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To identify any individual likelihood predictions that may be more influential or unusual.
Note this function may have a long runtime.
ind_roc( xpdb, mapping = NULL, cutpoint = 1, type = "ca", title = "Individual ROC curves | @run", subtitle = "Ofv: @ofv, Eps shrink: @epsshk", caption = "@dir | Page @page of @lastpage", tag = NULL, facets, .problem, quiet, ... )
The desired plot
<xp_xtras> or <xpose_data> object
xp_xtras
xpose_data
ggplot2 style mapping
ggplot2
<numeric> Of defined probabilities, which one to use in plots.
numeric
See Details.
Plot title
Plot subtitle
Plot caption
Plot tag
Additional facets
Problem number
Silence extra debugging output
Any additional aesthetics.
For type-based customization of plots:
c ROC curve (using geom_path)
c
geom_path
k Key points on ROC curve (where on curve the threshold is thres_fixed) (using geom_point)
k
thres_fixed
geom_point
p ROC space points (using geom_point)
p
t ROC space text (using geom_text)
t
geom_text
a AUC in bottom right (using geom_label)
a
geom_label
if (FALSE) { vismo_pomod %>% set_var_types(.problem=1, catdv=DV, dvprobs=matches("^P\\d+$")) %>% set_dv_probs(.problem=1, 0~P0,1~P1,ge(2)~P23) %>% ind_roc() }
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