2, dat),
gSquareBin(1,3, 2, dat),
gSquareBin(1,2, 3, dat),
gSquareBin(3,2, 1, dat))
)
(eq2 <- c(all.equal(pv.2, c(rep(0.89393365, 2), 0, 0), tol=1e-8),
all.equal(pv.2[1], pv.2[2], tol=1e-6)))
ok2 <- is.logical(eq2) && all(eq2)
ok3 <- ok4 <- ok5 <- ok6 <- TRUE
if (doExtras) {
suppressWarnings(RNGversion("3.5.0"))
##################################################
## collider: x1 -> x3 <- x2
##################################################
b0 <- 0
b1 <- 1.3
b2 <- 1.7
n <- 10000
set.seed(13)
x1 <- rbinom(n, 1, p=1/2)
x2 <- rbinom(n, 1, p=1/2)
x3 <- rbinom(n, 1, p3 <- plogis(b0 + b1*x1 + b2*x2))
##
dat <- cbind(x1,x2,x3)
##
system.time(
pv3 <- c(gSquareBin(1,2, 3, dat),
gSquareBin(1,2,NULL,dat),
gSquareBin(2,1, 3, dat),
gSquareBin(1,3, 2, dat))
)
(eq3 <- all.equal(pv3, c(0, 0.9054922, 0,0), tol=1e-7))
ok3 <- isTRUE(eq3)
##################################################
## add noise variables to collider model
##################################################
x4 <- sample(c(0,1),n, replace=TRUE)
x5 <- sample(c(0,1),n, replace=TRUE)
x6 <- sample(c(0,1),n, replace=TRUE)
dat <- cbind(x1,x2,x3,x4,x5,x6)
system.time(
pv4 <- c(gSquareBin(1,2, c(3,4,5,6), dat),
gSquareBin(1,3, c(2,4,5,6), dat),
gSquareBin(2,3, c(1,4,5,6), dat),
gSquareBin(2,4, c(1,3,5,6), dat),
gSquareBin(4,2, c(1,3,5,6), dat), # same
gSquareBin(3,4, c(1,2,5,6), dat),
##
gSquareBin(4,2, c(3,5,6), dat))
)
(eq4 <- c(all.equal(pv4, c(0, 0, 0, 0.0269101, 0.0269101, 0.7260011, 0.1121826),
tol = 2e-7),
all.equal(pv4[4], pv4[5], tol=1e-6)))
ok4 <- is.logical(eq4) && all(eq4)
##################################################
## rectangle model
##################################################
b0 <- 0
b1 <- 1
b2 <- 1
b3 <- 0.5
b4 <- 0.5
n <- 10000
set.seed(31)
x1 <- rbinom(n, size=1, prob = 1/2)# = sample(c(0,1), n, replace=TRUE)
x2 <- rbinom(n, size=1, prob = (p2 <- plogis(b0 + b1* x1)))
x3 <- rbinom(n, size=1, prob = (p3 <- plogis(b0 + b2* x1)))
x4 <- rbinom(n, size=1, prob = (p4 <- plogis(b0 + b3* x2 + b4* x3)))
##
dat <- cbind(x1,x2,x3,x4)
system.time(
pv5 <- c(gSquareBin(1,4,c(2,3),dat),
gSquareBin(1,4, 2, dat),
gSquareBin(1,4, 3, dat),
gSquareBin(1,4,NULL, dat),
gSquareBin(2,3, 4, dat),
gSquareBin(2,3, 1, dat),
gSquareBin(2,3,c(1,4),dat))
)
(eq5 <- all.equal(pv5, c(0.38484, 0.13601, 0.10974, 0, 0.00023, 0.56828, 0.43436), 4e-4))
ok5 <- isTRUE(eq5)
##################################################
## rectangle model -- extended
##################################################
x5 <- rbinom(n, size=1, prob = (p5 <- plogis(b0 + x2)))
x6 <- rbinom(n, size=1, prob = (p6 <- plogis(b0 + x3/2 - x4)))
x7 <- rbinom(n, size=1, prob = (p7 <- plogis(b0 + x5 - x6)))
x8 <- rbinom(n, size=1, prob = (p8 <- plogis(b0 - x3 + x6/2)))
x9 <- rbinom(n, size=1, prob = (p9 <- plogis(b0 + x2 - x7)))
##
dat <- cbind(x1,x2,x3,x4,x5,x6,x7,x8,x9)
## do many tests:
p <- ncol(dat) # = 9
## all (x,y) pairs:
xy <- combn(p,2)
"%w/o%" <- function(x, y) x[!x %in% y] #-- x without y
ip <- 1:p
str(rest <- apply(xy, 2, function(e) ip %w/o% e)) # 7 x 36
set.seed(101)
in2 <- seq_len(ncol(xy))
args <- c(lapply(in2, function(i) list(x=xy[1,i], y=xy[2,i], S=rest[,i])),
lapply(in2, function(i) list(x=xy[1,i], y=xy[2,i], S=sample(rest[,i], size= p-2-1))),
lapply(in2, function(i) list(x=xy[1,i], y=xy[2,i], S=sample(rest[,i], size= sample(0:(p-2), 1)))),
lapply(in2, function(i) list(x=xy[1,i], y=xy[2,i], S=sample(rest[,i], size= sample(2:(p-2), 1)))))
ia <- TRUE
system.time(
pv6 <- vapply(args, function(L) do.call(gSquareBin, c(L, list(dm=dat))), 1)
)# if(doExtras) about one minute
if(FALSE)
dput(signif(zapsmall(pv6), 4))
pv6.ok <- c(0, 0, 0.6427, 0.4745, 0.9698, 0.4586, 0.5739, 0.6701, 0.1889,
0, 0, 0.6476, 0.00601, 0.3586, 0, 0, 0.08916, 0, 0.7642, 0, 0.04021,
0.4694, 0, 0.8799, 0.3966, 0.358, 0.01514, 0, 0.3615, 0.3244,
0, 0, 0.6647, 0.6839, 0, 0.4136, 0, 0, 0.7023, 0.303, 0.9189,
0.5501, 0.7896, 0.8239, 0.04095, 0, 0, 0.5531, 0.6374, 0.755,
0, 0, 0.629, 0, 0.8377, 0, 0.5749, 0.6346, 0, 0.9513, 0.3138,
0.6293, 0.000153, 0, 0.7855, 0.1503, 0, 0, 0.2033, 0.9071, 0,
0.2587, 0, 0, 1.08e-05, 0.0002067, 0.03795, 0.5501, 3.5e-06,
0.001827, 0.5683, 0, 0, 0.005468, 0.4085, 0.1476, 0, 0, 0.6822,
0, 0.7642, 0, 0.7184, 0.4596, 0, 0.8799, 0.02051, 0.03413, 0.01689,
0, 0.2786, 0.07084, 0, 0, 5.17e-05, 0.4915, 0, 0.5029, 0, 0,
0.1891, 0.09992, 0.9698, 0.3937, 5.94e-05, 0.5473, 0.3894, 0,
0, 0.2236, 2.3e-06, 0.784, 0, 0, 0.3504, 0, 0.7642, 0, 0.3572,
0.4259, 0, 0.8139, 0.3326, 0.01518, 0.01514, 0, 0.3541, 0.021,
0, 0, 0.6647, 0.6167, 0, 0.4136)
(eq6 <- all.equal(pv6, pv6.ok[ia], tol= 1e-4))
ok6 <- isTRUE(eq6)
}
if (!all(ok1,ok2,ok3,ok4,ok5,ok6))
stop("Test gSquareBin wrong: Some dependence was not estimated correctly!")
Try the pcalg package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.