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This release adds support for quantal endpoints, introduces configuration presets, and changes how model selection works. As model selection has changed, results with the new version may be slightly different. The changes are listed in order of importance.
The model.list argument had no
effect. toxdrc_modelling() accepted
model.list but omitted it from the list it returned, so
modelcomp() always fell back to its internal defaults.
Custom model lists are now used in its place.
The default model list for continuous data is now
LL.4 alone, rather than LL.4,
LN.4, W1.4 and W2.4. Model
comparison is opt-in. To restore comparisons, supply more than one entry
to model.list to restore it.
getECx() now defaults to
type = "relative", matching
toxdrc_modelling(). Previously the two disagreed, so
calling the function directly and running the pipeline gave different
answers from the same arguments.
runtoxdrc() gained the N,
endpoint and preset arguments, and its
configuration arguments now default to NULL so that a
preset can supply them. Named calls are unaffected; positional calls
after quiet will shift.
model.list must be a named list whose names match
the model each entry holds, for example
list("LL.4" = LL.4()). Its default is now NULL
and is selected based on endpoint type.
Naming a column in IDcols that does not exist is now
an error. Previously averageresponse() warned and
continued, so the column was quietly absent from the results.
A reference, blank or control label that does not match the data
is now an error. Previously it resulted in NaN through the
threshold calculation.
Configuration helpers validate their arguments, so calls such as
toxdrc_qc(cv.flag = "yes") or
toxdrc_modelling(level = 95) now fail rather than being
accepted.
Quantal endpoints. Set
endpoint = toxdrc_endpoint(type = "binomial") and give the
group sizes with N to fit binary data such as mortality.
Models are fitted with drc::drm(type = "binomial"),
weighted by group size, and default to comparing LL.2
against LL.3u. Replicates are pooled by group size rather
than averaged, so a proportion from 30 of 60 counts for more than one
from 1 of 4. The response may be given as a proportion or, with
response.type = "count", as the number affected.
Blank correction, normalization and Grubbs’ outlier removal are
refused for quantal data, each with an error naming the alternative.
"Res var" is unavailable as model.metric,
since a binomial fit has no residual variance.
interpolateECx() estimates an
effect concentration by log-linear interpolation between the bracketing
concentrations, for quantal tests where no concentration produced a
partial effect and no model can be fitted. The EC50 is the geometric
mean of the two, which does not depend on the unknown shape of the
curve. Enable it in the pipeline with
toxdrc_modelling(interpolate = TRUE); such estimates are
marked best_model_name = "interpolated" and carry no
confidence interval. Effect levels other than 50% are computed with a
warning, since with no partial responses they largely restate the
bracketing concentrations.
toxdrc_preset() returns complete
configurations for common designs: "continuous",
"quantal", and "normalized" for
blank-corrected data expressed relative to a control. Pass one as
preset to runtoxdrc(); anything given
explicitly overrides it. Presets are ordinary lists and can be printed
to see every setting. Contact the package authours for support with
additional presets.
acutetox, a simulated acute
lethality dataset with a quantal endpoint. Its three substances exercise
a normal fit, the no-partial-response case that triggers interpolation,
and control mortality that favours LL.3u. Counts are
constructed deterministically from known curves, so the generating
values can be recovered exactly. See
data-raw/acutetox.R.
getECx() returned a bare data frame when
drc::ED() failed, even when list_obj was
supplied, so runtoxdrc() received the wrong object for that
subset. It now returns the list.
The placeholder returned when no estimate could be produced had
different column names from a successful result, because
data.frame() renamed “Effect Measure” and “Std. Error” by
default.
Condensing results failed when a run produced both subsets with point estimates and subsets without, because the two produced different columns.
Fields such as model and model_df
disappeared from an entry when nothing could be fitted, because
assigning NULL to a list element removes it. They are now
present and NULL.
mselect2() returned the baseline model twice, once
in its own right and once as a member of the comparison list, and
collapsed to a vector when only one model was supplied.
A failed lack-of-fit test aborted the whole run.
drc::modelFit() performs its own optimisation and can fail
on data the model itself fitted; it is now caught and recorded as
NA.
runtoxdrc() failed when IDcols was not
supplied. The whole dataset is now treated as a single subset.
Model comparison was fragile in a way that could silently produce
a table of NA scores. mselect2() refits
candidates with update(), which re-evaluates the model’s
stored call, so models must be fitted in a way that leaves that call
resolvable.
With quiet = TRUE, drc’s own reports of failed
optimisations are no longer printed. They are written directly to the
console rather than raised as conditions, so neither
suppressWarnings() nor suppressMessages()
affected them.
Errors are raised as classed conditions inheriting from
toxdrc_error, so they can be caught and tested by class
rather than by matching message text. Messages name the offending value
and, where useful, the alternative.
Function documentation revised throughout, including corrected cross references and several malformed help pages.
Test coverage expanded substantially, including the previously untested internals and the numerical plausibility of estimates rather than only their structure.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.