simcausal (version 0.1)

set.DAG: Create and lock DAG object

Description

Check current DAG (created with node) for errors and consistency of its node distributions, set the observed data generating distribution. Attempts to simulates several observations to catch any errors in DAG definition. New nodes cannot be added after function set.DAG has been applied.

Usage

set.DAG(DAG, vecfun)

Arguments

DAG

Named list of node objects that together will form a DAG. Temporal ordering of nodes is either determined by the order in which the nodes were added to the DAG (using +node(...) interface) or with an optional "order" argument to node()<

vecfun

A character vector with names of the vectorized user-defined node formula functions. See examples and the vignette for more information.

Value

• A DAG (S3) object, which is a list consisting of node object(s) sorted by their temporal order

Examples

#---------------------------------------------------------------------------------------
# EXAMPLE 1A: Define some Bernoulli nodes, survival outcome Y and put it together in a
# DAG object
#---------------------------------------------------------------------------------------
W1 <- node(name="W1", distr="rbern", prob=plogis(-0.5), order=1)
W2 <- node(name="W2", distr="rbern", prob=plogis(-0.5 + 0.5*W1), order=2)
A <- node(name="A", distr="rbern", prob=plogis(-0.5 - 0.3*W1 - 0.3*W2), order=3)
Y <- node(name="Y", distr="rbern", prob=plogis(-0.1 + 1.2*A + 0.3*W1 + 0.3*W2), order=4)
D1A <- set.DAG(c(W1,W2,A,Y))

#---------------------------------------------------------------------------------------
# EXAMPLE 1B: Same as 1A using +node interface and no order argument
#---------------------------------------------------------------------------------------
D1B <- DAG.empty()
D1B <- D1B + node(name="W1", distr="rbern", prob=plogis(-0.5))
D1B <- D1B + node(name="W2", distr="rbern", prob=plogis(-0.5 + 0.5*W1))
D1B <- D1B + node(name="A", distr="rbern", prob=plogis(-0.5 - 0.3*W1 - 0.3*W2))
D1B <- D1B + node(name="Y", distr="rbern", prob=plogis(-0.1 + 1.2*A + 0.3*W1 + 0.3*W2))
D1B <- set.DAG(D1B)

#---------------------------------------------------------------------------------------
# EXAMPLE 1C: Same as 1A and 1B using add.nodes interface and no order argument
#---------------------------------------------------------------------------------------
D1C <- DAG.empty()
D1C <- add.nodes(D1C, node(name="W1", distr="rbern", prob=plogis(-0.5)))
D1C <- add.nodes(D1C, node(name="W2", distr="rbern", prob=plogis(-0.5 + 0.5*W1)))
D1C <- add.nodes(D1C, node(name="A", distr="rbern", prob=plogis(-0.5 - 0.3*W1 - 0.3*W2)))
D1C <- add.nodes(D1C, node(name="Y", distr="rbern", prob=plogis(-0.1 + 1.2*A + 0.3*W1 + 0.3*W2)))
D1C <- set.DAG(D1C)

#---------------------------------------------------------------------------------------
# EXAMPLE 1D: Add a uniformly distributed node and redefine outcome Y as categorical
#---------------------------------------------------------------------------------------
D_unif <- DAG.empty()
D_unif <- D_unif + node("W1", distr="rbern", prob=plogis(-0.5))
D_unif <- D_unif + node("W2", distr="rbern", prob=plogis(-0.5 + 0.5*W1))
D_unif <- D_unif + node("W3", distr="runif", min=plogis(-0.5 + 0.7*W1 + 0.3*W2), max=10)
D_unif <- D_unif + node("Anode", distr="rbern", prob=plogis(-0.5 - 0.3*W1 - 0.3*W2 - 0.2*sin(W3)))
# Categorical syntax 1 (probabilities as values)
D_cat_1 <- D_unif + node("Y", distr="rcategor", probs={0.3;0.4})
# Categorical syntax 2 (probabilities as formulas)
D_cat_2 <- D_unif + node("Y", distr="rcategor",
                         probs={plogis(-0.1 + 1.2*Anode + 0.3*W1 + 0.3*W2 + 0.2*cos(W3));
                                plogis(-0.5 + 0.7*W1)})
D_cat_1 <- set.DAG(D_cat_1)
D_cat_2 <- set.DAG(D_cat_2)

#---------------------------------------------------------------------------------------
# EXAMPLE 1E: Defining new distribution function 'rbern', defining and passing a custom
# vectorized node function 'customfun'
#---------------------------------------------------------------------------------------
rbern <- function(n, prob) { # defining a bernoulli wrapper based on R rbinom function
 rbinom(n=n, prob=prob, size=1)
}
customfun <- function(arg, lambda) {
 res <- ifelse(arg==1,lambda,0.1)
 res
}
D <- DAG.empty()
D <- D + node("W1", distr="rbern", prob=0.05)
D <- D + node("W2", distr="rbern", prob=customfun(W1,0.5))
D <- D + node("W3", distr="rbern", prob=ifelse(W1==1,0.5,0.1))
D1d <- set.DAG(D, vecfun=c("customfun"))
sim1d <- simobs(D1d, n=200, rndseed=1)

#---------------------------------------------------------------------------------------
# EXAMPLE 2: Define time varying nodes for time points t = 0 to 16
#---------------------------------------------------------------------------------------
t_end <- 16
DTV <- DAG.empty()
DTV <- DTV + node(name="L2", t=0, distr="rbern", prob=0.05)
DTV <- DTV + node(name="L1", t=0, distr="rbern", prob=ifelse(L2[0]==1,0.5,0.1))
DTV <- DTV + node(name="A1", t=0, distr="rbern",
	prob=ifelse(L1[0]==1 & L2[0]==0, 0.5, ifelse(L1[0]==0 & L2[0]==0, 0.1,
                                         ifelse(L1[0]==1 & L2[0]==1, 0.9, 0.5))))
DTV <- DTV + node(name="A2", t=0, distr="rbern",
	prob=0)
DTV <- DTV + node(name="Y",  t=0, distr="rbern",
	prob=plogis(-6.5 + L1[0] + 4*L2[0] + 0.05*I(L2[0]==0)), EFU=TRUE)
DTV <- DTV + node(name="L2", t=1:t_end, distr="rbern",
	prob=ifelse(A1[t-1]==1, 0.1, ifelse(L2[t-1]==1, 0.9, min(1,0.1 + t/.(t_end)))))
DTV <- DTV + node(name="A1", t=1:t_end, distr="rbern",
	prob=ifelse(A1[t-1]==1, 1, ifelse(L1[0]==1 & L2[0]==0, 0.3,
                              ifelse(L1[0]==0 & L2[0]==0, 0.1,
                               ifelse(L1[0]==1 & L2[0]==1, 0.7, 0.5)))))
DTV <- DTV + node(name="A2", t=1:t_end, distr="rbern",
	prob=0, order=8+4*(0:(t_end-1)))
DTV <- DTV + node(name="Y", t=1:t_end, distr="rbern",
	prob=plogis(-6.5 + L1[0] + 4*L2[t] + 0.05*sum(I(L2[0:t]==rep(0,(t+1))))), EFU=TRUE)
DTV <- set.DAG(DTV)