bayesnecfit
or bayesmanecfit
.R/nsec.R
nsec.Rd
Extracts the predicted NSEC value as desired from an object of class
bayesnecfit
or bayesmanecfit
.
nsec(
object,
sig_val = 0.01,
precision = 1000,
posterior = FALSE,
x_range = NA,
hormesis_def = "control",
xform = identity,
prob_vals = c(0.5, 0.025, 0.975)
)
An object of class bayesnecfit
or
bayesmanecfit
returned by bnec
.
Probability value to use as the lower quantile to test significance of the predicted posterior values. against the lowest observed concentration (assumed to be the control), to estimate NEC as an interpolated NOEC value from smooth ECx curves.
The number of unique x values over which to find NSEC - large values will make the NSEC estimate more precise.
A logical
value indicating if the full
posterior sample of calculated NSEC values should be returned instead of
just the median and 95 credible intervals.
A range of x values over which to consider extracting NSEC.
A character
vector, taking values
of "max" or "control". See Details.
A function to apply to the returned estimated concentration values.
A vector indicating the probability values over which to return the estimated NSEC value. Defaults to 0.5 (median) and 0.025 and 0.975 (95 percent credible intervals).
A vector containing the estimated NSEC value, including upper and lower 95% credible interval bounds.
For hormesis_def
, if "max", then NSEC values are calculated
as a decline from the maximum estimates (i.e. the peak at NEC);
if "control", then ECx values are calculated relative to the control, which
is assumed to be the lowest observed concentration.
Calls to functions ecx
and nsec
and
compare_fitted
do not require the same level of flexibility
in the context of allowing argument newdata
(from a posterior_predict
perspective) to
be supplied manually, as this is and should be handled within the function
itself. The argument precision
controls how precisely the
ecx
or nsec
value is estimated, with
argument x_range
allowing estimation beyond the existing range of
the observed data (otherwise the default range) which can be useful in a
small number of cases. There is also no reasonable case where estimating
these from the raw data would be of value, because both functions would
simply return one of the treatment concentrations, making NOEC a better
metric in that case.