High-Performance Open-Source Archive
interactions(), which ranks numeric predictor
pairs by the count-weighted magnitude of their centred second-order ALE
surfaces.top_n to bivariate() for
method = "ale" so the plotting interface can rank all
eligible pairs with interactions() and display only the
strongest interaction surfaces.bivariate(method = "ale") now masks grid cells that
contain no observations as NA in the returned surface and
renders them in the fill scale’s na.value (default light
grey) rather than colouring them with a value extrapolated from
neighbouring cells. This prevents the interaction surface from
displaying confident estimates over regions of feature space that the
data does not support, which previously misled readers when predictors
were correlated.bivariate(method = "ale") now defaults to
n = 10 (the second-order surface uses an n x n
cell grid; n = 40 left most cells empty for typical data)
and to rug = TRUE so the data density is always
visible.ale_surface_limits() is robust to surfaces that are
entirely NA.Rd to describe the half-cell centring
convention used for the 2D ALE accumulation and the empty-cell masking
behaviour.multimodel() and
mapcurve(), matching the univariate ALE support and using a
shared level order across ensemble members when
multimodel() averages unordered factor effects.extrapolate = FALSE to bivariate()
so unsupported ALE grid cells remain masked by default but can be shown
on request using the interpolated values already used internally for
accumulation.univariate() with
method = "profile", "pdp", "ice",
and "ice+pdp" so single-profile, partial dependence, and
ICE plots share one entry point.method = "ale" to univariate() for
accumulated local effects curves on numeric predictors.univariate(method = "ale") to warn and skip
factor predictors instead of failing when numeric predictors are also
available.univariate() and mapcurve() sampling
controls so n sets numeric grid resolution while
background_n sets the number of randomly sampled background
rows used for PDP/ICE.interval to univariate() and
mapcurve() so method = "pdp" can draw central
quantile ribbons for numeric predictors.univariate() so
PDP/ICE methods can draw more background predictor combinations when
predict_data comes from a SpatRaster.bivariate() for bivariate response surfaces with
static heatmap and contour views.bivariate() with method = "pdp"
and method = "ale", plus optional marginal rugs for numeric
predictor pairs in static plots.plotly is installed.fun in
multimodel() so mixed model types can use model-specific
prediction wrappers before averaging curves.type
to plot_type so model-specific type arguments
can still be passed through ... to
predict().bivariate(plot_type = "heatmap") to use a
viridis fill scale by default and to stop drawing contour overlays on
heatmaps.univariate() examples
for profile, PDP, and ICE + PDP plots.univariate(), so categorical panels no longer imply numeric
intervals.
Need mirroring services?
Contact our team at info@vpspulse.com.
Mirror powered by VPSpulse
Infrastructure sponsored by VPSPulse & Secure Payments by ArionPay.