mcpower 0.3.1

Monte Carlo Power Analysis for Statistical Models

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, 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, any distribution
✅ R-style formulas: outcome = treatment + covariate + treatment*covariate
✅ 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 (every few days).

pip install --upgrade mcpower

Your First Power Analysis

# 0. First initialization could take a few seconds, due to compilation (package is not distributed yet with compiled files)
import mcpower

# 1. Define your model (just like R)
model = mcpower.LinearRegression("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)

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)

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.LinearRegression("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.LinearRegression("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)

Survey with Correlated Predictors

import mcpower

# Predictors are often correlated in real data
model = mcpower.LinearRegression("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 (0/1), skewed, or other distributions
model.set_variable_type("treatment=binary, income=right_skewed, age=normal")

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

Your Own Data (be careful, not yet well tested)

import pandas as pd

# Use your pilot data for realistic simulations
df = pd.read_csv('examples/cars.csv')
model.upload_own_data(df)  # Automatically preserves correlations

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)

More precision

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

# MCPower is already heavily optimized, but there is old code that allows for parallelization. Use it to speed up your largest simulations.
model.set_parallel(True)

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
Test overall model	target_test="overall"
Test multiple effects	target_test="effect1, effect2" or "all"
Binary variables	model.set_variable_type("var=binary")
Correlated predictors	model.set_correlations("corr(var1, var2)=0.4")
Multiple testing correction	Add correction="FDR", or "Holm" pr "Bonferroni"

When to Use MCPower

✅ Use MCPower when you have:

• Interaction terms (treatment*covariate)
• 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 (grants, dissertations)
• Working with messy real-world data
• Effect sizes are uncertain
• Want conservative sample size estimates
• Stakeholders need confidence in your numbers

❌ Use traditional power analysis for:

• For models that are not yet implemented
• When all assumptions are clearly met

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 and uploaded data features are experimental. Use with caution for critical decisions.

📚 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  
    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
""")

Complex Correlation Structures

import numpy as np

# Full correlation matrix for 3 variables
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

# 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")

Requirements

• Python ≥ 3.7
• NumPy, SciPy, scikit-learn, matplotlib
• joblib (optional, for parallel processing)

Need Help?

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

Aim for future (waiting for suggestions)

• ✅ Linear Regression
• ✅ Scenarios, robustness analysis
• 🚧 Logistic Regression (coming soon)
• 🚧 ANOVA (and factor as variables) (coming soon)
• 🚧 Guide about methods, corrections (coming soon)
• 📋 Rewriting to Cython (backend change)
• 📋 Mixed Effects Models
• 📋 2 groups comparision with alternative tests
• 📋 Robust regression methods

License & Citation

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

@software{mcpower2025,
  author = {Pawel Lenartowicz},
  title = {MCPower: Monte Carlo Power Analysis for Statistical Models},
  year = {2025},
  url = {https://github.com/pawlenartowicz/MCPower}
}

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