Nothing
R/simdata.R
In baycn: Bayesian Inference for Causal Networks
#' simdata
#'
#' A function for simulating data under various topologies for continuous and
#' mixed data.
#'
#' @param graph A character string of the graph for which data will be
#' simulated. The graphs that can be chosen are m1_ge, m1_gv, m1_cp, m1_cc,
#' m1_iv, m2_ge, m2_gv, m2_cp, m2_cc, m2_iv, m3_gv, m3_cp, m3_cc, m3_iv, mp_ge,
#' mp_gv, gn4, gn5, gn8, gn11, layer, layer_cp, layer_iv, and star.
#'
#' The following figures show the graph for each of the topologies listed above.
#' The nodes with a circle around the name are normally distributed and the
#' nodes with a diamond around the name are distributed multinomial. The nodes
#' labeled with a C represent confounding variables. The Principle of Mendelian
#' Randomization (PMR) can be used on graphs with discrete and continuous
#' random variables. This introduces the constraint that the continuous random
#' variables cannot be parents of discrete random variables and edges between
#' these types of variables only have two states: absent and directed with the
#' discrete random variable as the parent.
#'
#' m1_ge - Topology M1 with three continuous random variables. In this case M1
#' has two other Markov equivalent graphs.
#'
#' m1_gv - Topology M1 with one discrete random variable U and two continuous
#' random variables. When using the PMR this graph does not have any other
#' Markov equivalent graphs.
#'
#' We consider three types of confounding variables (Yang et al., 2017):
#'
#' m1_cp - Topology M1 with n common parent confounding variables.
#'
#' m1_cc - Topology M1 with n common child confounding variables.
#'
#' m1_iv - Topology M1 with n intermediate confounding variables.
#'
#' \if{html}{\figure{m1.png}{options: width="75\%" alt="Figure: m1.png"}}
#' \if{latex}{\figure{m1.pdf}{options: width=12cm}}
#'
#' m2_ge - Topology M2 with three continuous random variables. This graph is a v
#' structure and does not have any other Markov equivalent graphs.
#'
#' m2_gv - Topolog M2 with one discrete random variable U and two continuous
#' random variables. This graph is a v structure and does not have any other
#' Markov equivalent graphs.
#'
#' m2_cp - Topology M2 with n common parent confounding variables.
#'
#' m2_cc - Topology M2 with n common child confounding variables.
#'
#' m2_iv - Topology M2 with n intermediate confounding variables.
#'
#' \if{html}{\figure{m2.png}{options: width="75\%" alt="Figure: m2.png"}}
#' \if{latex}{\figure{m2.pdf}{options: width=12cm}}
#'
#' m3_gv - Topolog M3 with one discrete random variable U and two continuous
#' random variables.
#'
#' m3_cp - Topology M3 with n common parent confounding variables.
#'
#' m3_cc - Topology M3 with n common child confounding variables.
#'
#' m3_iv - Topology M3 with n intermediate confounding variables.
#'
#' \if{html}{\figure{m3.png}{options: width="75\%" alt="Figure: m3.png"}}
#' \if{latex}{\figure{m3.pdf}{options: width=12cm}}
#'
#' mp_ge - The multi-parent topology with continuous random variables. This
#' graph is made up of multiple v structures and has no other Markov equivalent
#' graphs.
#'
#' mp_gv - The multi-parent topology with one discrete random variable. This
#' graph is made up of multiple v structures and has no other Markov equivalent
#' graphs.
#'
#' \if{html}{\figure{mp.png}{options: width="55\%" alt="Figure: mp.png"}}
#' \if{latex}{\figure{mp.pdf}{options: width=7cm}}
#'
#' gn4 - Topology GN4 is formed by combining topologies M1 and M2. The Markov
#' equivalence class for this topology is made up of three different graphs.
#'
#' gn5 - Topology GN5 has three other Markov equivalent graphs.
#'
#' gn8 - Topology GN8 has three overlapping cycles, two v structures, and two
#' other Markov equivalent graphs.
#'
#' gn11 - Topology GN11 has two sub-graphs separated by a v structure at node
#' T6.
#'
#' \if{html}{\figure{gn.png}{options: width="55\%" alt="Figure: gn.png"}}
#' \if{latex}{\figure{gn.pdf}{options: width=7cm}}
#'
#' layer - The layer topology has no other Markov equivalent graphs when using
#' the PMR and is made up of multiple M1 topologies.
#'
#' layer_cp - The layer topology with 2 common parent confounding variables.
#'
#' layer_iv - The layer topology with 2 intermediate confounding variables.
#'
#' \if{html}{\figure{layer.png}{options: width="75\%" alt="Figure: layer.png"}}
#' \if{latex}{\figure{layer.pdf}{options: width=12cm}}
#'
#' star - The star topology has no other Markov equivalent graphs when using the
#' PMR and is made up of multiple M1 topologies.
#'
#' \if{html}{\figure{star.png}{options: width="25\%" alt="Figure: star.png"}}
#' \if{latex}{\figure{star.pdf}{options: width=2.5cm}}
#'
#' @param N The number of observations to simulate.
#'
#' @param b0 The mean of the variable if it does not have any parents. If the
#' variable has one or more parents it is the slope in the linear model that is
#' the mean of the normally distributed variables.
#'
#' @param ss The coefficient of the parent nodes (if there are any) in the
#' linear model that is the mean of the normally distributed variables. This
#' coefficient is referred to as the signal strength.
#'
#' @param s The standard deviation of the normal distribution.
#'
#' @param p The probability of success for a binomial random variable.
#'
#' @param q The frequency of the reference allele.
#'
#' @param ssc The signal strength of the confounding variables.
#'
#' @param nConfounding The number of confounding variables to simulate.
#'
#' @return A matrix with the variables across the columns and the observations
#' down the rows.
#'
#' @references Yang, F., Wang, J., The GTEx Consortium, Pierce, B. L., and
#' Chen, L. S. (2017).
#' Identifying cis-mediators for trans-eQTLs across many human tissues using
#' genomic mediation analysis. \emph{Genome Res.} 27, 1859-1871.
#'
#' @examples
#' # Generate data under topology GN4.
#' data_gn4 <- simdata(graph = 'gn4',
#' N = 500,
#' b0 = 1,
#' ss = 1,
#' s = 1)
#'
#' # Display the first few rows of the data.
#' data_gn4[1:5, ]
#'
#' # Generate data under topology M1 with 3 intermediate confounding variables.
#' data_m1_iv <- simdata(graph = 'm1_iv',
#' N = 500,
#' b0 = 0,
#' ss = 1,
#' s = 1,
#' q = 0.1,
#' ssc = 0.2,
#' nConfounding = 3)
#'
#' # Show the first few rows of the data.
#' data_m1_iv[1:5, ]
#'
#' @export
#'
simdata <- function (graph = 'gn4',
N = 500,
b0 = 0,
ss = 1,
s = 1,
p = 0.6,
q = 0.45,
ssc = 0.2,
nConfounding = 2) {
switch(graph,
# m1_ge ---------------------------------------------------------------
'm1_ge' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(T1, T2, T3))
},
# m1_gv ---------------------------------------------------------------
'm1_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2))
},
# m1_cph --------------------------------------------------------------
'm1_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss))
return (cbind(U, T1, T2, W))
},
# m1_cp ---------------------------------------------------------------
'm1_cp' = {
U <- sMulti(N = N,
q = q)
# create a vector with the signal strength of the parents for T1
ssT1 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT1[[1]] <- ss
# Create a list with the data of the parents for T1 as the elements
# of the list.
parT1 <- vector(mode = 'list',
length = nConfounding + 1)
parT1[[1]] <- U
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT2[[1]] <- ss
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding + 1)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- sNorm(N = N,
b0 = b0,
s = s)
# Add the signal strength of the confounding variable to the ssT1
# vector.
ssT1[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT1[[a + 1]] <- con[, a]
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a + 1]] <- con[, a]
}
T1 <- cNorm(N = N,
parentData = parT1,
b0 = b0,
b1 = ssT1,
s = s)
# Fill in the first element of parT2 with data generated from T1.
parT2[[1]] <- T1
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
return (cbind(U, T1, T2, con))
},
# m1_cc ---------------------------------------------------------------
'm1_cc' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ssc, ssc),
s = s)
}
return (cbind(U, T1, T2, con))
},
# m1_iv ---------------------------------------------------------------
'm1_iv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT2[[1]] <- ss
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding + 1)
parT2[[1]] <- T1
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ssc),
s = s)
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a + 1]] <- con[, a]
}
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
return (cbind(U, T1, T2, con))
},
# m2_ge ---------------------------------------------------------------
'm2_ge' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T3 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1, T3),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3))
},
# m2_gv ---------------------------------------------------------------
'm2_gv' = {
U <- sMulti(N = N,
q = q)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T1 <- cNorm(N = N,
parentData = list(U, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(U, T1, T2))
},
# m2_cp ---------------------------------------------------------------
'm2_cp' = {
U <- sMulti(N = N,
q = q)
# create a vector with the signal strength of the parents for T1
ssT1 <- vector(mode = 'numeric',
length = nConfounding + 2)
# Fill in the first and second elements of the ssT1 vector. Both
# elements will be ss becasue the first two parents of T1 are U and
# T2.
ssT1[[1]] <- ss
ssT1[[2]] <- ss
# Create a list with the data of the parents for T1 as the elements
# of the list.
parT1 <- vector(mode = 'list',
length = nConfounding + 2)
# The first parent of T1 is U.
parT1[[1]] <- U
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding)
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- sNorm(N = N,
b0 = b0,
s = s)
# Add the signal strength of the confounding variable to the ssT1
# vector.
ssT1[[a + 2]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT1[[a + 2]] <- con[, a]
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a]] <- con[, a]
}
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
# Fill in the second element of the parT1 list with the data from T2
parT1[[2]] <- T2
T1 <- cNorm(N = N,
parentData = parT1,
b0 = b0,
b1 = ssT1,
s = s)
return (cbind(U, T1, T2, con))
},
# m2_cc ---------------------------------------------------------------
'm2_cc' = {
U <- sMulti(N = N,
q = q)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T1 <- cNorm(N = N,
parentData = list(U, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ssc, ssc),
s = s)
}
return (cbind(U, T1, T2, con))
},
# m2_iv ---------------------------------------------------------------
'm2_iv' = {
U <- sMulti(N = N,
q = q)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# create a vector with the signal strength of the parents for T1
ssT1 <- vector(mode = 'numeric',
length = nConfounding + 2)
# The first two elements will be the signal strength of U and T2.
ssT1[[1]] <- ss
ssT1[[2]] <- ss
# Create a list with the data of the parents for T1 as the elements
# of the list.
parT1 <- vector(mode = 'list',
length = nConfounding + 2)
# The first to elements will be the data from U and T2.
parT1[[1]] <- U
parT1[[2]] <- T2
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ssc),
s = s)
# Add the signal strength of the confounding variable to the ssT1
# vector.
ssT1[[a + 2]] <- ssc
# Add the data of the current confounding variable to the parT1
# list.
parT1[[a + 2]] <- con[, a]
}
T1 <- cNorm(N = N,
parentData = parT1,
b0 = b0,
b1 = ssT1,
s = s)
return (cbind(U, T1, T2, con))
},
# m3_gv ---------------------------------------------------------------
'm3_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2))
},
# m3_cph --------------------------------------------------------------
'm3_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, W))
},
# m3_cp ---------------------------------------------------------------
'm3_cp' = {
U <- sMulti(N = N,
q = q)
# create a vector with the signal strength of the parents for T1
ssT1 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT1[[1]] <- ss
# Create a list with the data of the parents for T1 as the elements
# of the list.
parT1 <- vector(mode = 'list',
length = nConfounding + 1)
parT1[[1]] <- U
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT2[[1]] <- ss
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding + 1)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- sNorm(N = N,
b0 = b0,
s = s)
# Add the signal strength of the confounding variable to the ssT1
# vector.
ssT1[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT1[[a + 1]] <- con[, a]
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a + 1]] <- con[, a]
}
T1 <- cNorm(N = N,
parentData = parT1,
b0 = b0,
b1 = ssT1,
s = s)
# Fill in the first element of parT2 with data generated from U.
parT2[[1]] <- U
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
return (cbind(U, T1, T2, con))
},
# m3_cc ---------------------------------------------------------------
'm3_cc' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ssc, ssc),
s = s)
}
return (cbind(U, T1, T2, con))
},
# m3_iv ---------------------------------------------------------------
'm3_iv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT2[[1]] <- ss
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding + 1)
parT2[[1]] <- U
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ssc),
s = s)
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a + 1]] <- con[, a]
}
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
return (cbind(U, T1, T2, con))
},
# m4_ge ---------------------------------------------------------------
'm4_ge' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
# Generate data for T2 and T3 with a v structure at T3.
T2_a <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3_a <- cNorm(N = N,
parentData = list(T1, T2_a),
b0 = b0,
b1 = c(ss, ss),
s = s)
# Generate data for T2 and T3 with a v structure at T2.
T3_b <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T2_b <- cNorm(N = N,
parentData = list(T1, T3_b),
b0 = b0,
b1 = c(ss, ss),
s = s)
# Create a vector representing a coin toss.
coin_flip <- sample(x = 0:1,
size = N,
replace = TRUE)
# Create empty vectors for T2 and T3.
T2 <- vector(mode = 'numeric',
length = N)
T3 <- vector(mode = 'numeric',
length = N)
# Create a vector for T2 that is a combination of T2_a and T2_b.
T2[coin_flip == 0] <- T2_a[coin_flip == 0]
T2[coin_flip == 1] <- T2_b[coin_flip == 1]
# Create a vector for T3 that is a combination of T3_a and T3_b.
T3[coin_flip == 0] <- T3_a[coin_flip == 0]
T3[coin_flip == 1] <- T3_b[coin_flip == 1]
return (cbind(T1, T2, T3))
},
# m4_gv ---------------------------------------------------------------
'm4_gv' = {
U <- sMulti(N = N,
q = q)
# Generate data for T1 and T2 with a v structure at T2.
T1_a <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2_a <- cNorm(N = N,
parentData = list(U, T1_a),
b0 = b0,
b1 = c(ss, ss),
s = s)
# Generate data for T1 and T2 with a v structure at T1.
T2_b <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T1_b <- cNorm(N = N,
parentData = list(U, T2_b),
b0 = b0,
b1 = c(ss, ss),
s = s)
# Create a vector of 0s and 1s to represent flipping a coin.
coin_flip <- sample(x = 0:1,
size = N,
replace = TRUE)
# Create empty vectors for T1 and T2.
T1 <- vector(mode = 'numeric',
length = N)
T2 <- vector(mode = 'numeric',
length = N)
# Create a vector for T1 that is a combination of T1_a and T1_b.
T1[coin_flip == 0] <- T1_a[coin_flip == 0]
T1[coin_flip == 1] <- T1_b[coin_flip == 1]
# Create a vector for T2 that is a combination of T2_a and T2_b.
T2[coin_flip == 0] <- T2_a[coin_flip == 0]
T2[coin_flip == 1] <- T2_b[coin_flip == 1]
return (cbind(U, T1, T2))
},
# mp_ge ---------------------------------------------------------------
'mp_ge' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T3 <- sNorm(N = N,
b0 = b0,
s = s)
T4 <- cNorm(N = N,
parentData = list(T1, T2, T3),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
return (cbind(T1, T2, T3, T4))
},
# mp_gv ---------------------------------------------------------------
'mp_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T3 <- cNorm(N = N,
parentData = list(U, T1, T2),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
return (cbind(U, T1, T2, T3))
},
# mp_cph --------------------------------------------------------------
'mp_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T3 <- cNorm(N = N,
parentData = list(U, T1, T2),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, W))
},
# gn4 -----------------------------------------------------------------
'gn4' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T4),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3, T4))
},
# gn5 -----------------------------------------------------------------
'gn5' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T3, T4),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3, T4, T5))
},
# gn6 -----------------------------------------------------------------
'gn6' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2, T6),
b0 = b0,
b1 = c(ss, ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(T1, T2, T3, T4, T5, T6))
},
# gn6_gv --------------------------------------------------------------
'gn6_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2, T6),
b0 = b0,
b1 = c(ss, ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U, T3),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6))
},
# gn6_cph -------------------------------------------------------------
'gn6_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2, T6),
b0 = b0,
b1 = c(ss, ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U, T3),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, W))
},
# gn8 -----------------------------------------------------------------
'gn8' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- sNorm(N = N,
b0 = b0,
s = s)
T5 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T6),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3, T4, T5, T6, T7, T8))
},
# gn8_gv --------------------------------------------------------------
'gn8_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- sNorm(N = N,
b0 = b0,
s = s)
T5 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T6),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, T8))
},
# gn8_cph -------------------------------------------------------------
'gn8_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- sNorm(N = N,
b0 = b0,
s = s)
T5 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T6),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T3, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, T8, W))
},
# gn8_cph_2 -------------------------------------------------------------
'gn8_cph_2' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- sNorm(N = N,
b0 = b0,
s = s)
T5 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T6),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T3, T7),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, T8, W))
},
# gn11 ----------------------------------------------------------------
'gn11' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T7 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T7),
b0 = b0,
b1 = c(ss),
s = s)
T9 <- cNorm(N = N,
parentData = list(T8),
b0 = b0,
b1 = c(ss),
s = s)
T10 <- cNorm(N = N,
parentData = list(T9),
b0 = b0,
b1 = c(ss),
s = s)
T11 <- cNorm(N = N,
parentData = list(T10),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T5, T7),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11))
},
# gn13 ----------------------------------------------------------------
'gn13' = {
# Simulate data for the source nodes first.
U1 <- sMulti(N = N,
q = q)
U2 <- sMulti(N = N,
q = q)
U3 <- sMulti(N = N,
q = q)
U4 <- sMulti(N = N,
q = q)
U5 <- sMulti(N = N,
q = q)
T6 <- sNorm(N = N,
b0 = b0,
s = s)
T7 <- sNorm(N = N,
b0 = b0,
s = s)
# Simulate data for all other nodes with data for parent nodes
# simulated before the data is simulated for the child nodes. In
# other words, the order the data are simulated depends on who the
# parents and children are for each node.
T8 <- cNorm(N = N,
parentData = list(T7),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U5, T8),
b0 = b0,
b1 = c(ss, ss),
s = s)
T1 <- cNorm(N = N,
parentData = list(U1),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U2, T1),
b0 = b0,
b1 = c(ss, ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U3, T1, T6),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(U4, T6, T7),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
return (cbind(U1, U2, U3, U4, U5,
T1, T2, T3, T4, T5,
T6, T7, T8))
},
# gn13_cph ------------------------------------------------------------
'gn13_cph' = {
# Simulate data for the source nodes first.
U1 <- sMulti(N = N,
q = q)
U2 <- sMulti(N = N,
q = q)
U3 <- sMulti(N = N,
q = q)
U4 <- sMulti(N = N,
q = q)
U5 <- sMulti(N = N,
q = q)
T6 <- sNorm(N = N,
b0 = b0,
s = s)
T7 <- sNorm(N = N,
b0 = b0,
s = s)
# Simulate data for all other nodes with data for parent nodes
# simulated before the data is simulated for the child nodes. In
# other words, the order the data are simulated depends on who the
# parents and children are for each node.
T8 <- cNorm(N = N,
parentData = list(T7),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U5, T8),
b0 = b0,
b1 = c(ss, ss),
s = s)
T1 <- cNorm(N = N,
parentData = list(U1),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U2, T1),
b0 = b0,
b1 = c(ss, ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U3, T1, T6),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(U4, T6, T7),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(U1, U3, T2, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U1, U2, U3, U4, U5,
T1, T2, T3, T4, T5,
T6, T7, T8, W))
},
# gn13_cph_2 ----------------------------------------------------------
'gn13_cph_2' = {
# Simulate data for the source nodes first.
U1 <- sMulti(N = N,
q = q)
U2 <- sMulti(N = N,
q = q)
U3 <- sMulti(N = N,
q = q)
U4 <- sMulti(N = N,
q = q)
U5 <- sMulti(N = N,
q = q)
T6 <- sNorm(N = N,
b0 = b0,
s = s)
T7 <- sNorm(N = N,
b0 = b0,
s = s)
# Simulate data for all other nodes with data for parent nodes
# simulated before the data is simulated for the child nodes. In
# other words, the order the data are simulated depends on who the
# parents and children are for each node.
T8 <- cNorm(N = N,
parentData = list(T7),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U5, T8),
b0 = b0,
b1 = c(ss, ss),
s = s)
T1 <- cNorm(N = N,
parentData = list(U1),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U2, T1),
b0 = b0,
b1 = c(ss, ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U3, T1, T6),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(U4, T6, T7),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T4, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U1, U2, U3, U4, U5,
T1, T2, T3, T4, T5,
T6, T7, T8, W))
},
# cas_gv_3 ------------------------------------------------------------
'cas_gv_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3),
b0 = b0,
b1 = c(1, 1, 1))
return (cbind(U, T1, T2, T3, W))
},
# cas_gv_3_1 ----------------------------------------------------------
'cas_gv_3_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3),
b0 = b0,
b1 = c(1, 1, 1, 1))
return (cbind(U, T1, T2, T3, W))
},
# cas_gv_4 ------------------------------------------------------------
'cas_gv_4' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3, T4),
b0 = b0,
b1 = c(1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, W))
},
# cas_gv_4_1 ----------------------------------------------------------
'cas_gv_4_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3, T4),
b0 = b0,
b1 = c(1, 1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, W))
},
# cas_gv_5 ------------------------------------------------------------
'cas_gv_5' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3, T4, T5),
b0 = b0,
b1 = c(1, 1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# cas_gv_5_1 ----------------------------------------------------------
'cas_gv_5_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3, T4, T5),
b0 = b0,
b1 = c(1, 1, 1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# ffl_gv_3 ------------------------------------------------------------
'ffl_gv_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, W))
},
# ffl_gv_3_1 ----------------------------------------------------------
'ffl_gv_3_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, W))
},
# ffl_gv_4 ------------------------------------------------------------
'ffl_gv_4' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3, T4),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, W))
},
# ffl_gv_4_1 ----------------------------------------------------------
'ffl_gv_4_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3, T4),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, W))
},
# ffl_gv_5 ------------------------------------------------------------
'ffl_gv_5' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# ffl_gv_5_1 ----------------------------------------------------------
'ffl_gv_5_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# layer ---------------------------------------------------------------
'layer' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6, T7))
},
# layer_cph_3 ---------------------------------------------------------
'layer_cph_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, W))
},
# layer_cph_4 ---------------------------------------------------------
'layer_cph_4' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, W))
},
# layer_cph_4_0 -------------------------------------------------------
'layer_cph_4_0' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T4, T6, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, W))
},
# layer_cph_4_1 -------------------------------------------------------
'layer_cph_4_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T3, T4, T5, T7, W))
},
# layer_cph_4_2 -------------------------------------------------------
'layer_cph_4_2' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T3, T4, T5, T7, W))
},
# layer_cph_4_3 -------------------------------------------------------
'layer_cph_4_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T3, T5, T7, W))
},
# layer_cph_4_4 -------------------------------------------------------
'layer_cph_4_4' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T3, T5, T7, W))
},
# layer_cph_4_5 -------------------------------------------------------
'layer_cph_4_5' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T1, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T3, T7, W))
},
# layer_cp ------------------------------------------------------------
'layer_cp' = {
U <- sMulti(N = N,
q = q)
C1 <- sNorm(N = N,
b0 = b0,
s = s)
C2 <- sNorm(N = N,
b0 = b0,
s = s)
T1 <- cNorm(N = N,
parentData = list(U, C1),
b0 = b0,
b1 = c(ss, ssc),
s = s)
T2 <- cNorm(N = N,
parentData = list(U, C2),
b0 = b0,
b1 = c(ss, ssc),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2, C1),
b0 = b0,
b1 = c(ss, ss, ssc),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2, C2),
b0 = b0,
b1 = c(ss, ssc),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, C1, C2))
},
# layer_iv ------------------------------------------------------------
'layer_iv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
C1 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ssc),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
C2 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ssc),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2, C1),
b0 = b0,
b1 = c(ss, ss, ssc),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2, C2),
b0 = b0,
b1 = c(ss, ssc),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, C1, C2))
},
# star ----------------------------------------------------------------
'star' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5))
},
# star_cph ------------------------------------------------------------
'star_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T4),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# star_cph_2 ----------------------------------------------------------
'star_cph_2' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T2, T3, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# star_cph_3 ----------------------------------------------------------
'star_cph_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T2, T3, T4, T5, T6),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, W))
},
# star_cph_3_1 --------------------------------------------------------
'star_cph_3_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T2, T3, T4, T5, T6),
b0 = b0,
b1 = c(1, 1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, T5, T6, W))
})
}
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#' simdata
#'
#' A function for simulating data under various topologies for continuous and
#' mixed data.
#'
#' @param graph A character string of the graph for which data will be
#' simulated. The graphs that can be chosen are m1_ge, m1_gv, m1_cp, m1_cc,
#' m1_iv, m2_ge, m2_gv, m2_cp, m2_cc, m2_iv, m3_gv, m3_cp, m3_cc, m3_iv, mp_ge,
#' mp_gv, gn4, gn5, gn8, gn11, layer, layer_cp, layer_iv, and star.
#'
#' The following figures show the graph for each of the topologies listed above.
#' The nodes with a circle around the name are normally distributed and the
#' nodes with a diamond around the name are distributed multinomial. The nodes
#' labeled with a C represent confounding variables. The Principle of Mendelian
#' Randomization (PMR) can be used on graphs with discrete and continuous
#' random variables. This introduces the constraint that the continuous random
#' variables cannot be parents of discrete random variables and edges between
#' these types of variables only have two states: absent and directed with the
#' discrete random variable as the parent.
#'
#' m1_ge - Topology M1 with three continuous random variables. In this case M1
#' has two other Markov equivalent graphs.
#'
#' m1_gv - Topology M1 with one discrete random variable U and two continuous
#' random variables. When using the PMR this graph does not have any other
#' Markov equivalent graphs.
#'
#' We consider three types of confounding variables (Yang et al., 2017):
#'
#' m1_cp - Topology M1 with n common parent confounding variables.
#'
#' m1_cc - Topology M1 with n common child confounding variables.
#'
#' m1_iv - Topology M1 with n intermediate confounding variables.
#'
#' \if{html}{\figure{m1.png}{options: width="75\%" alt="Figure: m1.png"}}
#' \if{latex}{\figure{m1.pdf}{options: width=12cm}}
#'
#' m2_ge - Topology M2 with three continuous random variables. This graph is a v
#' structure and does not have any other Markov equivalent graphs.
#'
#' m2_gv - Topolog M2 with one discrete random variable U and two continuous
#' random variables. This graph is a v structure and does not have any other
#' Markov equivalent graphs.
#'
#' m2_cp - Topology M2 with n common parent confounding variables.
#'
#' m2_cc - Topology M2 with n common child confounding variables.
#'
#' m2_iv - Topology M2 with n intermediate confounding variables.
#'
#' \if{html}{\figure{m2.png}{options: width="75\%" alt="Figure: m2.png"}}
#' \if{latex}{\figure{m2.pdf}{options: width=12cm}}
#'
#' m3_gv - Topolog M3 with one discrete random variable U and two continuous
#' random variables.
#'
#' m3_cp - Topology M3 with n common parent confounding variables.
#'
#' m3_cc - Topology M3 with n common child confounding variables.
#'
#' m3_iv - Topology M3 with n intermediate confounding variables.
#'
#' \if{html}{\figure{m3.png}{options: width="75\%" alt="Figure: m3.png"}}
#' \if{latex}{\figure{m3.pdf}{options: width=12cm}}
#'
#' mp_ge - The multi-parent topology with continuous random variables. This
#' graph is made up of multiple v structures and has no other Markov equivalent
#' graphs.
#'
#' mp_gv - The multi-parent topology with one discrete random variable. This
#' graph is made up of multiple v structures and has no other Markov equivalent
#' graphs.
#'
#' \if{html}{\figure{mp.png}{options: width="55\%" alt="Figure: mp.png"}}
#' \if{latex}{\figure{mp.pdf}{options: width=7cm}}
#'
#' gn4 - Topology GN4 is formed by combining topologies M1 and M2. The Markov
#' equivalence class for this topology is made up of three different graphs.
#'
#' gn5 - Topology GN5 has three other Markov equivalent graphs.
#'
#' gn8 - Topology GN8 has three overlapping cycles, two v structures, and two
#' other Markov equivalent graphs.
#'
#' gn11 - Topology GN11 has two sub-graphs separated by a v structure at node
#' T6.
#'
#' \if{html}{\figure{gn.png}{options: width="55\%" alt="Figure: gn.png"}}
#' \if{latex}{\figure{gn.pdf}{options: width=7cm}}
#'
#' layer - The layer topology has no other Markov equivalent graphs when using
#' the PMR and is made up of multiple M1 topologies.
#'
#' layer_cp - The layer topology with 2 common parent confounding variables.
#'
#' layer_iv - The layer topology with 2 intermediate confounding variables.
#'
#' \if{html}{\figure{layer.png}{options: width="75\%" alt="Figure: layer.png"}}
#' \if{latex}{\figure{layer.pdf}{options: width=12cm}}
#'
#' star - The star topology has no other Markov equivalent graphs when using the
#' PMR and is made up of multiple M1 topologies.
#'
#' \if{html}{\figure{star.png}{options: width="25\%" alt="Figure: star.png"}}
#' \if{latex}{\figure{star.pdf}{options: width=2.5cm}}
#'
#' @param N The number of observations to simulate.
#'
#' @param b0 The mean of the variable if it does not have any parents. If the
#' variable has one or more parents it is the slope in the linear model that is
#' the mean of the normally distributed variables.
#'
#' @param ss The coefficient of the parent nodes (if there are any) in the
#' linear model that is the mean of the normally distributed variables. This
#' coefficient is referred to as the signal strength.
#'
#' @param s The standard deviation of the normal distribution.
#'
#' @param p The probability of success for a binomial random variable.
#'
#' @param q The frequency of the reference allele.
#'
#' @param ssc The signal strength of the confounding variables.
#'
#' @param nConfounding The number of confounding variables to simulate.
#'
#' @return A matrix with the variables across the columns and the observations
#' down the rows.
#'
#' @references Yang, F., Wang, J., The GTEx Consortium, Pierce, B. L., and
#' Chen, L. S. (2017).
#' Identifying cis-mediators for trans-eQTLs across many human tissues using
#' genomic mediation analysis. \emph{Genome Res.} 27, 1859-1871.
#'
#' @examples
#' # Generate data under topology GN4.
#' data_gn4 <- simdata(graph = 'gn4',
#' N = 500,
#' b0 = 1,
#' ss = 1,
#' s = 1)
#'
#' # Display the first few rows of the data.
#' data_gn4[1:5, ]
#'
#' # Generate data under topology M1 with 3 intermediate confounding variables.
#' data_m1_iv <- simdata(graph = 'm1_iv',
#' N = 500,
#' b0 = 0,
#' ss = 1,
#' s = 1,
#' q = 0.1,
#' ssc = 0.2,
#' nConfounding = 3)
#'
#' # Show the first few rows of the data.
#' data_m1_iv[1:5, ]
#'
#' @export
#'
simdata <- function (graph = 'gn4',
N = 500,
b0 = 0,
ss = 1,
s = 1,
p = 0.6,
q = 0.45,
ssc = 0.2,
nConfounding = 2) {
switch(graph,
# m1_ge ---------------------------------------------------------------
'm1_ge' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(T1, T2, T3))
},
# m1_gv ---------------------------------------------------------------
'm1_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2))
},
# m1_cph --------------------------------------------------------------
'm1_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss))
return (cbind(U, T1, T2, W))
},
# m1_cp ---------------------------------------------------------------
'm1_cp' = {
U <- sMulti(N = N,
q = q)
# create a vector with the signal strength of the parents for T1
ssT1 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT1[[1]] <- ss
# Create a list with the data of the parents for T1 as the elements
# of the list.
parT1 <- vector(mode = 'list',
length = nConfounding + 1)
parT1[[1]] <- U
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT2[[1]] <- ss
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding + 1)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- sNorm(N = N,
b0 = b0,
s = s)
# Add the signal strength of the confounding variable to the ssT1
# vector.
ssT1[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT1[[a + 1]] <- con[, a]
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a + 1]] <- con[, a]
}
T1 <- cNorm(N = N,
parentData = parT1,
b0 = b0,
b1 = ssT1,
s = s)
# Fill in the first element of parT2 with data generated from T1.
parT2[[1]] <- T1
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
return (cbind(U, T1, T2, con))
},
# m1_cc ---------------------------------------------------------------
'm1_cc' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ssc, ssc),
s = s)
}
return (cbind(U, T1, T2, con))
},
# m1_iv ---------------------------------------------------------------
'm1_iv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT2[[1]] <- ss
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding + 1)
parT2[[1]] <- T1
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ssc),
s = s)
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a + 1]] <- con[, a]
}
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
return (cbind(U, T1, T2, con))
},
# m2_ge ---------------------------------------------------------------
'm2_ge' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T3 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1, T3),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3))
},
# m2_gv ---------------------------------------------------------------
'm2_gv' = {
U <- sMulti(N = N,
q = q)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T1 <- cNorm(N = N,
parentData = list(U, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(U, T1, T2))
},
# m2_cp ---------------------------------------------------------------
'm2_cp' = {
U <- sMulti(N = N,
q = q)
# create a vector with the signal strength of the parents for T1
ssT1 <- vector(mode = 'numeric',
length = nConfounding + 2)
# Fill in the first and second elements of the ssT1 vector. Both
# elements will be ss becasue the first two parents of T1 are U and
# T2.
ssT1[[1]] <- ss
ssT1[[2]] <- ss
# Create a list with the data of the parents for T1 as the elements
# of the list.
parT1 <- vector(mode = 'list',
length = nConfounding + 2)
# The first parent of T1 is U.
parT1[[1]] <- U
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding)
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- sNorm(N = N,
b0 = b0,
s = s)
# Add the signal strength of the confounding variable to the ssT1
# vector.
ssT1[[a + 2]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT1[[a + 2]] <- con[, a]
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a]] <- con[, a]
}
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
# Fill in the second element of the parT1 list with the data from T2
parT1[[2]] <- T2
T1 <- cNorm(N = N,
parentData = parT1,
b0 = b0,
b1 = ssT1,
s = s)
return (cbind(U, T1, T2, con))
},
# m2_cc ---------------------------------------------------------------
'm2_cc' = {
U <- sMulti(N = N,
q = q)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T1 <- cNorm(N = N,
parentData = list(U, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ssc, ssc),
s = s)
}
return (cbind(U, T1, T2, con))
},
# m2_iv ---------------------------------------------------------------
'm2_iv' = {
U <- sMulti(N = N,
q = q)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# create a vector with the signal strength of the parents for T1
ssT1 <- vector(mode = 'numeric',
length = nConfounding + 2)
# The first two elements will be the signal strength of U and T2.
ssT1[[1]] <- ss
ssT1[[2]] <- ss
# Create a list with the data of the parents for T1 as the elements
# of the list.
parT1 <- vector(mode = 'list',
length = nConfounding + 2)
# The first to elements will be the data from U and T2.
parT1[[1]] <- U
parT1[[2]] <- T2
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ssc),
s = s)
# Add the signal strength of the confounding variable to the ssT1
# vector.
ssT1[[a + 2]] <- ssc
# Add the data of the current confounding variable to the parT1
# list.
parT1[[a + 2]] <- con[, a]
}
T1 <- cNorm(N = N,
parentData = parT1,
b0 = b0,
b1 = ssT1,
s = s)
return (cbind(U, T1, T2, con))
},
# m3_gv ---------------------------------------------------------------
'm3_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2))
},
# m3_cph --------------------------------------------------------------
'm3_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, W))
},
# m3_cp ---------------------------------------------------------------
'm3_cp' = {
U <- sMulti(N = N,
q = q)
# create a vector with the signal strength of the parents for T1
ssT1 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT1[[1]] <- ss
# Create a list with the data of the parents for T1 as the elements
# of the list.
parT1 <- vector(mode = 'list',
length = nConfounding + 1)
parT1[[1]] <- U
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT2[[1]] <- ss
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding + 1)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- sNorm(N = N,
b0 = b0,
s = s)
# Add the signal strength of the confounding variable to the ssT1
# vector.
ssT1[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT1[[a + 1]] <- con[, a]
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a + 1]] <- con[, a]
}
T1 <- cNorm(N = N,
parentData = parT1,
b0 = b0,
b1 = ssT1,
s = s)
# Fill in the first element of parT2 with data generated from U.
parT2[[1]] <- U
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
return (cbind(U, T1, T2, con))
},
# m3_cc ---------------------------------------------------------------
'm3_cc' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ssc, ssc),
s = s)
}
return (cbind(U, T1, T2, con))
},
# m3_iv ---------------------------------------------------------------
'm3_iv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
# create a list to hold the data for the confounding variables
con <- matrix(nrow = N,
ncol = nConfounding)
# add column names to the matrix of confounding variables
colnames(con) <- paste0('C', 1:nConfounding)
# create a vector with the signal strength of the parents for T2
ssT2 <- vector(mode = 'numeric',
length = nConfounding + 1)
ssT2[[1]] <- ss
# Create a list with the data of the parents for T2 as the elements
# of the list.
parT2 <- vector(mode = 'list',
length = nConfounding + 1)
parT2[[1]] <- U
# Simulate the data for the hidden variables
for (a in 1:nConfounding) {
con[, a] <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ssc),
s = s)
# Add the signal strength of the confounding variable to the ssT2
# vector.
ssT2[[a + 1]] <- ssc
# Add the data of the current confounding variable to the parT2
# list.
parT2[[a + 1]] <- con[, a]
}
T2 <- cNorm(N = N,
parentData = parT2,
b0 = b0,
b1 = ssT2,
s = s)
return (cbind(U, T1, T2, con))
},
# m4_ge ---------------------------------------------------------------
'm4_ge' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
# Generate data for T2 and T3 with a v structure at T3.
T2_a <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3_a <- cNorm(N = N,
parentData = list(T1, T2_a),
b0 = b0,
b1 = c(ss, ss),
s = s)
# Generate data for T2 and T3 with a v structure at T2.
T3_b <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T2_b <- cNorm(N = N,
parentData = list(T1, T3_b),
b0 = b0,
b1 = c(ss, ss),
s = s)
# Create a vector representing a coin toss.
coin_flip <- sample(x = 0:1,
size = N,
replace = TRUE)
# Create empty vectors for T2 and T3.
T2 <- vector(mode = 'numeric',
length = N)
T3 <- vector(mode = 'numeric',
length = N)
# Create a vector for T2 that is a combination of T2_a and T2_b.
T2[coin_flip == 0] <- T2_a[coin_flip == 0]
T2[coin_flip == 1] <- T2_b[coin_flip == 1]
# Create a vector for T3 that is a combination of T3_a and T3_b.
T3[coin_flip == 0] <- T3_a[coin_flip == 0]
T3[coin_flip == 1] <- T3_b[coin_flip == 1]
return (cbind(T1, T2, T3))
},
# m4_gv ---------------------------------------------------------------
'm4_gv' = {
U <- sMulti(N = N,
q = q)
# Generate data for T1 and T2 with a v structure at T2.
T1_a <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2_a <- cNorm(N = N,
parentData = list(U, T1_a),
b0 = b0,
b1 = c(ss, ss),
s = s)
# Generate data for T1 and T2 with a v structure at T1.
T2_b <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T1_b <- cNorm(N = N,
parentData = list(U, T2_b),
b0 = b0,
b1 = c(ss, ss),
s = s)
# Create a vector of 0s and 1s to represent flipping a coin.
coin_flip <- sample(x = 0:1,
size = N,
replace = TRUE)
# Create empty vectors for T1 and T2.
T1 <- vector(mode = 'numeric',
length = N)
T2 <- vector(mode = 'numeric',
length = N)
# Create a vector for T1 that is a combination of T1_a and T1_b.
T1[coin_flip == 0] <- T1_a[coin_flip == 0]
T1[coin_flip == 1] <- T1_b[coin_flip == 1]
# Create a vector for T2 that is a combination of T2_a and T2_b.
T2[coin_flip == 0] <- T2_a[coin_flip == 0]
T2[coin_flip == 1] <- T2_b[coin_flip == 1]
return (cbind(U, T1, T2))
},
# mp_ge ---------------------------------------------------------------
'mp_ge' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T3 <- sNorm(N = N,
b0 = b0,
s = s)
T4 <- cNorm(N = N,
parentData = list(T1, T2, T3),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
return (cbind(T1, T2, T3, T4))
},
# mp_gv ---------------------------------------------------------------
'mp_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T3 <- cNorm(N = N,
parentData = list(U, T1, T2),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
return (cbind(U, T1, T2, T3))
},
# mp_cph --------------------------------------------------------------
'mp_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- sNorm(N = N,
b0 = b0,
s = s)
T3 <- cNorm(N = N,
parentData = list(U, T1, T2),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, W))
},
# gn4 -----------------------------------------------------------------
'gn4' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T4),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3, T4))
},
# gn5 -----------------------------------------------------------------
'gn5' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T3, T4),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3, T4, T5))
},
# gn6 -----------------------------------------------------------------
'gn6' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2, T6),
b0 = b0,
b1 = c(ss, ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(T1, T2, T3, T4, T5, T6))
},
# gn6_gv --------------------------------------------------------------
'gn6_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2, T6),
b0 = b0,
b1 = c(ss, ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U, T3),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6))
},
# gn6_cph -------------------------------------------------------------
'gn6_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T2, T6),
b0 = b0,
b1 = c(ss, ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U, T3),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, W))
},
# gn8 -----------------------------------------------------------------
'gn8' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- sNorm(N = N,
b0 = b0,
s = s)
T5 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T6),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3, T4, T5, T6, T7, T8))
},
# gn8_gv --------------------------------------------------------------
'gn8_gv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- sNorm(N = N,
b0 = b0,
s = s)
T5 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T6),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, T8))
},
# gn8_cph -------------------------------------------------------------
'gn8_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- sNorm(N = N,
b0 = b0,
s = s)
T5 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T6),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T3, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, T8, W))
},
# gn8_cph_2 -------------------------------------------------------------
'gn8_cph_2' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- sNorm(N = N,
b0 = b0,
s = s)
T5 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T6),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T1, T5),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T3, T7),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, T8, W))
},
# gn11 ----------------------------------------------------------------
'gn11' = {
T1 <- sNorm(N = N,
b0 = b0,
s = s)
T7 <- sNorm(N = N,
b0 = b0,
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
T8 <- cNorm(N = N,
parentData = list(T7),
b0 = b0,
b1 = c(ss),
s = s)
T9 <- cNorm(N = N,
parentData = list(T8),
b0 = b0,
b1 = c(ss),
s = s)
T10 <- cNorm(N = N,
parentData = list(T9),
b0 = b0,
b1 = c(ss),
s = s)
T11 <- cNorm(N = N,
parentData = list(T10),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T5, T7),
b0 = b0,
b1 = c(ss, ss),
s = s)
return (cbind(T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11))
},
# gn13 ----------------------------------------------------------------
'gn13' = {
# Simulate data for the source nodes first.
U1 <- sMulti(N = N,
q = q)
U2 <- sMulti(N = N,
q = q)
U3 <- sMulti(N = N,
q = q)
U4 <- sMulti(N = N,
q = q)
U5 <- sMulti(N = N,
q = q)
T6 <- sNorm(N = N,
b0 = b0,
s = s)
T7 <- sNorm(N = N,
b0 = b0,
s = s)
# Simulate data for all other nodes with data for parent nodes
# simulated before the data is simulated for the child nodes. In
# other words, the order the data are simulated depends on who the
# parents and children are for each node.
T8 <- cNorm(N = N,
parentData = list(T7),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U5, T8),
b0 = b0,
b1 = c(ss, ss),
s = s)
T1 <- cNorm(N = N,
parentData = list(U1),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U2, T1),
b0 = b0,
b1 = c(ss, ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U3, T1, T6),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(U4, T6, T7),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
return (cbind(U1, U2, U3, U4, U5,
T1, T2, T3, T4, T5,
T6, T7, T8))
},
# gn13_cph ------------------------------------------------------------
'gn13_cph' = {
# Simulate data for the source nodes first.
U1 <- sMulti(N = N,
q = q)
U2 <- sMulti(N = N,
q = q)
U3 <- sMulti(N = N,
q = q)
U4 <- sMulti(N = N,
q = q)
U5 <- sMulti(N = N,
q = q)
T6 <- sNorm(N = N,
b0 = b0,
s = s)
T7 <- sNorm(N = N,
b0 = b0,
s = s)
# Simulate data for all other nodes with data for parent nodes
# simulated before the data is simulated for the child nodes. In
# other words, the order the data are simulated depends on who the
# parents and children are for each node.
T8 <- cNorm(N = N,
parentData = list(T7),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U5, T8),
b0 = b0,
b1 = c(ss, ss),
s = s)
T1 <- cNorm(N = N,
parentData = list(U1),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U2, T1),
b0 = b0,
b1 = c(ss, ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U3, T1, T6),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(U4, T6, T7),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(U1, U3, T2, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U1, U2, U3, U4, U5,
T1, T2, T3, T4, T5,
T6, T7, T8, W))
},
# gn13_cph_2 ----------------------------------------------------------
'gn13_cph_2' = {
# Simulate data for the source nodes first.
U1 <- sMulti(N = N,
q = q)
U2 <- sMulti(N = N,
q = q)
U3 <- sMulti(N = N,
q = q)
U4 <- sMulti(N = N,
q = q)
U5 <- sMulti(N = N,
q = q)
T6 <- sNorm(N = N,
b0 = b0,
s = s)
T7 <- sNorm(N = N,
b0 = b0,
s = s)
# Simulate data for all other nodes with data for parent nodes
# simulated before the data is simulated for the child nodes. In
# other words, the order the data are simulated depends on who the
# parents and children are for each node.
T8 <- cNorm(N = N,
parentData = list(T7),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(U5, T8),
b0 = b0,
b1 = c(ss, ss),
s = s)
T1 <- cNorm(N = N,
parentData = list(U1),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U2, T1),
b0 = b0,
b1 = c(ss, ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U3, T1, T6),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(U4, T6, T7),
b0 = b0,
b1 = c(ss, ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T4, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U1, U2, U3, U4, U5,
T1, T2, T3, T4, T5,
T6, T7, T8, W))
},
# cas_gv_3 ------------------------------------------------------------
'cas_gv_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3),
b0 = b0,
b1 = c(1, 1, 1))
return (cbind(U, T1, T2, T3, W))
},
# cas_gv_3_1 ----------------------------------------------------------
'cas_gv_3_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3),
b0 = b0,
b1 = c(1, 1, 1, 1))
return (cbind(U, T1, T2, T3, W))
},
# cas_gv_4 ------------------------------------------------------------
'cas_gv_4' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3, T4),
b0 = b0,
b1 = c(1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, W))
},
# cas_gv_4_1 ----------------------------------------------------------
'cas_gv_4_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3, T4),
b0 = b0,
b1 = c(1, 1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, W))
},
# cas_gv_5 ------------------------------------------------------------
'cas_gv_5' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3, T4, T5),
b0 = b0,
b1 = c(1, 1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# cas_gv_5_1 ----------------------------------------------------------
'cas_gv_5_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3, T4, T5),
b0 = b0,
b1 = c(1, 1, 1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# ffl_gv_3 ------------------------------------------------------------
'ffl_gv_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, W))
},
# ffl_gv_3_1 ----------------------------------------------------------
'ffl_gv_3_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, W))
},
# ffl_gv_4 ------------------------------------------------------------
'ffl_gv_4' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3, T4),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, W))
},
# ffl_gv_4_1 ----------------------------------------------------------
'ffl_gv_4_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3, T4),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, W))
},
# ffl_gv_5 ------------------------------------------------------------
'ffl_gv_5' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T3, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# ffl_gv_5_1 ----------------------------------------------------------
'ffl_gv_5_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T4),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T1, T2, T3, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# layer ---------------------------------------------------------------
'layer' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6, T7))
},
# layer_cph_3 ---------------------------------------------------------
'layer_cph_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, W))
},
# layer_cph_4 ---------------------------------------------------------
'layer_cph_4' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, W))
},
# layer_cph_4_0 -------------------------------------------------------
'layer_cph_4_0' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2),
b0 = b0,
b1 = c(ss, ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T4, T6, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, W))
},
# layer_cph_4_1 -------------------------------------------------------
'layer_cph_4_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T3, T4, T5, T7, W))
},
# layer_cph_4_2 -------------------------------------------------------
'layer_cph_4_2' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T3, T4, T5, T7, W))
},
# layer_cph_4_3 -------------------------------------------------------
'layer_cph_4_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T3, T5, T7, W))
},
# layer_cph_4_4 -------------------------------------------------------
'layer_cph_4_4' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T5),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T3, T5, T7, W))
},
# layer_cph_4_5 -------------------------------------------------------
'layer_cph_4_5' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(U, T3, T1, T7),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T3, T7, W))
},
# layer_cp ------------------------------------------------------------
'layer_cp' = {
U <- sMulti(N = N,
q = q)
C1 <- sNorm(N = N,
b0 = b0,
s = s)
C2 <- sNorm(N = N,
b0 = b0,
s = s)
T1 <- cNorm(N = N,
parentData = list(U, C1),
b0 = b0,
b1 = c(ss, ssc),
s = s)
T2 <- cNorm(N = N,
parentData = list(U, C2),
b0 = b0,
b1 = c(ss, ssc),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2, C1),
b0 = b0,
b1 = c(ss, ss, ssc),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2, C2),
b0 = b0,
b1 = c(ss, ssc),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, C1, C2))
},
# layer_iv ------------------------------------------------------------
'layer_iv' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
C1 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ssc),
s = s)
T2 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
C2 <- cNorm(N = N,
parentData = list(T2),
b0 = b0,
b1 = c(ssc),
s = s)
T3 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1, T2, C1),
b0 = b0,
b1 = c(ss, ss, ssc),
s = s)
T6 <- cNorm(N = N,
parentData = list(T2, C2),
b0 = b0,
b1 = c(ss, ssc),
s = s)
T7 <- cNorm(N = N,
parentData = list(T3),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5, T6, T7, C1, C2))
},
# star ----------------------------------------------------------------
'star' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
return (cbind(U, T1, T2, T3, T4, T5))
},
# star_cph ------------------------------------------------------------
'star_cph' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T1, T2, T4),
b0 = b0,
b1 = c(ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# star_cph_2 ----------------------------------------------------------
'star_cph_2' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T2, T3, T4, T5),
b0 = b0,
b1 = c(ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, W))
},
# star_cph_3 ----------------------------------------------------------
'star_cph_3' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T2, T3, T4, T5, T6),
b0 = b0,
b1 = c(ss, ss, ss, ss, ss))
return (cbind(U, T1, T2, T3, T4, T5, T6, W))
},
# star_cph_3_1 --------------------------------------------------------
'star_cph_3_1' = {
U <- sMulti(N = N,
q = q)
T1 <- cNorm(N = N,
parentData = list(U),
b0 = b0,
b1 = c(ss),
s = s)
T2 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T3 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T4 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T5 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
T6 <- cNorm(N = N,
parentData = list(T1),
b0 = b0,
b1 = c(ss),
s = s)
W <- cBinom(N = N,
parentData = list(T2, T3, T4, T5, T6),
b0 = b0,
b1 = c(1, 1, 1, 1, 1))
return (cbind(U, T1, T2, T3, T4, T5, T6, W))
})
}
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