Extracts the predicted NSEC value as desired from an object of class
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
bayesmanecfit returned by
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.
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.
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.
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
compare_fitted do not require the same level of flexibility
in the context of allowing argument
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
nsec value is estimated, with
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.