survlab 0.2.0
impute_nondetect() gains a control
argument accepting a survival::survreg.control() object.
This allows users to tune the underlying survreg fitting
algorithm - most notably maxiter - for datasets where the
default iteration limit causes convergence warnings.impute_nondetect() gains a zero_value
argument (default 1e-5): exact zeros are replaced by it
when fitting the log-normal, Weibull, exponential and log-logistic
distributions, which previously failed silently on data containing
zeros. The zeros are kept in the final values.impute_nondetect() now records the AIC and convergence
of every distribution (attribute fits) and selects the best
model among the converged fits only. If none converged, the best
non-converged fit is used, a warning is raised, and attribute
best_converged is FALSE. Convergence warnings
from the individual fits are no longer passed on to the caller.- Fixed: with
min_value = 0, logistic imputations were
not truncated at 0, and draws below 0 were set to exactly 0. They are
now drawn from the logistic truncated between min_value and
the detection limit.
survlab 0.1.0