simcausal (version 0.2.0)

sim: Simulate Observed or Full Data from DAG Object

Description

This function simulates full data based on a list of intervention DAGs, returning a list of data.frames. See the vignette for examples and detailed description.

Usage

sim(DAG, actions, n, wide = TRUE, LTCF = NULL, rndseed = NULL)

Arguments

DAG

A DAG objects that has been locked with set.DAG(DAG). Observed data from this DAG will be simulated if actions argument is omitted.

actions

Character vector of action names which will be extracted from the DAG object. Alternatively, this can be a list of action DAGs selected with A(DAG) function, in which case the argument DAG is unused. When this argument is missing

n

Number of observations to sample.

wide

A logical, if TRUE the output data is generated in wide format, if FALSE, the output longitudinal data in generated in long format

LTCF

If forward imputation is desired for the missing variable values, this argument should be set to the name of the node that indicates the end of follow-up event.

rndseed

Seed for the random number generator.

Value

• If actions argument is missing a simulated data.frame is returned, otherwise the function returns a named list of action-specific simulated data.frames with action names giving names to corresponding list items.

Forward Imputation

By default, when LTCF is left unspecified, all variables that follow after any end of follow-up (EFU) event are set to missing (NA). The end of follow-up event occurs when a binary node of type EFU=TRUE is equal to 1, indicating a failing or right-censoring event. To forward impute the values of the time-varying nodes after the occurrence of the EFU event, set the LTCF argument to a name of the EFU node representing this event. For additional details and examples see the vignette and doLTCF function.

See Also

simobs - a wrapper function for simulating observed data only; simfull - a wrapper function for simulating full data only; doLTCF - forward imputation of the missing values in already simulating data; DF.to.long, DF.to.longDT - converting longitudinal data from wide to long formats.

Other simulation.functions: simfull; simobs

Examples

t_end <- 16
lDAG <- DAG.empty()
lDAG <- lDAG + 
	node(name = "L2", t = 0, distr = "rconst", const = 0) + 
	node(name = "A1", t = 0, distr = "rconst", const = 0) + 
	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))))) + 
	node(name = "A1", t = 1:t_end, distr = "rbern", 
 	prob = ifelse(A1[t - 1]  ==  1, 1, 
 			 ifelse(L2[0] == 0, 0.3, 
			  ifelse(L2[0] == 0, 0.1, 
			   ifelse(L2[0] == 1, 0.7, 0.5))))) + 
	node(name = "Y", t = 1:t_end, distr = "rbern", 
 	prob = plogis(-6.5 + 4 * L2[t] + 0.05 * sum(I(L2[0:t] == rep(0,(t + 1))))), 
 	EFU = TRUE)
lDAG <- set.DAG(lDAG)
#---------------------------------------------------------------------------------------
# EXAMPLE 1. No forward imputation.
#---------------------------------------------------------------------------------------
Odat.wide <- sim(DAG = lDAG, n = 1000, rndseed = 123)
Odat.wide[c(21,47), 1:18]
Odat.wideLTCF <- sim(DAG = lDAG, n = 1000, LTCF = "Y", rndseed = 123)
Odat.wideLTCF[c(21,47), 1:18]
#---------------------------------------------------------------------------------------
# EXAMPLE 2. With forward imputation.
#---------------------------------------------------------------------------------------
Odat.wideLTCF2 <- doLTCF(data = Odat.wide, LTCF = "Y")
Odat.wideLTCF2[c(21,47), 1:18]
all.equal(Odat.wideLTCF, Odat.wideLTCF2)