sim: Simulate Observed or Full Data from 'DAG' Object
In osofr/simcausal: Simulating Longitudinal Data with Causal Inference Applications

View source: R/simulation.r

sim	R Documentation

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,
  rndseed.reset.node = NULL, verbose = getOption("simcausal.verbose"))

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 `actions` is omitted, the function returns simulated observed data (see `simobs`).
`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.
`rndseed.reset.node`	When `rndseed` is specified, use this argument to specify the name of the `DAG` node at which the random number generator seed is reset back to `NULL` (simulation function will call `set.seed(NULL)`). Can be useful if one wishes to simulate data using the set seed `rndseed` only for the first K nodes of the DAG and use an entirely random sample when simulating the rest of the nodes starting at K+1 and on. The name of such (K+1)th order `DAG` node should be then specified with this argument.
`verbose`	Set to `TRUE` to print messages on status and information to the console. Turn this off by default using options(simcausal.verbose=FALSE).

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.

References

Sofrygin O, van der Laan MJ, Neugebauer R (2017). "simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data." Journal of Statistical Software, 81(2), 1-47. doi: 10.18637/jss.v081.i02.

Examples

t_end <- 10
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)

osofr/simcausal documentation built on Oct. 21, 2022, 3:09 a.m.