View source: R/base_simhelpers.R
simdata_quantized_emm | R Documentation |
Simulate quantized exposures for testing methods
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, ... )
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 |
Simulate continuous (normally distributed errors), binary (logistic function), or event-time outcomes as a linear function
a data frame
qgcomp.boot
, and qgcomp.noboot
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)
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