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"""Optimal data-driven regression discontinuity plots (Calonico, Cattaneo &
Titiunik 2015, JASA) with end-to-end parity against R ``rdrobust`` 4.0.0's
``rdplot()``.
An RD plot has two ingredients (CCT 2015, Section 2.1): global order-``p``
polynomial fits of the outcome on the running variable, estimated separately
on each side of the cutoff, and binned local sample means of the outcome over
a disjoint partition of each side's support. :class:`RDPlot` selects the
number of bins with the paper's data-driven selectors (IMSE-optimal or
mimicking-variance, evenly-spaced or quantile-spaced partitions, spacings or
polynomial-regression variance estimators - the eight ``binselect`` values)
and returns the full set of plot quantities; rendering is a thin optional
matplotlib layer on the result.
Parity target: ``rdplot()`` in the CRAN rdrobust 4.0.0 sources (rdplot.R; the
line references in comments below point there). All numerical outputs -
``J``/``J_IMSE``/``J_MV``, the implied scale and WIMSE weights, bin means,
per-bin CIs, and the global-fit coefficients/curves - are golden-tested
against R output on the vendored Senate dataset and simulated designs.
Notes on scope (documented seams, mirroring :class:`RegressionDiscontinuity`):
sampling weights and R's ``subset=`` are not parameters (filter the DataFrame
instead); ``covs_eval`` is fixed at R's default ``"mean"``. R prints an
unconditional ``message()`` whenever covariates are used with the global
polynomial fit ("may deliver results that are not visually compatible with
the local binned means"); that caveat lives here in the docstring rather than
as an unavoidable runtime warning.
"""
import
math
import
warnings
from
dataclasses
import
dataclass
from
typing
import
Any
,
Dict
,
List
,
Optional
,
Tuple
,
Union
import
numpy
as
np
import
pandas
as
pd
from
scipy
import
stats
from
.
_base
import
BaseEstimator
from
.
_deprecation
import
NOT_SUPPLIED
,
require_arg
,
resolve_renamed_kwarg
from
.
_rdrobust_port
import
(
_covs_gamma
,
_normalize_kernel
,
_var0
,
covs_drop_fun
,
qrXXinv
,
rdrobust_kweight
,
)
from
.
results_base
import
Diagnostic
from
.
utils
import
validate_covariate_names
__all__
=
[
"RDPlot"
,
"RDPlotResult"
]
# binselect value -> (partition meth, variance family, target, description).
# Description strings are transcribed exactly from rdplot.R:395-446.
_BINSELECT_INFO
:
Dict
[
str
,
Tuple
[
str
,
str
,
str
,
str
]]
=
{
"es"
: (
"es"
,
"hat"
,
"imse"
,
"IMSE-optimal evenly-spaced method using spacings estimators"
),
"espr"
: (
"es"
,
"chk"
,
"imse"
,
"IMSE-optimal evenly-spaced method using polynomial regression"
),
"esmv"
: (
"es"
,
"hat"
,
"mv"
,
"mimicking variance evenly-spaced method using spacings estimators"
,
),
"esmvpr"
: (
"es"
,
"chk"
,
"mv"
,
"mimicking variance evenly-spaced method using polynomial regression"
,
),
"qs"
: (
"qs"
,
"hat"
,
"imse"
,
"IMSE-optimal quantile-spaced method using spacings estimators"
),
"qspr"
: (
"qs"
,
"chk"
,
"imse"
,
"IMSE-optimal quantile-spaced method using polynomial regression"
,
),
"qsmv"
: (
"qs"
,
"hat"
,
"mv"
,
"mimicking variance quantile-spaced method using spacings estimators"
,
),
"qsmvpr"
: (
"qs"
,
"chk"
,
"mv"
,
"mimicking variance quantile-spaced method using polynomial regression"
,
),
}
# masspoints="adjust" remap under heavy ties (rdplot.R:130-135): the spacings
# variance estimators degenerate on tied data, so R switches each spacings
# selection to its polynomial-regression sibling. Explicit *pr picks are
# untouched.
_MASSPOINTS_REMAP
=
{
"es"
:
"espr"
,
"esmv"
:
"esmvpr"
,
"qs"
:
"qspr"
,
"qsmv"
:
"qsmvpr"
}
_KERNEL_TYPE_LABEL
=
{
"uniform"
:
"Uniform"
,
"epanechnikov"
:
"Epanechnikov"
,
"triangular"
:
"Triangular"
,
}
_PairF
=
Tuple
[
float
,
float
]
_PairI
=
Tuple
[
int
,
int
]
def
_r_seq_by
(
from_
:
float
,
to
:
float
,
by
:
float
)
->
np
.
ndarray
:
"""R ``seq(from, to, by)`` for ``by > 0`` (seq.default): the point count
is ``as.integer((to - from)/by + 1e-10)`` (truncation with R's fuzz) and
the grid is clamped into ``to`` via ``pmin``."""
m
=
int
((
to
-
from_
)
/
by
+
1e-10
)
return
np
.
minimum
(
from_
+
np
.
arange
(
m
+
1
)
*
by
,
to
)
def
_find_interval
(
x
:
np
.
ndarray
,
vec
:
np
.
ndarray
)
->
np
.
ndarray
:
"""R ``findInterval(x, vec, rightmost.closed=TRUE)`` (1-based interval
ids). ``searchsorted(side='right')`` counts elements ``<= x``, which is
findInterval's answer everywhere except ``x == vec[last]``, where
rightmost.closed folds the point into the last interval. Values beyond
``vec[last]`` keep id ``len(vec)`` exactly like R - reachable when the
quantile prob grid's last point lands one ulp short of 1 (the ``j/J``
float products decide whether R's ``pmin`` clamp engages; the identical
IEEE arithmetic here reproduces whichever way it falls)."""
idx
=
np
.
searchsorted
(
vec
,
x
,
side
=
"right"
)
idx
[
x
==
vec
[
-
1
]]
=
len
(
vec
)
-
1
return
idx
def
_pow_outer
(
u
:
np
.
ndarray
,
degree
:
int
)
->
np
.
ndarray
:
"""R ``outer(u, 0:degree, "^")`` - elementwise pow, NOT the successive
products of ``rdrobust_vander`` (rdplot.R builds every design this way)."""
return
np
.
power
.
outer
(
u
,
np
.
arange
(
degree
+
1
))
def
_resolve_pair
(
value
:
Union
[
None
,
float
,
int
,
Tuple
[
Any
,
Any
],
List
[
Any
]],
default
:
float
)
->
Tuple
[
float
,
float
]:
"""R's scalar-or-length-2 argument convention (rdplot.R:77-104)."""
if
value
is
None
:
return
float
(
default
),
float
(
default
)
if
isinstance
(
value
, (
tuple
,
list
)):
return
float
(
value
[
0
]),
float
(
value
[
1
])
return
float
(
value
),
float
(
value
)
@
dataclass
class
RDPlotResult
(
Diagnostic
):
"""Quantities of a data-driven RD plot (field names follow R's rdplot
return list; ``vars_bins`` columns carry R's ``rdplot_*`` names).
``J`` is always integral: a fractional ``scale * J`` product takes the
ceiling per CCT 2015 Equation 2 (see the Deviation note in
:class:`RDPlot` - R 4.0.0 crashes on that surface instead).
``vars_bins`` contains only NON-EMPTY bins (R's ``tapply`` semantics)
while ``bin_avg``/``bin_med`` summarize ALL bin lengths of the partition.
CI columns are always computed (at ``ci_level``); ``ci_requested`` records
whether ``ci=`` was passed and is the default for drawing error bars in
:meth:`plot`.
"""
coef
:
pd
.
DataFrame
vars_bins
:
pd
.
DataFrame
vars_poly
:
pd
.
DataFrame
J
:
_PairF
J_IMSE
:
_PairF
J_MV
:
_PairF
scale
:
_PairF
rscale
:
_PairF
bin_avg
:
_PairF
bin_med
:
_PairF
p
:
int
cutoff
:
float
h
:
_PairF
N
:
_PairI
N_h
:
_PairI
binselect
:
str
kernel_type
:
str
ci_level
:
float
ci_requested
:
bool
covariate_coefficients
:
Optional
[
Dict
[
str
,
float
]]
=
None
covariates_dropped
:
Optional
[
List
[
str
]]
=
None
@
property
def
wimse_variance_weight
(
self
)
->
_PairF
:
"""Implied WIMSE variance weights ``1/(1 + rscale^3)`` per side
(CCT 2015 Supplement S.1; printed by R's ``summary.rdplot``)."""
return
(
1
/
(
1
+
self
.
rscale
[
0
]
**
3
),
1
/
(
1
+
self
.
rscale
[
1
]
**
3
))
@
property
def
wimse_bias_weight
(
self
)
->
_PairF
:
"""Implied WIMSE bias weights ``rscale^3/(1 + rscale^3)`` per side."""
return
(
self
.
rscale
[
0
]
**
3
/
(
1
+
self
.
rscale
[
0
]
**
3
),
self
.
rscale
[
1
]
**
3
/
(
1
+
self
.
rscale
[
1
]
**
3
),
)
def
to_dataframe
(
self
)
->
pd
.
DataFrame
:
"""The non-empty-bin table (``vars_bins``), copied."""
return
self
.
vars_bins
.
copy
()
def
summary
(
self
)
->
str
:
"""Formatted summary mirroring R's ``summary.rdplot`` layout
(rdplot.R:610-654), including the implied scale and WIMSE weights."""
def
_num
(
v
:
float
)
->
str
:
return
f"
{
v
:10.3f
}
"
def
_int
(
v
:
float
)
->
str
:
return
f"
{
v
:10g
}
"
lines
=
[
"RD Plot (CCT 2015 / rdrobust rdplot parity)"
,
""
,
f"Number of Obs.
{
self
.
N
[
0
]
+
self
.
N
[
1
]:10d
}
"
,
f"Kernel
{
self
.
kernel_type
:>10
}
"
,
f"Bin Method
{
self
.
binselect
}
"
,
""
,
f"
{
''
:25
}
{
'Left'
:>10
}
{
'Right'
:>10
}
"
,
f"Number of Obs.
{
self
.
N
[
0
]:10d
}
{
self
.
N
[
1
]:10d
}
"
,
f"Eff. Number of Obs.
{
self
.
N_h
[
0
]:10d
}
{
self
.
N_h
[
1
]:10d
}
"
,
f"Order poly. fit (p)
{
self
.
p
:10d
}
{
self
.
p
:10d
}
"
,
f"BW poly. fit (h)
{
_num
(
self
.
h
[
0
])
}
{
_num
(
self
.
h
[
1
])
}
"
,
f"Number of bins scale
{
_num
(
self
.
scale
[
0
])
}
{
_num
(
self
.
scale
[
1
])
}
"
,
""
,
f"Bins Selected
{
_int
(
self
.
J
[
0
])
}
{
_int
(
self
.
J
[
1
])
}
"
,
f"Average Bin Length
{
_num
(
self
.
bin_avg
[
0
])
}
{
_num
(
self
.
bin_avg
[
1
])
}
"
,
f"Median Bin Length
{
_num
(
self
.
bin_med
[
0
])
}
{
_num
(
self
.
bin_med
[
1
])
}
"
,
""
,
f"IMSE-optimal bins
{
_int
(
self
.
J_IMSE
[
0
])
}
{
_int
(
self
.
J_IMSE
[
1
])
}
"
,
f"Mimicking Variance bins
{
_int
(
self
.
J_MV
[
0
])
}
{
_int
(
self
.
J_MV
[
1
])
}
"
,
""
,
"Relative to IMSE-optimal:"
,
f"Implied scale
{
_num
(
self
.
rscale
[
0
])
}
{
_num
(
self
.
rscale
[
1
])
}
"
,
f"WIMSE variance weight
{
_num
(
self
.
wimse_variance_weight
[
0
])
}
"
f"
{
_num
(
self
.
wimse_variance_weight
[
1
])
}
"
,
f"WIMSE bias weight
{
_num
(
self
.
wimse_bias_weight
[
0
])
}
"
f"
{
_num
(
self
.
wimse_bias_weight
[
1
])
}
"
,
]
if
self
.
covariate_coefficients
is
not
None
:
lines
+=
[
""
,
"Covariate adjustment (nuisance coefficients, not causal effects):"
]
lines
+=
[
f"
{
k
:20
}
{
v
: .6g
}
"
for
k
,
v
in
self
.
covariate_coefficients
.
items
()]
return
"
\n
"
.
join
(
lines
)
def
plot
(
self
,
ax
:
Optional
[
Any
]
=
None
,
title
:
Optional
[
str
]
=
None
,
xlabel
:
Optional
[
str
]
=
None
,
ylabel
:
Optional
[
str
]
=
None
,
dots_color
:
Optional
[
str
]
=
None
,
line_color
:
Optional
[
str
]
=
None
,
show_ci
:
Optional
[
bool
]
=
None
,
)
->
Any
:
"""Render the RD plot (binned means, global polynomial curves, cutoff
line; error bars when CIs were requested at fit time or forced via
``show_ci=True``). Requires matplotlib (an optional dependency).
Returns the matplotlib axes. Rendering is cosmetic - the parity
surface of this port is the NUMBERS on the result object.
"""
try
:
import
matplotlib
.
pyplot
as
plt
except
ImportError
:
raise
ImportError
(
"matplotlib is required for plotting"
)
if
ax
is
None
:
_
,
ax
=
plt
.
subplots
(
figsize
=
(
10
,
6
))
if
show_ci
is
None
:
show_ci
=
self
.
ci_requested
vb
=
self
.
vars_bins
if
show_ci
:
ax
.
errorbar
(
vb
[
"rdplot_mean_bin"
],
vb
[
"rdplot_mean_y"
],
yerr
=
np
.
vstack
(
[
vb
[
"rdplot_mean_y"
]
-
vb
[
"rdplot_ci_l"
],
vb
[
"rdplot_ci_r"
]
-
vb
[
"rdplot_mean_y"
],
]
),
fmt
=
"none"
,
ecolor
=
"gray"
,
elinewidth
=
1
,
alpha
=
0.8
,
zorder
=
1
,
)
ax
.
scatter
(
vb
[
"rdplot_mean_bin"
],
vb
[
"rdplot_mean_y"
],
color
=
dots_color
or
"darkblue"
,
s
=
18
,
zorder
=
2
,
)
# Split by construction (500 points per side): both sides carry an
# x == cutoff endpoint with DIFFERENT fitted values (the jump), so an
# x-based mask would misassign the right curve's cutoff point.
vp
=
self
.
vars_poly
n_side
=
len
(
vp
)
//
2
for
segment
in
(
vp
.
iloc
[:
n_side
],
vp
.
iloc
[
n_side
:]):
ax
.
plot
(
segment
[
"rdplot_x"
],
segment
[
"rdplot_y"
],
color
=
line_color
or
"red"
,
zorder
=
3
,
)
ax
.
axvline
(
self
.
cutoff
,
color
=
"black"
,
linewidth
=
0.8
)
ax
.
set_title
(
title
if
title
is
not
None
else
"RD Plot"
)
ax
.
set_xlabel
(
xlabel
if
xlabel
is
not
None
else
"Running variable"
)
ax
.
set_ylabel
(
ylabel
if
ylabel
is
not
None
else
"Outcome"
)
return
ax
class
RDPlot
(
BaseEstimator
):
"""Data-driven RD plot builder (CCT 2015; rdrobust 4.0.0 ``rdplot()``
parity).
Parameters mirror R's ``rdplot()`` and keep its defaults - including
``kernel="uniform"`` and ``p=4``, which deliberately differ from
:class:`RegressionDiscontinuity`'s estimation defaults (triangular, p=1):
the global fit is a descriptive overlay, not the local RD estimator.
Parameters
----------
cutoff : float, default 0.0
Threshold in the running variable.
p : int, default 4
Order of the global polynomial fits (R accepts any integer >= 0).
nbins : int or (int, int), optional
Manual number of bins per side; overrides the data-driven selector.
Any positive count is accepted (R permissiveness); note that very
large bin counts allocate proportional jump grids and, with
covariates, a dense per-side bin-dummy matrix (documented
performance seam).
binselect : str, default "esmv"
One of ``es, espr, esmv, esmvpr, qs, qspr, qsmv, qsmvpr`` - partition
scheme (evenly/quantile spaced) x target (IMSE-optimal / mimicking
variance) x variance estimator (spacings / polynomial regression).
The spacings variants assume a continuously distributed OUTCOME
(CCT 2015 Theorems 3-4); heavy outcome ties trigger a warning
recommending the ``*pr`` sibling. QS cutpoints follow R: ``quantile``
type-7 interpolation, not the paper's inf-form empirical inverse
(documented deviation-from-paper inherited from rdrobust).
scale : float or (float, float), optional
Multiplies the selected number of bins (undersmoothing knob). A
fractional product is resolved with the ceiling of CCT 2015
Equation 2. Deviation from R: rdrobust 4.0.0 crashes on fractional
products (vector-indexing accident) and rejects a length-2 scale
outright on R >= 4.2 (vectorized-if error); both surfaces work here.
kernel : str, default "uniform"
Weighting for the global polynomial fit within ``h``.
h : float or (float, float), optional
Window for the global fit; default is each side's full range.
support : (float, float), optional
Extended support for bin construction; only WIDENS the observed
range (R semantics).
masspoints : str, default "adjust"
``"check"`` warns when either side has >= 20% duplicate running
values; ``"adjust"`` additionally switches a spacings ``binselect``
to its polynomial-regression sibling; ``"off"`` disables detection.
ci : float, optional
Confidence level (percent) for the per-bin intervals drawn by
:meth:`RDPlotResult.plot`. CI columns are ALWAYS computed (at 95 when
``ci`` is None, matching R); ``ci`` only sets the level and turns the
error bars on.
covs_drop : bool, default True
Drop redundant covariates (R's pipeline: stable name-length sort +
LINPACK-style QR check) with a warning naming the dropped columns;
``False`` raises a deterministic error on collinear covariates.
"""
def
__init__
(
self
,
cutoff
:
float
=
0.0
,
p
:
int
=
4
,
nbins
:
Union
[
None
,
int
,
Tuple
[
int
,
int
]]
=
None
,
binselect
:
str
=
"esmv"
,
scale
:
Union
[
None
,
float
,
Tuple
[
float
,
float
]]
=
None
,
kernel
:
str
=
"uniform"
,
h
:
Union
[
None
,
float
,
Tuple
[
float
,
float
]]
=
None
,
support
:
Optional
[
Tuple
[
float
,
float
]]
=
None
,
masspoints
:
str
=
"adjust"
,
ci
:
Optional
[
float
]
=
None
,
covs_drop
:
bool
=
True
,
):
self
.
cutoff
=
cutoff
self
.
p
=
p
self
.
nbins
=
nbins
self
.
binselect
=
binselect
self
.
scale
=
scale
self
.
kernel
=
kernel
self
.
h
=
h
self
.
support
=
support
self
.
masspoints
=
masspoints
self
.
ci
=
ci
self
.
covs_drop
=
covs_drop
self
.
_validate_constructor_args
()
# ------------------------------------------------------------------
# Configuration plumbing (sklearn-like)
# ------------------------------------------------------------------
@
staticmethod
def
_is_real_scalar
(
val
:
Any
)
->
bool
:
return
isinstance
(
val
, (
int
,
float
,
np
.
integer
,
np
.
floating
))
and
not
isinstance
(
val
, (
bool
,
np
.
bool_
)
)
@
staticmethod
def
_is_int_scalar
(
val
:
Any
)
->
bool
:
return
isinstance
(
val
, (
int
,
np
.
integer
))
and
not
isinstance
(
val
, (
bool
,
np
.
bool_
))
@
classmethod
def
_check_pair
(
cls
,
name
:
str
,
value
:
Any
,
*
,
integer
:
bool
=
False
,
positive
:
bool
=
True
,
)
->
None
:
"""Validate R's scalar-or-length-2 numeric arguments fail-closed."""
items
=
list
(
value
)
if
isinstance
(
value
, (
tuple
,
list
))
else
[
value
]
if
isinstance
(
value
, (
tuple
,
list
))
and
len
(
items
)
!=
2
:
raise
ValueError
(
f"
{
name
}
must be a scalar or a (left, right) pair; got
{
value
!r
}
."
)
for
item
in
items
:
ok
=
cls
.
_is_int_scalar
(
item
)
if
integer
else
cls
.
_is_real_scalar
(
item
)
ok
=
ok
and
np
.
isfinite
(
item
)
and
(
item
>
0
or
not
positive
)
if
not
ok
:
kind
=
"an integer"
if
integer
else
"a finite number"
raise
ValueError
(
f"
{
name
}
entries must be
{
kind
}
> 0; got
{
item
!r
}
."
)
def
_validate_constructor_args
(
self
)
->
None
:
if
not
(
self
.
_is_real_scalar
(
self
.
cutoff
)
and
np
.
isfinite
(
self
.
cutoff
)):
raise
ValueError
(
f"cutoff must be finite; got
{
self
.
cutoff
!r
}
."
)
# R validates p >= 0 and integrality (rdplot.R:173-188); it imposes
# no upper bound (extreme orders fail later in the linear algebra).
if
not
(
self
.
_is_int_scalar
(
self
.
p
)
and
self
.
p
>=
0
):
raise
ValueError
(
f"p must be an integer >= 0; got
{
self
.
p
!r
}
."
)
if
self
.
nbins
is
not
None
:
self
.
_check_pair
(
"nbins"
,
self
.
nbins
,
integer
=
True
)
if
self
.
binselect
not
in
_BINSELECT_INFO
:
raise
ValueError
(
f"binselect must be one of
{
sorted
(
_BINSELECT_INFO
)
}
; got
{
self
.
binselect
!r
}
."
)
if
self
.
scale
is
not
None
:
self
.
_check_pair
(
"scale"
,
self
.
scale
)
_normalize_kernel
(
self
.
kernel
)
# raises on unknown kernel
if
self
.
h
is
not
None
:
self
.
_check_pair
(
"h"
,
self
.
h
)
if
self
.
support
is
not
None
:
if
not
(
isinstance
(
self
.
support
, (
tuple
,
list
))
and
len
(
self
.
support
)
==
2
and
all
(
self
.
_is_real_scalar
(
v
)
and
np
.
isfinite
(
v
)
for
v
in
self
.
support
)
and
float
(
self
.
support
[
0
])
<
float
(
self
.
support
[
1
])
):
raise
ValueError
(
"support must be a (lower, upper) pair of finite numbers "
f"with lower < upper; got
{
self
.
support
!r
}
."
)
if
self
.
masspoints
not
in
(
"check"
,
"adjust"
,
"off"
):
raise
ValueError
(
f"masspoints must be 'check', 'adjust', or 'off'; got
{
self
.
masspoints
!r
}
."
)
if
self
.
ci
is
not
None
and
not
(
self
.
_is_real_scalar
(
self
.
ci
)
and
0
<
self
.
ci
<
100
):
raise
ValueError
(
f"ci must be a confidence level in (0, 100); got
{
self
.
ci
!r
}
."
)
if
not
isinstance
(
self
.
covs_drop
, (
bool
,
np
.
bool_
)):
raise
ValueError
(
f"covs_drop must be a bool; got
{
self
.
covs_drop
!r
}
."
)
# get_params/set_params come from BaseEstimator.
# ------------------------------------------------------------------
# Fit
# ------------------------------------------------------------------
def
fit
(
self
,
data
:
pd
.
DataFrame
,
outcome
:
Any
=
NOT_SUPPLIED
,
running
:
Any
=
NOT_SUPPLIED
,
covariates
:
Optional
[
List
[
str
]]
=
None
,
outcome_col
:
Any
=
NOT_SUPPLIED
,
running_col
:
Any
=
NOT_SUPPLIED
,
)
->
RDPlotResult
:
"""Build the RD plot quantities.
Parameters
----------
data : pd.DataFrame
Cross-sectional data.
outcome, running : str
Column names of the outcome and the running variable.
covariates : list of str, optional
Covariate-adjusted plot per rdplot's ``covs=`` (global fit
partials the covariates out with a pooled-across-sides gamma;
bin means use the fitted values of per-side regressions of the
outcome on covariates and bin indicators, R's
``lm(y ~ z + factor(bin))`` with ``covs_eval="mean"``). Note
R's caveat: covariate adjustment is meant for plotting alongside
covariate-adjusted rdrobust ESTIMATES; the adjusted global fit
may not be visually compatible with unadjusted binned means.
``covariate_coefficients`` is keyed in R's processing order:
name-length-sorted under ``covs_drop=True`` (the sort decides
which of a collinear set survives), the user-given order under
``covs_drop=False`` (R sorts only inside its drop pipeline;
same contract as ``RegressionDiscontinuity``). Per-name values
are identical either way on full-rank covariates.
outcome_col, running_col : str, optional
Deprecated aliases for ``outcome`` / ``running`` (rows
M-088/M-089); each warns with ``FutureWarning`` and will be
removed in 4.0.
Returns
-------
RDPlotResult
"""
qualname
=
"RDPlot.fit"
outcome
=
resolve_renamed_kwarg
(
qualname
,
"outcome_col"
,
outcome_col
,
"outcome"
,
outcome
,
default
=
NOT_SUPPLIED
,
)
require_arg
(
qualname
,
"outcome"
,
outcome
)
running
=
resolve_renamed_kwarg
(
qualname
,
"running_col"
,
running_col
,
"running"
,
running
,
default
=
NOT_SUPPLIED
,
)
require_arg
(
qualname
,
"running"
,
running
)
# Body-local names; the public parameters are outcome/running
# (M-088/M-089).
outcome_col
=
outcome
running_col
=
running
if
covariates
is
not
None
:
if
isinstance
(
covariates
,
str
):
# A bare string would iterate characters; fail closed.
raise
ValueError
(
f"covariates must be a list of column names; got
{
covariates
!r
}
."
)
# Materialize before validating (a generator would be consumed
# by the checks below and silently collapse to empty).
covariates
=
list
(
covariates
)
if
not
covariates
:
raise
ValueError
(
"covariates must be a non-empty list of column names or None."
)
if
not
all
(
isinstance
(
name
,
str
)
for
name
in
covariates
):
raise
ValueError
(
f"covariates must be a list of column names; got
{
covariates
!r
}
."
)
# Duplicate names corrupt the name-keyed coefficient dict and
# the index-based column lookups (same contract as the
# estimator's validate_covariate_names).
validate_covariate_names
(
covariates
,
[
outcome_col
,
running_col
],
estimator
=
"RDPlot"
,
)
for
col
in
[
outcome_col
,
running_col
]
+
list
(
covariates
or
[]):
if
col
not
in
data
.
columns
:
raise
ValueError
(
f"Column '
{
col
}
' not found in data."
)
c
=
float
(
self
.
cutoff
)
y_all
=
np
.
asarray
(
pd
.
to_numeric
(
data
[
outcome_col
],
errors
=
"coerce"
),
dtype
=
np
.
float64
)
x_all
=
np
.
asarray
(
pd
.
to_numeric
(
data
[
running_col
],
errors
=
"coerce"
),
dtype
=
np
.
float64
)
ok
=
np
.
isfinite
(
x_all
)
&
np
.
isfinite
(
y_all
)
z_all
:
Optional
[
np
.
ndarray
]
=
None
if
covariates
is
not
None
:
z_all
=
np
.
column_stack
(
[
np
.
asarray
(
pd
.
to_numeric
(
data
[
col
],
errors
=
"coerce"
),
dtype
=
np
.
float64
)
for
col
in
covariates
]
)
ok
&=
np
.
all
(
np
.
isfinite
(
z_all
),
axis
=
1
)
n_dropped
=
int
(
x_all
.
shape
[
0
]
-
np
.
sum
(
ok
))
if
n_dropped
>
0
:
# Deviation from R (which drops silently via complete.cases);
# same warning contract as RegressionDiscontinuity.fit().
dropped_cols
=
f"
{
outcome_col
!r
}
/
{
running_col
!r
}
"
if
covariates
is
not
None
:
dropped_cols
+=
"/covariates"
warnings
.
warn
(
f"Dropping
{
n_dropped
}
row(s) with missing or non-numeric "
f"values in
{
dropped_cols
}
."
,
UserWarning
,
stacklevel
=
2
,
)
x
,
y
=
x_all
[
ok
],
y_all
[
ok
]
z
=
z_all
[
ok
]
if
z_all
is
not
None
else
None
n
=
x
.
size
if
n
==
0
:
raise
ValueError
(
"No complete observations after dropping missing values."
)
# --- Covariate redundancy pipeline (rdplot.R:144-159; identical to
# the estimator's: stable name-length sort decides which of a
# collinear set survives, QR check on x-sorted rows) ---
model_covariates
:
Optional
[
List
[
str
]]
=
None
covariates_dropped
:
Optional
[
List
[
str
]]
=
None
if
covariates
is
not
None
and
z
is
not
None
and
self
.
covs_drop
:
model_covariates
=
sorted
(
list
(
covariates
),
key
=
len
)
z
=
np
.
column_stack
([
z
[:,
covariates
.
index
(
name
)]
for
name
in
model_covariates
])
keep_idx
,
rank
=
covs_drop_fun
(
z
[
np
.
argsort
(
x
,
kind
=
"stable"
)])
if
rank
==
0
:
raise
ValueError
(
"All covariates are numerically zero (rank-0 covariate "
"matrix); remove the covariates instead."
)
covariates_dropped
=
[]
if
rank
<
len
(
model_covariates
):
covariates_dropped
=
[
name
for
i
,
name
in
enumerate
(
model_covariates
)
if
i
not
in
set
(
keep_idx
.
tolist
())
]
warnings
.
warn
(
"Multicollinearity detected in covariates: dropped "
f"redundant column(s)
{
covariates_dropped
}
"
"(covs_drop=True; set covs_drop=False for a strict "
"error instead)."
,
UserWarning
,
stacklevel
=
2
,
)
model_covariates
=
[
name
for
i
,
name
in
enumerate
(
model_covariates
)
if
i
in
set
(
keep_idx
.
tolist
())
]
z
=
z
[:,
keep_idx
]
elif
covariates
is
not
None
:
model_covariates
=
list
(
covariates
)
x_min
,
x_max
=
float
(
np
.
min
(
x
)),
float
(
np
.
max
(
x
))
if
self
.
support
is
not
None
:
# support only WIDENS (rdplot.R:62-67)
if
float
(
self
.
support
[
0
])
<
x_min
:
x_min
=
float
(
self
.
support
[
0
])
if
float
(
self
.
support
[
1
])
>
x_max
:
x_max
=
float
(
self
.
support
[
1
])
if
c
<=
x_min
or
c
>=
x_max
:
raise
ValueError
(
f"cutoff must lie strictly inside the range of '
{
running_col
}
' "
f"([
{
x_min
}
,
{
x_max
}
]); got
{
c
}
."
)
if
n
<
20
:
# R stops outright (rdplot.R:190-193) - no small-sample fallback.
raise
ValueError
(
f"Not enough observations to perform bin calculations (n=
{
n
}
< 20)."
)
ind_l
,
ind_r
=
x
<
c
,
x
>=
c
x_l
,
y_l
,
x_r
,
y_r
=
x
[
ind_l
],
y
[
ind_l
],
x
[
ind_r
],
y
[
ind_r
]
n_l
,
n_r
=
int
(
x_l
.
size
),
int
(
x_r
.
size
)
# Defensive guard beyond R: with n >= 20 one side can still be
# near-empty, where R degenerates opaquely (NA variance poisoning an
# `if`). Fail with the side named instead.
if
n_l
<
2
or
n_r
<
2
:
side
=
"below"
if
n_l
<
2
else
"above"
raise
ValueError
(
f"Need at least 2 observations on each side of the cutoff; "
f"found
{
min
(
n_l
,
n_r
)
}
{
side
}
cutoff=
{
c
}
."
)
# A side whose running variable has a SINGLE support point cannot be
# partitioned: every spacing is zero, the variance components
# collapse to 0, J becomes Inf, and the jump grid divides by zero -
# R crashes opaquely inside seq() there. Defensive clear error.
if
np
.
unique
(
x_l
).
size
<
2
or
np
.
unique
(
x_r
).
size
<
2
:
side
=
"below"
if
np
.
unique
(
x_l
).
size
<
2
else
"above"
raise
ValueError
(
f"The running variable has a single distinct value
{
side
}
"
f"cutoff=
{
c
}
; at least 2 distinct values per side are "
"required to construct bins."
)
range_l
,
range_r
=
c
-
x_min
,
x_max
-
c
scale_l
,
scale_r
=
_resolve_pair
(
self
.
scale
,
1.0
)
h_l
,
h_r
=
_resolve_pair
(
self
.
h
,
0.0
)
if
self
.
h
is
None
:
h_l
,
h_r
=
range_l
,
range_r
ci_requested
=
self
.
ci
is
not
None
ci_level
=
float
(
self
.
ci
)
if
self
.
ci
is
not
None
else
95.0
binselect
=
self
.
binselect
# --- Mass points (rdplot.R:116-137) ---
if
self
.
masspoints
in
(
"check"
,
"adjust"
):
m_l
=
np
.
unique
(
x_l
).
size
m_r
=
np
.
unique
(
x_r
).
size
if
(
1
-
m_l
/
n_l
)
>=
0.2
or
(
1
-
m_r
/
n_r
)
>=
0.2
:
hint
=
(
" Try masspoints='adjust'."
if
self
.
masspoints
==
"check"
else
f" binselect switched to '
{
_MASSPOINTS_REMAP
.
get
(
binselect
,
binselect
)
}
'"
" (polynomial-regression variance estimators; spacings"
" degenerate under ties)."
)
warnings
.
warn
(
"Mass points detected in the running variable."
+
hint
,
UserWarning
,
stacklevel
=
2
,
)
if
self
.
masspoints
==
"adjust"
:
binselect
=
_MASSPOINTS_REMAP
.
get
(
binselect
,
binselect
)
# Defensive warning beyond R (which runs spacings selectors on any
# outcome): the spacings/concomitant selectors assume a CONTINUOUSLY
# distributed outcome (CCT 2015 Theorems 3-4; Remarks 1-2 route
# noncontinuous outcomes to the series estimators). Heavy outcome
# ties - the masspoints criterion applied to y - invalidate them;
# warn and recommend the *pr sibling. No auto-remap: R does not
# remap, and the numbers must stay golden-comparable.
if
_BINSELECT_INFO
[
binselect
][
1
]
==
"hat"
:
ties_l
=
1
-
np
.
unique
(
y_l
).
size
/
n_l
ties_r
=
1
-
np
.
unique
(
y_r
).
size
/
n_r
if
ties_l
>=
0.2
or
ties_r
>=
0.2
:
warnings
.
warn
(
"Heavy ties detected in the outcome variable (possibly "
"discrete/binary); the spacings-based bin selectors "
"assume a continuously distributed outcome (CCT 2015 "
"Theorems 3-4). Consider binselect="
f"'
{
_MASSPOINTS_REMAP
.
get
(
binselect
,
binselect
)
}
' "
"(polynomial-regression variance estimators, valid for "
"noncontinuous outcomes)."
,
UserWarning
,
stacklevel
=
2
,
)
# ------------------------------------------------------------------
# Global polynomial curve of order p (rdplot.R:206-270)
# ------------------------------------------------------------------
p
=
int
(
self
.
p
)
W_h_l
=
rdrobust_kweight
(
x_l
,
c
,
h_l
,
self
.
kernel
)
W_h_r
=
rdrobust_kweight
(
x_r
,
c
,
h_r
,
self
.
kernel
)
n_h_l
=
int
(
np
.
sum
(
W_h_l
>
0
))
n_h_r
=
int
(
np
.
sum
(
W_h_r
>
0
))
if
n_h_l
==
0
or
n_h_r
==
0
:
# Defensive guard beyond R: a manual h smaller than the nearest
# observation's distance zeroes every kernel weight, and R
# silently fits a zero-weight design (ginv of a zero Gram),
# returning an all-zero "curve" that used no data.
side
=
"below"
if
n_h_l
==
0
else
"above"
raise
ValueError
(
f"h=
{
self
.
h
!r
}
leaves zero effective observations
{
side
}
the "
f"cutoff for the global polynomial fit; increase h (default: "
"each side's full range)."
)
R_p_l
=
_pow_outer
(
x_l
-
c
,
p
)
R_p_r
=
_pow_outer
(
x_r
-
c
,
p
)
invG_p_l
=
qrXXinv
(
np
.
sqrt
(
W_h_l
)[:,
None
]
*
R_p_l
)
invG_p_r
=
qrXXinv
(
np
.
sqrt
(
W_h_r
)[:,
None
]
*
R_p_r
)
gamma_p
:
Optional
[
np
.
ndarray
]
=
None
covariate_coefficients
:
Optional
[
Dict
[
str
,
float
]]
=
None
if
z
is
None
:
gamma_p1_l
=
invG_p_l
@ (
R_p_l
*
W_h_l
[:,
None
]).
T
@
y_l
gamma_p1_r
=
invG_p_r
@ (
R_p_r
*
W_h_r
[:,
None
]).
T
@
y_r
else
:
z_l
,
z_r
=
z
[
ind_l
],
z
[
ind_r
]
D_l
=
np
.
column_stack
([
y_l
,
z_l
])
D_r
=
np
.
column_stack
([
y_r
,
z_r
])
U_p_l
=
(
R_p_l
*
W_h_l
[:,
None
]).
T
@
D_l
U_p_r
=
(
R_p_r
*
W_h_r
[:,
None
]).
T
@
D_r
beta_p_l
=
invG_p_l
@
U_p_l
beta_p_r
=
invG_p_r
@
U_p_r
ZWD_p_l
=
(
z_l
*
W_h_l
[:,
None
]).
T
@
D_l
ZWD_p_r
=
(
z_r
*
W_h_r
[:,
None
]).
T
@
D_r
colsZ
=
slice
(
1
,
D_l
.
shape
[
1
])
UiGU_l
=
U_p_l
[:,
colsZ
].
T
@ (
invG_p_l
@
U_p_l
)
UiGU_r
=
U_p_r
[:,
colsZ
].
T
@ (
invG_p_r
@
U_p_r
)
ZWZ_p
=
(
ZWD_p_l
[:,
colsZ
]
-
UiGU_l
[:,
colsZ
])
+
(
ZWD_p_r
[:,
colsZ
]
-
UiGU_r
[:,
colsZ
])
ZWY_p
=
(
ZWD_p_l
[:, :
1
]
-
UiGU_l
[:, :
1
])
+
(
ZWD_p_r
[:, :
1
]
-
UiGU_r
[:, :
1
])
diag_pre
=
np
.
diag
(
ZWD_p_l
[:,
colsZ
])
+
np
.
diag
(
ZWD_p_r
[:,
colsZ
])
gamma_p
,
covs_excluded
,
covs_set_degenerate
=
_covs_gamma
(
ZWZ_p
,
ZWY_p
,
diag_pre
,
bool
(
self
.
covs_drop
)
)
assert
model_covariates
is
not
None
if
covs_excluded
.
any
()
or
covs_set_degenerate
:
# Same guarded Deviation-from-R as the estimator: R's
# ginv(tol=1e-20) inverts noise singular values on exactly-
# degenerate partialled systems, making gamma platform-noise.
parts
=
[]
if
covs_excluded
.
any
():
excluded_names
=
[
model_covariates
[
i
]
for
i
in
np
.
flatnonzero
(
covs_excluded
)]
parts
.
append
(
f"covariate(s)
{
excluded_names
}
are numerically "
"collinear with the polynomial design (e.g. constant) "
"and were excluded from the adjustment"
)
if
covs_set_degenerate
:
parts
.
append
(
"the covariate set is numerically rank-deficient "
"after partialling; a stabilized pseudo-inverse cut "
"was used - consider dropping a reference category"
)
warnings
.
warn
(
"Degenerate covariate adjustment: "
+
"; "
.
join
(
parts
)
+
"."
,
UserWarning
,
stacklevel
=
2
,
)
s_Y
=
np
.
concatenate
([[
1.0
],
-
gamma_p
[:,
0
]])
gamma_p1_l
=
beta_p_l
@
s_Y
gamma_p1_r
=
beta_p_r
@
s_Y
covariate_coefficients
=
{
name
:
float
(
gamma_p
[
i
,
0
])
for
i
,
name
in
enumerate
(
model_covariates
)
}
nplot
=
500
x_plot_l
=
np
.
linspace
(
c
-
h_l
,
c
,
nplot
)
x_plot_r
=
np
.
linspace
(
c
,
c
+
h_r
,
nplot
)
y_hat_l
=
_pow_outer
(
x_plot_l
-
c
,
p
) @
gamma_p1_l
y_hat_r
=
_pow_outer
(
x_plot_r
-
c
,
p
) @
gamma_p1_r
if
z
is
not
None
and
gamma_p
is
not
None
:
# covs_eval="mean" (rdplot.R:266-270): a single scalar shift.
gammaZ
=
float
(
np
.
mean
(
z
,
axis
=
0
) @
gamma_p
[:,
0
])
y_hat_l
=
y_hat_l
+
gammaZ
y_hat_r
=
y_hat_r
+
gammaZ
# ------------------------------------------------------------------
# Bin-count selectors (order-k global fits; rdplot.R:276-388)
# ------------------------------------------------------------------
# k=4 with a 4 -> 3 -> 2 fallback ladder that fires ONLY when
# qrXXinv itself raises: R's qrXXinv absorbs finite singular Grams
# via an internal ginv fallback at UNCHANGED k, so the try-error
# ladder triggers on non-finite Grams (raw x**8 overflow) or SVD
# failure. scipy raises ValueError on non-finite input, numpy
# LinAlgError on SVD failure - catch both.
def
_selector_grams
()
->
Tuple
[
int
,
np
.
ndarray
,
np
.
ndarray
,
np
.
ndarray
,
np
.
ndarray
]:
for
k_try
in
(
4
,
3
,
2
):
rk_l_try
=
_pow_outer
(
x_l
,
k_try
)
rk_r_try
=
_pow_outer
(
x_r
,
k_try
)
try
:
return
(
k_try
,
rk_l_try
,
rk_r_try
,
qrXXinv
(
rk_l_try
),
qrXXinv
(
rk_r_try
),
)
except
(
ValueError
,
np
.
linalg
.
LinAlgError
):
if
k_try
==
2
:
raise
raise
AssertionError
(
"unreachable"
)
# pragma: no cover
k
,
rk_l
,
rk_r
,
invG_k_l
,
invG_k_r
=
_selector_grams
()
gamma_k1_l
=
invG_k_l
@
rk_l
.
T
@
y_l
gamma_k2_l
=
invG_k_l
@
rk_l
.
T
@
y_l
**
2
gamma_k1_r
=
invG_k_r
@
rk_r
.
T
@
y_r
gamma_k2_r
=
invG_k_r
@
rk_r
.
T
@
y_r
**
2
ord_l
=
np
.
argsort
(
x_l
,
kind
=
"stable"
)
ord_r
=
np
.
argsort
(
x_r
,
kind
=
"stable"
)
x_i_l
,
y_i_l
=
x_l
[
ord_l
],
y_l
[
ord_l
]
x_i_r
,
y_i_r
=
x_r
[
ord_r
],
y_r
[
ord_r
]
dxi_l
,
dyi_l
=
np
.
diff
(
x_i_l
),
np
.
diff
(
y_i_l
)
dxi_r
,
dyi_r
=
np
.
diff
(
x_i_r
),
np
.
diff
(
y_i_r
)
x_bar_i_l
=
(
x_i_l
[
1
:]
+
x_i_l
[:
-
1
])
/
2
x_bar_i_r
=
(
x_i_r
[
1
:]
+
x_i_r
[:
-
1
])
/
2
rk_i_l
=
_pow_outer
(
x_bar_i_l
,
k
)
rk_i_r
=
_pow_outer
(
x_bar_i_r
,
k
)
j_arange
=
np
.
arange
(
1
,
k
+
1
)
drk_l
=
_pow_outer
(
x_l
,
k
-
1
)
*
j_arange
drk_r
=
_pow_outer
(
x_r
,
k
-
1
)
*
j_arange
drk_i_l
=
_pow_outer
(
x_bar_i_l
,
k
-
1
)
*
j_arange
drk_i_r
=
_pow_outer
(
x_bar_i_r
,
k
-
1
)
*
j_arange
# R's two-pass var() is EXACTLY zero on a constant vector; numpy's
# single-pass mean leaves ~1e-32 roundoff for constants like 0.7,
# which would skip the var==0 rescue below and crash the bin grid on
# a zero jump (J_MV = Inf). Route through the port's exact-constancy
# helper (_var0, the #686-ported R semantics) so constant sides give
# var_y = 0.0 exactly, reproducing R's rescue and its 0/0 -> NaN
# selector echoes.
var_y_l
=
0.0
if
_var0
(
y_l
)
else
float
(
np
.
var
(
y_l
,
ddof
=
1
))
var_y_r
=
0.0
if
_var0
(
y_r
)
else
float
(
np
.
var
(
y_r
,
ddof
=
1
))
mu0_i_l
=
rk_i_l
@
gamma_k1_l
mu0_i_r
=
rk_i_r
@
gamma_k1_r
mu2_i_l
=
rk_i_l
@
gamma_k2_l
mu2_i_r
=
rk_i_r
@
gamma_k2_r
mu0_l
=
rk_l
@
gamma_k1_l
mu0_r
=
rk_r
@
gamma_k1_r
mu2_l
=
rk_l
@
gamma_k2_l
mu2_r
=
rk_r
@
gamma_k2_r
mu1_l
=
drk_l
@
gamma_k1_l
[
1
:]
mu1_r
=
drk_r
@
gamma_k1_r
[
1
:]
mu1_i_l
=
drk_i_l
@
gamma_k1_l
[
1
:]
mu1_i_r
=
drk_i_r
@
gamma_k1_r
[
1
:]
# Negative variance-function values are floored to the side's sample
# variance (rdplot.R:356-371; an R-source fact absent from the paper).
s2bar_l
=
mu2_i_l
-
mu0_i_l
**
2
s2bar_r
=
mu2_i_r
-
mu0_i_r
**
2
s2bar_l
[
s2bar_l
<
0
]
=
var_y_l
s2bar_r
[
s2bar_r
<
0
]
=
var_y_r
s2_l
=
mu2_l
-
mu0_l
**
2
s2_r
=
mu2_r
-
mu0_r
**
2
s2_l
[
s2_l
<
0
]
=
var_y_l
s2_r
[
s2_r
<
0
]
=
var_y_r
B_es
=
(
(
range_l
**
2
/
(
12
*
n
))
*
float
(
np
.
sum
(
mu1_l
**
2
)),
(
range_r
**
2
/
(
12
*
n
))
*
float
(
np
.
sum
(
mu1_r
**
2
)),
)
V_es_hat
=
(
(
0.5
/
range_l
)
*
float
(
np
.
sum
(
dxi_l
*
dyi_l
**
2
)),
(
0.5
/
range_r
)
*
float
(
np
.
sum
(
dxi_r
*
dyi_r
**
2
)),
)
V_es_chk
=
(
(
1
/
range_l
)
*
float
(
np
.
sum
(
dxi_l
*
s2bar_l
)),
(
1
/
range_r
)
*
float
(
np
.
sum
(
dxi_r
*
s2bar_r
)),
)
B_qs
=
(
(
n_l
**
2
/
(
24
*
n
))
*
float
(
np
.
sum
(
dxi_l
**
2
*
mu1_i_l
**
2
)),
(
n_r
**
2
/
(
24
*
n
))
*
float
(
np
.
sum
(
dxi_r
**
2
*
mu1_i_r
**
2
)),
)
V_qs_hat
=
(
(
1
/
(
2
*
n_l
))
*
float
(
np
.
sum
(
dyi_l
**
2
)),
(
1
/
(
2
*
n_r
))
*
float
(
np
.
sum
(
dyi_r
**
2
)),
)
V_qs_chk
=
(
(
1
/
n_l
)
*
float
(
np
.
sum
(
s2_l
)),
(
1
/
n_r
)
*
float
(
np
.
sum
(
s2_r
)),
)
# numpy scalar arithmetic so a zero-variance side propagates Inf/NaN
# through the selectors exactly like R (0/0 -> NaN, x/0 -> Inf, no
# exception); the var==0 rescue below then pins J to 1 as R does.
def
_J_imse
(
B
:
_PairF
,
V
:
_PairF
)
->
_PairF
:
# J.fun (functions.R:588)
with
np
.
errstate
(
divide
=
"ignore"
,
invalid
=
"ignore"
):
return
(
float
(
np
.
ceil
((((
2
*
np
.
float64
(
B
[
0
]))
/
V
[
0
])
*
n
)
**
(
1
/
3
))),
float
(
np
.
ceil
((((
2
*
np
.
float64
(
B
[
1
]))
/
V
[
1
])
*
n
)
**
(
1
/
3
))),
)
def
_J_mv
(
V
:
_PairF
)
->
_PairF
:
with
np
.
errstate
(
divide
=
"ignore"
,
invalid
=
"ignore"
):
return
(
float
(
np
.
ceil
((
np
.
float64
(
var_y_l
)
/
V
[
0
])
*
n
/
math
.
log
(
n
)
**
2
)),
float
(
np
.
ceil
((
np
.
float64
(
var_y_r
)
/
V
[
1
])
*
n
/
math
.
log
(
n
)
**
2
)),
)
meth
,
family
,
target
,
binselect_type
=
_BINSELECT_INFO
[
binselect
]
V_pair
=
{
(
"es"
,
"hat"
):
V_es_hat
,
(
"es"
,
"chk"
):
V_es_chk
,
(
"qs"
,
"hat"
):
V_qs_hat
,
(
"qs"
,
"chk"
):
V_qs_chk
,
}[(
meth
,
family
)]
B_pair
=
B_es
if
meth
==
"es"
else
B_qs
J_IMSE
=
_J_imse
(
B_pair
,
V_pair
)
J_MV
=
_J_mv
(
V_pair
)
J_star_orig
=
J_IMSE
if
target
==
"imse"
else
J_MV
# --- J resolution (rdplot.R:449-469) ---
# Deviation from R: for a fractional scale*J product, R 4.0.0
# propagates the unrounded value into 1:J / vector-indexing
# arithmetic and CRASHES assembling vars_bins (verified live:
# "arguments imply differing number of rows"); a length-2 scale dies
# even earlier on R >= 4.2 (vectorized-if error at rdplot.R:178).
# CCT 2015 Equation 2 places the ceiling over the RESCALED product
# (J = ceil(omega * (2B/V n)^(1/3))), so the paper-faithful integer
# partition is used here instead of replicating an accident.
# np.ceil keeps a NaN/Inf selector value (zero-variance side, rescued
# just below) flowing instead of raising like math.ceil would. The
# max(1, .) clamp keeps an ultra-small positive scale (product below
# the 1e-12 integer-product epsilon) from ceiling to zero bins - the
# Eq 2 partition is at least one bin (NaN passes through max()
# untouched for the variance rescue).
with
np
.
errstate
(
invalid
=
"ignore"
):
J_star_l
:
float
=
float
(
np
.
maximum
(
1.0
,
np
.
ceil
(
scale_l
*
J_star_orig
[
0
]
-
1e-12
)))
J_star_r
:
float
=
float
(
np
.
maximum
(
1.0
,
np
.
ceil
(
scale_r
*
J_star_orig
[
1
]
-
1e-12
)))
if
self
.
nbins
is
not
None
:
nb_l
,
nb_r
=
_resolve_pair
(
self
.
nbins
,
0
)
J_star_l
,
J_star_r
=
float
(
nb_l
),
float
(
nb_r
)
# R keeps this label even when binselect selected quantile
# spacing - the BINS below still follow `meth` (label-only quirk).
binselect_type
=
"manually evenly spaced"
if
var_y_l
==
0
:
J_star_l
=
1.0
warnings
.
warn
(
"Not enough variability in the outcome variable below the threshold"
,
UserWarning
,
stacklevel
=
2
,
)
if
var_y_r
==
0
:
J_star_r
=
1.0
warnings
.
warn
(
"Not enough variability in the outcome variable above the threshold"
,
UserWarning
,
stacklevel
=
2
,
)
rscale
=
(
J_star_l
/
J_IMSE
[
0
],
J_star_r
/
J_IMSE
[
1
])
# --- Bin construction (rdplot.R:471-508) ---
jump_l
=
range_l
/
J_star_l
jump_r
=
range_r
/
J_star_r
if
meth
==
"es"
:
jumps_l
=
_r_seq_by
(
x_min
,
c
,
jump_l
)
jumps_r
=
_r_seq_by
(
c
,
x_max
,
jump_r
)
else
:
probs_l
=
_r_seq_by
(
0.0
,
1.0
,
1
/
J_star_l
)
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