Cox for fixedLassoInf -- "Error in term1[o] <- ff(a[o]) * exp(-(a[o]^2 - z[o]^2)/2) : NAs are not allowed in subscripted assignments" throws despite no NA's in data

[violet-nova]

I have verified there is no missing data. The glmnet function runs fine. beta_hat creates a vector of values for every predictor. But when I run fixedLassoInf I get the error. I have tried tweaking arguments like gridrange, tol.beta, tol.kkt and it does not go away.

For this dataset, n = 398, p = 51 (after dummifying factors).

I believe I've faithfully followed the steps recommended for Cox in example documentation. Below is the traceback, and then the code I've been using that creates it.

Traceback:

• 8 -- bryc.tnorm.surv(z, mm[oo], sd, a, b)
• 7 -- tnorm.surv(limits.info$estimate, param, limits.info$sd, limits.info$vlo,
  limits.info$vup, bits)
• 6 -- fun(grid)
• 5 -- grid.search(param_grid, pivot, alpha/2, 1 - alpha/2, gridpts,
  griddepth)
• 4 -- TG.interval.base(limits.info, alpha = alpha, gridrange = gridrange,
  griddepth = griddepth, flip = flip, bits = bits)
• 3 -- TG.interval(bbar, A1, b1, vj, MM, alpha, flip = (sign_bhat[jj] ==
  -1))
• 2 -- fixedCoxLassoInf(x, y, status, beta, lambda, alpha = alpha, type = "partial",
  tol.beta = tol.beta, tol.kkt = tol.kkt, gridrange = gridrange,
  bits = bits, verbose = verbose, this.call = this.call)
• 1 -- fixedLassoInf(x = xsurv, y = coxdf$censor_time,
  beta = beta_hat, lambda = coxLasso$lambda.min,
  status = status, family = "cox")

My code:

` Make a df that contains: predictors, censor time, and outcome (0 or 1 numeric)
coxdf<- df %>%dplyr::select(any_of(preds), censor_time,
outcome)

[Make X matrix]
xsurv <- coxdf %>%dplyr::select(any_of(preds)) %>%
makeX(na.impute = TRUE) %>% scale(TRUE, FALSE) ## Centering x

[Determine Optimal Lambda from CV]
coxLassocv <- cv.glmnet(x = xsurv,
y = Surv(coxdf$censor_time,
as.numeric(coxdf$outcome)-1
),
family = "cox",
) ## runs without issue

[Originally, I tried going straight from cv.glmnet. When that failed, I built a separate object from glmnet() but it did not fix the issue:]
coxglm <- glmnet(x = xsurv,
y = Surv(coxdf$censor_time,
as.numeric(coxdf$outcome)-1
),
lambda = coxLassocv$lambda.min,
family = "cox"
) ## also runs without issue

[Generate beta_hat as recommended in documentation, this seemingly runs without issue: ]
beta_hat = as.numeric(coef(coxglm,
x=xsurv, y=Surv(coxdf$censor_time,
as.numeric(coxdf$outcome)-1
),
s=coxLassocv$lambda.min/398, ## n = 398 for this dataset.
exact=TRUE
) # the beta values from lasso
) ## Generates a vector of values for all predictors, as expected
status = as.numeric(coxdf$outcome)-1 ## I tried assigning a variable to this rather than calling it directly, it again changed nothing

[The moment of truth, this is where I get the error message:]
out = fixedLassoInf(x=xsurv,
y = coxdf$censor_time,
beta = beta_hat, lambda = coxLassocv$lambda.min,
status= status, family="cox"
) ## At this step, I keep getting: "Error in term1[o] <- ff(a[o]) * exp(-(a[o]^2 - z[o]^2)/2) : NAs are not allowed in subscripted assignments"`