MCPower 0.4.0

Monte Carlo Power Analysis for Statistical Models

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MCPower

Simple Monte Carlo power analysis for complex models. Find the sample size you need or check if your study has enough power - even with complex models that traditional power analysis can't handle.

Why MCPower?

Traditional power analysis breaks down with interactions, correlated predictors, categorical variables, or non-normal data. MCPower uses simulation instead of formulas - it generates thousands of datasets exactly like yours, then sees how often your analysis finds real effects.

✅ Works with complexity: Interactions, correlations, factors, any distribution
✅ R-style formulas: outcome = treatment + covariate + treatment*covariate
✅ Categorical variables: Multi-level factors automatically handled
✅ Two simple commands: Find sample size or check power
✅ Scenario analysis: Test robustness under realistic conditions
✅ Minimal math required: Just specify your model and effects

Get Started in 2 Minutes

Install

pip install mcpower

Update to the latest version.

pip install --upgrade mcpower

Your First Power Analysis

# 0. Import installed package
import mcpower

# 1. Define your model (just like R)
model = mcpower.MCPower("satisfaction = treatment + motivation")

# 2. Set effect sizes (how big you expect effects to be)
model.set_effects("treatment=0.5, motivation=0.3")

# 3. Change the treatment to "binary" (people receive treatment or not).
model.set_variable_type("treatment=binary")

# 4. Find the sample size you need
model.find_sample_size(target_test="treatment", from_size=50, to_size=200, summary="long")

Output: "You need N=75 for 80% power to detect the treatment effect"

That's it! 🎉

🎯 Scenario Analysis: Test Your Assumptions

Real studies rarely match perfect assumptions. MCPower's scenario analysis tests how robust your power calculations are under realistic conditions.

# Test robustness with scenario analysis
model.find_sample_size(
    target_test="treatment", 
    from_size=50, to_size=300,
    scenarios=True  # 🔥 The magic happens here
)

Output:

SCENARIO SUMMARY
================================================================================

Uncorrected Sample Sizes:
Test                                     Optimistic   Realistic    Doomer      
-------------------------------------------------------------------------------
treatment                                75           85           100         
================================================================================

What each scenario means:

• Optimistic: Your ideal conditions (original settings)
• Realistic: Moderate real-world complications (small effect variations, mild assumption violations)
• Doomer: Conservative estimate (larger effect variations, stronger assumption violations)

💡 Pro tip: Use the Realistic scenario for planning. If Doomer is acceptable, you're really safe!

Understanding Effect Sizes

Effect sizes tell you how much the outcome changes when predictors change.

• Effect size = 0.5 means the outcome increases by 0.5 standard deviations when:
  • Continuous variables: Predictor increases by 1 standard deviation
  • Binary variables: Predictor changes from 0 to 1 (e.g., control → treatment)
  • Factor variables: Each level compared to reference level (first level)

Practical examples:

model.set_effects("treatment=0.5, age=0.3, income=0.2")

• treatment=0.5: Treatment increases outcome by 0.5 SD (medium-large effect)
• age=0.3: Each 1 SD increase in age → 0.3 SD increase in outcome
• income=0.2: Each 1 SD increase in income → 0.2 SD increase in outcome

Effect size guidelines:

• 0.1 = Small effect (detectable but modest)
• 0.25 = Medium effect (clearly noticeable)
• 0.4 = Large effect (substantial impact)

Effect size guidelines (binary variables):

• 0.2 = Small effect (detectable but modest)
• 0.5 = Medium effect (clearly noticeable)
• 0.8 = Large effect (substantial impact)

Your uploaded data is automatically standardized (mean=0, SD=1) so effect sizes work the same way whether you use synthetic or real data.

Copy-Paste Examples for Common Studies

Randomized Controlled Trial

import mcpower

# RCT with treatment + control variables
model = mcpower.MCPower("outcome = treatment + age + baseline_score")
model.set_effects("treatment=0.6, age=0.2, baseline_score=0.8")
model.set_variable_type("treatment=binary")  # 0/1 treatment

# Find sample size for treatment effect with scenario analysis
model.find_sample_size(target_test="treatment", from_size=100, to_size=500, 
                      by=50, scenarios=True)

A/B Test with Interaction

import mcpower

# Test if treatment effect depends on user type
model = mcpower.MCPower("conversion = treatment + user_type + treatment*user_type")
model.set_effects("treatment=0.4, user_type=0.3, treatment:user_type=0.5")
model.set_variable_type("treatment=binary, user_type=binary")

# Check power robustness for the interaction
model.find_power(sample_size=400, target_test="treatment:user_type", scenarios=True)

Multi-Group Study with Categorical Variables

import mcpower

# Study with 3 treatment groups and 4 education levels
model = mcpower.MCPower("wellbeing = treatment + education + age")
model.set_variable_type("treatment=(factor,3), education=(factor,4)")

# Set effects for each factor level (vs. reference level 1)
model.set_effects("treatment[2]=0.4, treatment[3]=0.6, education[2]=0.3, education[3]=0.5, education[4]=0.7, age=0.2")

# Find sample size for treatment effects
model.find_sample_size(target_test="treatment[2], treatment[3]", scenarios=True)

Survey with Correlated Predictors

import mcpower

# Predictors are often correlated in real data
model = mcpower.MCPower("wellbeing = income + education + social_support")
model.set_effects("income=0.4, education=0.3, social_support=0.6")
model.set_correlations("corr(income, education)=0.5, corr(income, social_support)=0.3")

# Find sample size for any effect
model.find_sample_size(target_test="all", from_size=200, to_size=800, 
                      by=100, scenarios=True)

Customize for Your Study

Different Variable Types

# Binary, factors, skewed, or other distributions
model.set_variable_type("treatment=binary, condition=(factor,3), income=right_skewed, age=normal")

# Binary with custom proportions (30% get treatment)
model.set_variable_type("treatment=(binary,0.3)")

# Factors with custom group sizes (20%, 50%, 30%)
model.set_variable_type("condition=(factor,0.2,0.5,0.3)")

Working with Factors (Categorical Variables)

# Factors automatically create dummy variables
model = mcpower.MCPower("outcome = treatment + education")
model.set_variable_type("treatment=(factor,3), education=(factor,4)")

# Set effects for specific levels (level 1 is always reference)
model.set_effects("treatment[2]=0.5, treatment[3]=0.7, education[2]=0.3, education[3]=0.4, education[4]=0.6")

Your Own Data

Use upload_data() to preserve real-world distribution shapes and relationships:

import pandas as pd

# Load your data
data = pd.read_csv("my_data.csv")

# Upload with automatic type detection
model = mcpower.MCPower("mpg = hp + wt + cyl")
model.upload_data(data[["hp", "wt", "cyl"]])
model.set_effects("hp=0.5, wt=0.3, cyl[2]=0.2, cyl[3]=0.4")
model.find_power(sample_size=100)

Auto-Detection

Variables are automatically classified based on unique values:

• 1 unique value: Dropped (constant)
• 2 unique values: Binary variable
• 3-6 unique values: Factor/categorical variable
• 7+ unique values: Continuous variable

Correlation Preservation Modes

Control how correlations are handled with the preserve_correlation parameter:

# No correlation preservation
model.upload_data(data, preserve_correlation="no")

# Partial: Compute correlations from data, merge with user settings
model.upload_data(data, preserve_correlation="partial")

# Strict: Bootstrap whole rows to preserve exact relationships (default)
model.upload_data(data, preserve_correlation="strict")

Override Auto-Detection

Force specific variable types:

model.upload_data(
    data,
    data_types={"cyl": "factor", "hp": "continuous"}
)

Multiple Testing

# Testing multiple effects? Control false positives
model.find_power(
    sample_size=200, 
    target_test="treatment,covariate,treatment:covariate",
    correction="Benjamini-Hochberg",
    scenarios=True  # Test robustness too!
)

Test the single violation of assumptions.

# Customize how much "messiness" to add in scenarios
model.set_heterogeneity(0.2)        # Effect sizes vary between people
model.set_heteroskedasticity(0.15)  # Violation of equal variance assumption

# Then run scenario analysis
model.find_sample_size(target_test="treatment", scenarios=False)

Mixed-Effects Models (Random Intercept)

import mcpower

# Define a model with a random intercept for clustered data
model = mcpower.MCPower("satisfaction ~ treatment + motivation + (1|school)")
model.set_cluster("school", ICC=0.2, n_clusters=20)
model.set_effects("treatment=0.5, motivation=0.3")
model.set_variable_type("treatment=binary")

# Allow some convergence failures (common with mixed models)
model.set_max_failed_simulations(0.10)  # Up to 10% failures tolerated

# Total sample_size is split across clusters: 1000 / 20 = 50 per cluster
model.find_power(sample_size=1000)

Constraints:

• Only random intercepts (1|group) — random slopes are not yet supported
• At least 25 observations per cluster and 10 observations per model parameter
• ICC must be between 0.1 and 0.9 (or exactly 0)

More precision

# To make a more precise estimation, consider increasing the number of simulations.
model.set_simulations(10000)

# Parallelization is enabled by default for mixed models ("mixedmodels" mode).
# To enable it for all analyses:
model.set_parallel(True)

# To disable parallelization entirely:
model.set_parallel(False)

Reproducibility & programmatic use

# Set a seed for reproducible results
model.set_seed(42)

# All set_* methods support chaining
model.set_effects("x1=0.5").set_variable_type("x1=binary").set_alpha(0.01)

# Get results as a Python dict for further processing
results = model.find_power(sample_size=200, return_results=True)

# Custom progress callback (useful in notebooks or GUIs)
model.find_power(sample_size=200, progress_callback=lambda cur, tot: print(f"{cur}/{tot}"))

# Disable progress output entirely
model.find_power(sample_size=200, progress_callback=False)

Quick Reference

Want to...	Use this
Find required sample size	model.find_sample_size(target_test="effect_name")
Check power for specific N	model.find_power(sample_size=150, target_test="effect_name")
Test robustness	Add scenarios=True to either method
Detailed output with plots	Add summary="long" to either method
Test overall model	target_test="overall"
Test multiple effects	target_test="effect1, effect2" or "all"
Binary variables	model.set_variable_type("var=binary")
Factor variables	model.set_variable_type("var=(factor,3)")
Factor effects	model.set_effects("var[2]=0.5, var[3]=0.7")
Correlated predictors	model.set_correlations("corr(var1, var2)=0.4")
Multiple testing correction	Add correction="FDR", "Holm", or "Bonferroni"
Mixed model (random intercept)	MCPower("y ~ x + (1|group)") + model.set_cluster(...)
Reproducible results	model.set_seed(42)
Get results as dict	Add return_results=True to either method
Stricter significance	model.set_alpha(0.01)
Target 90% power	model.set_power(90)

When to Use MCPower

✅ Use MCPower when you have:

• Interaction terms (treatment*covariate)
• Categorical variables with multiple levels
• Binary or non-normal variables
• Correlated predictors
• Multiple effects to test
• Need to test assumption robustness
• Complex models where traditional power analysis fails

✅ Use Scenario Analysis when:

• Planning important studies
• Working with messy real-world data
• Effect sizes are uncertain
• Want conservative sample size estimates
• You need confidence in your numbers

❌ Use traditional power analysis for:

• For models that are not yet implemented
• For simple models where all assumptions are clearly met.
• For large analyses with tens of thousands of observations, tiny effects, or very low alpha levels.

What Makes Scenarios Different? (Be careful, unvalidated, preliminary scenarios)

Traditional power analysis assumes perfect conditions. MCPower's scenarios add realistic "messiness":

Scenario	What's Different	When to Use
Optimistic	Your exact settings	Best-case planning
Realistic	Mild effect variations, small assumption violations	Recommended for most studies
Doomer	Larger effect variations, stronger assumption violations	Conservative/worst-case planning

Behind the scenes, scenarios randomly vary:

• Effect sizes between participants
• Correlation strengths
• Variable distributions
• Assumption violations

This gives you a range of realistic outcomes instead of a single optimistic estimate. ⚠️ Important: Scenario analysis is rule of thumb recognition of condition, and could not be accurate in all settings, as it tries to cover many diffrent fields reality.

📚 Advanced Features (Click to expand)

Advanced Options

All Variable Types

model.set_variable_type("""
    treatment=binary,           # 0/1 with 50% split
    ses=(binary,0.3),          # 0/1 with 30% split  
    condition=(factor,3),       # 3-level factor (equal proportions)
    education=(factor,0.2,0.5,0.3), # 3-level factor (custom proportions)
    age=normal,                # Standard normal (default)
    income=right_skewed,       # Positively skewed
    depression=left_skewed,    # Negatively skewed
    response_time=high_kurtosis, # Heavy-tailed
    rating=uniform             # Uniform distribution
""")

Factor Variables in Detail

# Factor variables are categorical with multiple levels
model = mcpower.MCPower("outcome = treatment + education")

# Create factors
model.set_variable_type("treatment=(factor,3), education=(factor,4)")

# This creates dummy variables automatically:
# treatment[2], treatment[3] (treatment[1] is reference)
# education[2], education[3], education[4] (education[1] is reference)

# Set effects for specific levels
model.set_effects("treatment[2]=0.5, treatment[3]=0.7, education[2]=0.3")

# Or set same effect for all levels of a factor
model.set_effects("treatment=0.5")  # Applies to treatment[2] and treatment[3]

# Important: Factors cannot be used in correlations
# This will error: model.set_correlations("corr(treatment, education)=0.3")
# Use continuous variables only: model.set_correlations("corr(age, income)=0.3")

Complex Correlation Structures

import numpy as np

# Full correlation matrix for 3 CONTINUOUS variables only
# (Factors are excluded from correlation matrices)
corr_matrix = np.array([
    [1.0, 0.4, 0.6],    # Variable 1 with others
    [0.4, 1.0, 0.2],    # Variable 2 with others
    [0.6, 0.2, 1.0]     # Variable 3 with others
])
model.set_correlations(corr_matrix)

Performance Tuning

# Adjust for your needs
model.set_power(90)           # Target 90% power instead of 80%
model.set_alpha(0.01)         # Stricter significance (p < 0.01)
model.set_simulations(10000)  # High precision (slower)

Formula Syntax

# These are equivalent:
"y = x1 + x2 + x1*x2"        # Assignment style
"y ~ x1 + x2 + x1*x2"        # R-style formula  
"x1 + x2 + x1*x2"            # Predictors only

# Interactions:
"x1*x2"         # Main effects + interaction (x1 + x2 + x1:x2)
"x1:x2"         # Interaction only
"x1*x2*x3"      # All main effects + all interactions

Correlation Syntax (Continuous Variables Only)

# String format (recommended)
model.set_correlations("corr(x1, x2)=0.3, corr(x1, x3)=-0.2")

# Shorthand format  
model.set_correlations("(x1, x2)=0.3, (x1, x3)=-0.2")

# Note: Factor variables cannot be correlated
# Only use continuous/binary variables in correlations

Requirements

• Python ≥ 3.10
• NumPy, SciPy, matplotlib, Pandas, joblib
• C++ compiler (automatically used during install for native backend; falls back to Python if unavailable)
• statsmodels (optional, for mixed-effects models — install with pip install mcpower[mixed])
• Numba (optional, for JIT compilation fallback — install with pip install mcpower[JIT])

Need Help?

• Issues: GitHub Issues
• Questions: [e-mail]

Aim for future (waiting for suggestions)

• ✅ Linear Regression
• ✅ Scenarios, robustness analysis
• ✅ Factor variables (categorical predictors)
• ✅ C++ native backend (pybind11 + Eigen, 3x speedup)
• 🚧 Mixed Effects Models (partly implemented — random intercept only)
• 🚧 Logistic Regression (coming soon)
• 🚧 ANOVA (coming soon)
• 🚧 Guide about methods, corrections (coming soon)
• 📋 2 groups comparison with alternative tests
• 📋 Robust regression methods

License & Citation

GPL v3. If you use MCPower in research, please cite:

Lenartowicz, P. (2025). MCPower: Monte Carlo Power Analysis for Statistical Models. Zenodo. DOI: 10.5281/zenodo.16502734

@software{mcpower2025,
  author = {Pawel Lenartowicz},
  title = {MCPower: Monte Carlo Power Analysis for Statistical Models},
  year = {2025},
  publisher = {Zenodo},
  doi = {10.5281/zenodo.16502734},
  url = {https://doi.org/10.5281/zenodo.16502734}
}

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🚀 Ready to start? Copy one of the examples above and adapt it to your study!

I created this project for free without receiving any payment, and if you'd like to support my work, donations are appreciated!

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