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which argument in
plot_survival() and plot_stress().which argument in the
plot functions. Now it contains the proper default curve names. If it is
NA only the axes and labels will get drawn.NULL
to the console.predict_mixture(), which was a temporary
development name, to multi_tox().proportion_ca in the mixture model
multi_tox() was renamed and its value reversed. It is now
called sa_contribution and specifies the proportion of
stress addition in the calculation of toxicant stress. To convert your
code from the old version use this equation:
sa_contribution = 1 - proportion_ca.stress_tox_sam to stress_tox_sa in
the output of multi_tox().plot_stress() with argument
which = NULL would result in an error. Now it correctly
draws the axes without data.log10_ticks() for calculating tick
mark labels and positions on a base 10 logarithmic axis.multiple_stress for use with
multi_tox().predict_mixture() now also returns the various
stresses.curves data frame in the output of
ecxsys() now contains a column with the concentrations
which are used for the plot functions in this package. This is useful
for generating a nicer concentration axis.ec():
response_value to effect in the
output list.response_level of 0 or 100 is now allowed. 0 returns
the concentration 0 and 100 returns the concentration Inf.
Previously this resulted in an error.plot_effect() and
plot_stress() where supplying an empty vector caused the
four standard curves to show. Now setting which to an empty
vector or NULL shows just the axes. The default value is
NA.mixture_effect column in the
predict_mixture output data frame to
effect.predict_mixture() must be the same length. The longer
length must be a multiple of the shorter length because the shorter
vector gets recycled to the longer length.plot_effect() and plot_stress().
You can now control whether the observed values (the points) should be
plotted using the which argument.sys_tox_not_fitted and
sys_tox_env_not_fitted to sys_tox_observed and
sys_tox_env_observed in the output of
ecxsys().predict_mixture() now accepts multiple values for the
concentration of the second toxicant. Both concentration vectors must be
the same length.predict_mixture() now returns a data frame with the
concentrations and effects. Previously it was only a vector of
effects.predict_mixture() received a new argument “effect_max”
which scales the returned effect values.predict_mixture() to use
underscore letters a and b instad of 1 and 2. For example model_1 is now
model_a.predict_mixture() and
included example of symmetry.ec() now raises an error if the curve does not cross
the desired response level.ecxsys() gained a new argument
curves_concentration_max which allows setting the maximum
concentration of the predicted curves.plot_effect() to also
show effect_tox and effect_tox_env.plot_effect() and
plot_stress() gained a which argument that
controls which curves are plotted. Consequently, the
show_LL5_model argument of plot_effect() was
removed.xlab and ylab to
plot_stress.main to both plot functions.predict_mixture() for the prediction of the
effects of mixtures of two toxicants.ecxsys() and
predict_ecxsys().ecxsys().hormesis_index argument from
ecxsys(). Use hormesis_concentration
instead.predict_ecxsys() replaces
fn() from the ecxsys() output.ec().ec() more flexible. It now also accepts a
data.frame with a concentration column and a column of response
values.plot_effect().plot_system_stress() to
plot_stress() because it is planned to plot more stresses
with this function in a future update.predict_ecxsys().NEWS.md file to track changes to the
package.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.