A coefficient plot displays regression coefficients as points positioned along a horizontal axis, with horizontal error bars showing confidence intervals. This visualization makes it easy to assess effect sizes and statistical significance - coefficients whose confidence intervals cross zero are not statistically significant. Typically used to summarize results from linear, logistic, or other regression models in a clear, publication-ready format.
""" anyplot.ai
coefficient-confidence: Coefficient Plot with Confidence Intervals
Library: bokeh 3.9.0 | Python 3.13.13
Quality: 94/100 | Updated: 2026-05-18
"""
import os
import time
from pathlib import Path
import numpy as np
from bokeh.io import output_file, save
from bokeh.models import ColumnDataSource, HoverTool, Legend, LegendItem, Span
from bokeh.plotting import figure
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Data - Regression coefficients for housing price prediction model
np.random.seed(42)
variables = [
"Square Footage",
"Number of Bedrooms",
"Number of Bathrooms",
"Age of House",
"Distance to City Center",
"Lot Size",
"Garage Size",
"School Rating",
"Crime Rate Index",
"Property Tax Rate",
]
# Generate realistic regression coefficients (some significant, some not)
coefficients = np.array([0.45, 0.12, 0.18, -0.08, -0.22, 0.15, 0.09, 0.28, -0.35, -0.05])
std_errors = np.array([0.05, 0.08, 0.06, 0.03, 0.07, 0.04, 0.06, 0.05, 0.09, 0.07])
# Calculate 95% confidence intervals
ci_lower = coefficients - 1.96 * std_errors
ci_upper = coefficients + 1.96 * std_errors
# Determine significance (CI does not cross zero)
significant = ~((ci_lower < 0) & (ci_upper > 0))
# Sort by coefficient magnitude for better visualization
sort_idx = np.argsort(np.abs(coefficients))
variables = [variables[i] for i in sort_idx]
coefficients = coefficients[sort_idx]
ci_lower = ci_lower[sort_idx]
ci_upper = ci_upper[sort_idx]
significant = significant[sort_idx]
# Okabe-Ito palette - using brand green for significant, neutral for non-significant
SIG_COLOR = "#009E73"
NONSIG_COLOR = INK_SOFT
colors = [SIG_COLOR if sig else NONSIG_COLOR for sig in significant]
# Create figure with categorical y-axis
p = figure(
width=4800,
height=2700,
y_range=variables,
title="coefficient-confidence · python · bokeh · anyplot.ai",
x_axis_label="Coefficient Estimate (Standardized)",
y_axis_label="Predictor Variable",
)
# Add vertical reference line at zero
zero_line = Span(location=0, dimension="height", line_color=INK_SOFT, line_width=3, line_dash="dashed")
p.add_layout(zero_line)
# Draw confidence interval segments (error bars) with distinct colors
for i, var in enumerate(variables):
color = colors[i]
# Main confidence interval line
p.line(x=[ci_lower[i], ci_upper[i]], y=[var, var], line_width=6, line_color=color, line_alpha=0.85)
# Plot coefficient points - separate renderers for legend with distinct colors
sig_indices = [i for i, s in enumerate(significant) if s]
nonsig_indices = [i for i, s in enumerate(significant) if not s]
# Create separate data sources for legend
sig_source = ColumnDataSource(
data={
"variables": [variables[i] for i in sig_indices],
"coefficients": [coefficients[i] for i in sig_indices],
"ci_lower_fmt": [f"{ci_lower[i]:.3f}" for i in sig_indices],
"ci_upper_fmt": [f"{ci_upper[i]:.3f}" for i in sig_indices],
"coef_fmt": [f"{coefficients[i]:.3f}" for i in sig_indices],
"significance": ["Significant (p < 0.05)"] * len(sig_indices),
}
)
nonsig_source = ColumnDataSource(
data={
"variables": [variables[i] for i in nonsig_indices],
"coefficients": [coefficients[i] for i in nonsig_indices],
"ci_lower_fmt": [f"{ci_lower[i]:.3f}" for i in nonsig_indices],
"ci_upper_fmt": [f"{ci_upper[i]:.3f}" for i in nonsig_indices],
"coef_fmt": [f"{coefficients[i]:.3f}" for i in nonsig_indices],
"significance": ["Not Significant"] * len(nonsig_indices),
}
)
# Render significant points with brand green
sig_renderer = p.scatter(
x="coefficients", y="variables", source=sig_source, size=30, color=SIG_COLOR, line_color="white", line_width=3
)
# Render non-significant points with muted color
nonsig_renderer = p.scatter(
x="coefficients", y="variables", source=nonsig_source, size=30, color=NONSIG_COLOR, line_color="white", line_width=3
)
# Add HoverTool for interactive tooltips (Bokeh distinctive feature)
hover = HoverTool(
tooltips=[
("Variable", "@variables"),
("Coefficient", "@coef_fmt"),
("95% CI", "[@ci_lower_fmt, @ci_upper_fmt]"),
("Status", "@significance"),
],
renderers=[sig_renderer, nonsig_renderer],
)
p.add_tools(hover)
# Create legend inside the plot area (top right corner within plot bounds)
legend = Legend(
items=[
LegendItem(label="Significant (p < 0.05)", renderers=[sig_renderer]),
LegendItem(label="Not Significant", renderers=[nonsig_renderer]),
],
location="top_right",
label_text_font_size="24pt",
label_text_color=INK_SOFT,
glyph_width=40,
glyph_height=40,
border_line_color=INK_SOFT,
border_line_width=2,
background_fill_color=ELEVATED_BG,
background_fill_alpha=0.95,
padding=20,
margin=30,
)
p.add_layout(legend)
# Style text sizes for large canvas (scaled for 4800x2700)
p.title.text_font_size = "28pt"
p.title.text_color = INK
p.xaxis.axis_label_text_font_size = "22pt"
p.yaxis.axis_label_text_font_size = "22pt"
p.xaxis.axis_label_text_color = INK
p.yaxis.axis_label_text_color = INK
p.xaxis.major_label_text_font_size = "18pt"
p.yaxis.major_label_text_font_size = "18pt"
p.xaxis.major_label_text_color = INK_SOFT
p.yaxis.major_label_text_color = INK_SOFT
# Grid styling
p.xgrid.grid_line_alpha = 0.10
p.xgrid.grid_line_dash = "dashed"
p.xgrid.grid_line_color = INK
p.ygrid.grid_line_alpha = 0.10
p.ygrid.grid_line_dash = "dashed"
p.ygrid.grid_line_color = INK
# Background styling (theme-adaptive)
p.background_fill_color = PAGE_BG
p.border_fill_color = PAGE_BG
p.outline_line_color = INK_SOFT
# Axis styling
p.xaxis.axis_line_color = INK_SOFT
p.yaxis.axis_line_color = INK_SOFT
p.xaxis.axis_line_width = 2
p.yaxis.axis_line_width = 2
p.xaxis.major_tick_line_color = INK_SOFT
p.yaxis.major_tick_line_color = INK_SOFT
p.xaxis.major_tick_line_width = 2
p.yaxis.major_tick_line_width = 2
# Save plot (HTML and PNG via Selenium)
output_file(f"plot-{THEME}.html")
save(p)
# Screenshot with headless Chrome via Selenium
W, H = 4800, 2700
opts = Options()
for arg in (
"--headless=new",
"--no-sandbox",
"--disable-dev-shm-usage",
"--disable-gpu",
f"--window-size={W},{H}",
"--hide-scrollbars",
):
opts.add_argument(arg)
driver = webdriver.Chrome(options=opts)
driver.set_window_size(W, H)
driver.get(f"file://{Path(f'plot-{THEME}.html').resolve()}")
time.sleep(3)
driver.save_screenshot(f"plot-{THEME}.png")
driver.quit()
Part of Coefficient Plot with Confidence Intervals on anyplot.ai.