timepoints() fails when SL is not used to model treatment

[sgruber65]

Hi,
When I model treatment using glm, a subsequent call to timepoints() fails. The error message is:

Error in terms.formula(newformula, specials = names(attr(termobj, "specials"))) :
invalid model formula in ExtractVars
In addition: Warning message:
In if (class(SL.trt) != "SuperLearner") { :
the condition has length > 1 and only the first element will be used

Here is a reproducible example straight from the survtmle help file:

set.seed(1234)
n <- 200
trt <- rbinom(n, 1, 0.5)
adjustVars <- data.frame(W1 = round(runif(n)), W2 = round(runif(n, 0, 2)))
ftime <- round(1 + runif(n, 1, 4) - trt + adjustVars$W1 + adjustVars$W2)
ftype <- round(runif(n, 0, 1))

fit1 <- survtmle(
  ftime = ftime, ftype = ftype,
  trt = trt, adjustVars = adjustVars,
  glm.trt = "W1 + W2",
  glm.ftime = "trt + W1 + W2",
  glm.ctime = "trt + W1 + W2",
  method = "mean", t0 = 6
)
\# the next call  fails
timepoints(fit1, t = 4)

But if instead we specify SL.trt = "SL.glm", it works fine.

fit1.alt <- survtmle(
  ftime = ftime, ftype = ftype,
  trt = trt, adjustVars = adjustVars,
  SL.trt = "SL.glm",
  glm.ftime = "trt + W1 + W2",
  glm.ctime = "trt + W1 + W2",
  method = "mean", t0 = 6
)
timepoints(fit1.alt, t=4)

One clue: this line in the checkInputs function
tglm <- any(class(object$trtMod[[1]]) %in% c("glm", "speedglm"))
returns FALSE if we specify glm.trt, because class(object$trtMod) is not a list (it evaluates to: "glm, lm")