posc 0.2.0
New features
posc_emp() estimates a POSC directly from raw data,
with no normality assumption: a spline logistic regression on all
pairwise differences (or a random subset of max_pairs of
them for large samples), set up so that P(0) = 0.5 and P(-delta) = 1 -
P(delta), with a person-level bootstrap confidence band.posc_emp() also computes observed proportions in bins
of the difference (bins = number of equal-count bins or a
vector of breakpoints). plot() draws them as points with
simultaneous (family-wise) confidence intervals, computed by a max-t
bootstrap on the empirical-logit scale so they are asymmetric and stay
within [0, 1]. posc_bins() returns them as a table.plot(x, mark = ...) highlights chosen differences: a
guide line runs from the x-axis up to the curve, with the probability
labelled (add mark_ci = TRUE for its confidence interval).
Works for single curves and comparisons.posc_uni(), posc_multi(),
posc_lm() and posc_emp() return a
"posc" object with print(),
summary(), plot(), autoplot(),
predict() and as.data.frame() methods.
predict() gives probabilities and confidence intervals at
any difference.plot(x, full = TRUE) draws the full sigmoid over
negative and positive differences.posc_comp() accepts any number of curves (named or with
labels) and uses a homogeneity test of the correlations
when there are more than two.posc_multi() and posc_lm() gain
adjust, which uses the adjusted multiple correlation by
default to correct the optimism of in-sample R.- Redesigned plots: by default just the curve and its confidence band,
with axis lines, very light gridlines and a probability axis in steps of
0.1. Options add percentages (
percent = TRUE), remove
gridlines (grid = FALSE), an automatic title and subtitle
(title = TRUE, subtitle = TRUE), and for
empirical curves the binned proportions (bins = TRUE) and
normal-theory curve (reference = TRUE). Comparisons use a
colour-blind-safe palette.
Development
- The core of the package was written by the author; Claude
(Anthropic) assisted with extending, documenting and testing the code in
this version, under the author’s direction and review.
Bug fixes
- The x-axis of
posc_uni(), posc_multi() and
posc_lm() plots was stretched by a factor of sqrt(2)
relative to the plotted probabilities. Differences are now on the
predictor’s own scale.
posc_lm() referred to an object mdl that
did not exist and assumed the response was named y; it now
works with any lm() model.- Functions no longer change the global
warn option.
Breaking changes
- Curves are computed for signed differences, and always as the
probability of a higher outcome. With a negative relationship
the curve falls below 0.5, rather than being relabelled as the
probability of a lower outcome.
ci.lvl is now level; colours are set in
plot() rather than when the curve is created;
posc_multi(outcome_idx =) is now outcome =
(the old name still works, with a warning).- The returned object is a
"posc" list with a
curve data frame (delta, p,
lower, upper), replacing
predict_matrix and posc_plot_object.
- Plots are no longer drawn automatically; call
plot() on
the result.
- The unused ggExtra dependency was removed.
posc 0.1.0