library(qgcomp)
library(qgcompint)
set.seed(23)
dat2 <- simdata_quantized_emm(
  outcometype="logistic",
  # sample size
  n = 100,
  # correlation between x1 and x2, x3, ...
  corr=c(0.8, 0.6, 0.3, -0.3, -0.3, -0.3),
  # model intercept
  b0=-2,
  # linear model coefficients for x1, x2, ... at referent level of interacting variable
  mainterms=c(0.3, -0.1, 0.1, 0.0, 0.3, 0.1, 0.1),
  # linear model coefficients for product terms between x1, x2, ... and interacting variable
  prodterms = c(1.0, 0.0, 0.0, 0.0, 0.2, 0.2, 0.2),
  # type of interacting variable
  ztype = "categorical",
  # number of levels of exposure
  q = 4,
  # residual variance of y
  yscale = 2.0
)

head(dat2)
table(dat2$z)
table(dat2$y)
dat2$z = as.factor(dat2$z)
qfit2 <- qgcomp.emm.glm.noboot(y~x1,
                           data = dat2,
                           expnms = paste0("x", 1:1),
                           emmvar = "z",
                           q = 4)
qfit2
qfit2$fit