simdata_quantized_emm: Simulate quantized exposures for testing methods

In qgcompint: Quantile G-Computation Extensions for Effect Measure Modification

Simulate quantized exposures for testing methods

Description

Simulate quantized exposures for testing methods

Usage

simdata_quantized_emm(
  outcometype = c("continuous", "logistic", "survival"),
  n = 100,
  corr = NULL,
  b0 = 0,
  mainterms = c(1, 0, 0, 0),
  prodterms = c(1, 0, 0, 0),
  ztype = "binary",
  q = 4,
  yscale = 1,
  shape0 = 3,
  scale0 = 5,
  censtime = 4,
  ncheck = TRUE,
  ...
)

Arguments

outcometype	Character variable that is one of c("continuous", "logistic", "survival"). Selects what type of outcome should be simulated (or how). continuous = normal, continous outcome, logistic= binary outcome from logistic model, survival = right censored survival outcome from Weibull model.
n	Sample size
corr	NULL, or vector of correlations between the first exposure and subsequent exposures (if length(corr) < (length(coef)-1), then this will be backfilled with zeros)
b0	(continuous, binary outcomes) model intercept
mainterms	beta coefficients for X in the outcome model at referent (0) level of interacting variable
prodterms	product term coefficients for interacting variable
ztype	type of interacting variable: "continuous", "binary", "categorical"
q	Number of levels or "quanta" of each exposure
yscale	(continuous outcomes) error scale (residual error) for normally distributed outcomes
shape0	(survival outcomes) baseline shape of weibull distribution rweibull
scale0	(survival outcomes) baseline scale of weibull distribution rweibull
censtime	(survival outcomes) administrative censoring time
ncheck	(logical, default=TRUE) adjust sample size if needed so that exposures are exactly evenly distributed (so that qgcomp::quantize(exposure) = exposure)
...	unused

Details

Simulate continuous (normally distributed errors), binary (logistic function), or event-time outcomes as a linear function

Value

a data frame

See Also

qgcomp.boot, and qgcomp.noboot

Examples

set.seed(50)
qdat = simdata_quantized_emm(
  outcomtype="continuous",
  n=10000, corr=c(.9,.3,0,0), mainterms=c(1,1,0,0), prodterms=c(1,1,0,0),
  q = 8
)
cor(qdat)
qdat = simdata_quantized_emm(
  outcomtype="continuous",
  n=10000, corr=c(-.9,.3,0,0), mainterms=c(1,2,0,0), prodterms=c(1,1,0,0),
  q = 4
)
cor(qdat)
table(qdat$x1)
qgcomp.emm.noboot(y~x1+x2+x3+x4,expnms = c("x1", "x2", "x3", "x4"), emmvar = "z", data=qdat)

qgcompint documentation built on March 22, 2022, 5:06 p.m.