# 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
```bash
pip install mcpower
```
### Update to the latest version (every few days).
```bash
pip install --upgrade mcpower
```
### Your First Power Analysis
```python
# 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.
```python
# 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:**
```python
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
```python
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
```python
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
```python
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
```python
# 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)
```python
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
```python
# 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.
```python
# 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
```python
# 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.
<details>
<summary><strong>📚 Advanced Features (Click to expand)</strong></summary>
## Advanced Options
### All Variable Types
```python
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
```python
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
```python
# 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
```python
# 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
```python
# 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")
```
</details>
## Requirements
- Python ≥ 3.7
- NumPy, SciPy, scikit-learn, matplotlib
- joblib (optional, for parallel processing)
## Need Help?
- **Issues**: [GitHub Issues](https://github.com/pawlenartowicz/MCPower/issues)
- **Questions**: pawellenartowicz@europe.com
## 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:
```bibtex
@software{mcpower2025,
author = {Pawel Lenartowicz},
title = {MCPower: Monte Carlo Power Analysis for Statistical Models},
year = {2025},
url = {https://github.com/pawlenartowicz/MCPower}
}
```
---
**🚀 Ready to start?** Copy one of the examples above and adapt it to your study!
Raw data
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"description": "# MCPower\n\n**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.\n\n## Why MCPower?\n\n**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.\n\n\u2705 **Works with complexity**: Interactions, correlations, any distribution \n\u2705 **R-style formulas**: `outcome = treatment + covariate + treatment*covariate` \n\u2705 **Two simple commands**: Find sample size or check power \n\u2705 **Scenario analysis**: Test robustness under realistic conditions \n\u2705 **Minimal math required**: Just specify your model and effects\n\n## Get Started in 2 Minutes\n\n### Install\n```bash\npip install mcpower\n```\n\n### Update to the latest version (every few days).\n```bash\npip install --upgrade mcpower\n```\n\n### Your First Power Analysis\n```python\n\n# 0. First initialization could take a few seconds, due to compilation (package is not distributed yet with compiled files)\nimport mcpower\n\n# 1. Define your model (just like R)\nmodel = mcpower.LinearRegression(\"satisfaction = treatment + motivation\")\n\n# 2. Set effect sizes (how big you expect effects to be)\nmodel.set_effects(\"treatment=0.5, motivation=0.3\")\n\n# 3. Change the treatment to \"binary\" (people receive treatment or not).\nmodel.set_variable_type(\"treatment=binary\")\n\n# 4. Find the sample size you need\nmodel.find_sample_size(target_test=\"treatment\", from_size=50, to_size=200)\n```\n**Output**: \"You need N=75 for 80% power to detect the treatment effect\"\n\nThat's it! \ud83c\udf89\n\n## \ud83c\udfaf Scenario Analysis: Test Your Assumptions\n\n**Real studies rarely match perfect assumptions.** MCPower's scenario analysis tests how robust your power calculations are under realistic conditions.\n\n```python\n# Test robustness with scenario analysis\nmodel.find_sample_size(\n target_test=\"treatment\", \n from_size=50, to_size=300,\n scenarios=True # \ud83d\udd25 The magic happens here\n)\n```\n\n**Output:**\n```\nSCENARIO SUMMARY\n================================================================================\n\nUncorrected Sample Sizes:\nTest Optimistic Realistic Doomer \n-------------------------------------------------------------------------------\ntreatment 75 85 100 \n================================================================================\n```\n\n**What each scenario means:**\n- **Optimistic**: Your ideal conditions (original settings)\n- **Realistic**: Moderate real-world complications (small effect variations, mild assumption violations)\n- **Doomer**: Conservative estimate (larger effect variations, stronger assumption violations)\n\n**\ud83d\udca1 Pro tip**: Use the **Realistic** scenario for planning. If **Doomer** is acceptable, you're really safe!\n\n## Understanding Effect Sizes\n\n**Effect sizes tell you how much the outcome changes when predictors change.**\n\n- **Effect size = 0.5** means the outcome increases by **0.5 standard deviations** when:\n - **Continuous variables**: Predictor increases by 1 standard deviation \n - **Binary variables**: Predictor changes from 0 to 1 (e.g., control \u2192 treatment)\n\n**Practical examples:**\n```python\nmodel.set_effects(\"treatment=0.5, age=0.3, income=0.2\")\n```\n\n- **`treatment=0.5`**: Treatment increases outcome by 0.5 SD (medium-large effect)\n- **`age=0.3`**: Each 1 SD increase in age \u2192 0.3 SD increase in outcome \n- **`income=0.2`**: Each 1 SD increase in income \u2192 0.2 SD increase in outcome\n\n**Effect size guidelines:**\n- **0.1** = Small effect (detectable but modest)\n- **0.25** = Medium effect (clearly noticeable) \n- **0.4** = Large effect (substantial impact)\n\n**Effect size guidelines (binary variables):**\n- **0.2** = Small effect (detectable but modest)\n- **0.5** = Medium effect (clearly noticeable) \n- **0.8** = Large effect (substantial impact)\n\n**Your uploaded data is automatically standardized** (mean=0, SD=1) so effect sizes work the same way whether you use synthetic or real data.\n\n## Copy-Paste Examples for Common Studies\n\n### Randomized Controlled Trial\n```python\nimport mcpower\n\n# RCT with treatment + control variables\nmodel = mcpower.LinearRegression(\"outcome = treatment + age + baseline_score\")\nmodel.set_effects(\"treatment=0.6, age=0.2, baseline_score=0.8\")\nmodel.set_variable_type(\"treatment=binary\") # 0/1 treatment\n\n# Find sample size for treatment effect with scenario analysis\nmodel.find_sample_size(target_test=\"treatment\", from_size=100, to_size=500, \n by=50, scenarios=True)\n```\n\n### A/B Test with Interaction\n```python\nimport mcpower\n\n# Test if treatment effect depends on user type\nmodel = mcpower.LinearRegression(\"conversion = treatment + user_type + treatment*user_type\")\nmodel.set_effects(\"treatment=0.4, user_type=0.3, treatment:user_type=0.5\")\nmodel.set_variable_type(\"treatment=binary, user_type=binary\")\n\n# Check power robustness for the interaction\nmodel.find_power(sample_size=400, target_test=\"treatment:user_type\", scenarios=True)\n```\n\n### Survey with Correlated Predictors\n```python\nimport mcpower\n\n# Predictors are often correlated in real data\nmodel = mcpower.LinearRegression(\"wellbeing = income + education + social_support\")\nmodel.set_effects(\"income=0.4, education=0.3, social_support=0.6\")\nmodel.set_correlations(\"corr(income, education)=0.5, corr(income, social_support)=0.3\")\n\n# Find sample size for any effect\nmodel.find_sample_size(target_test=\"all\", from_size=200, to_size=800, \n by=100, scenarios=True)\n```\n\n## Customize for Your Study\n\n### Different Variable Types\n```python\n# Binary (0/1), skewed, or other distributions\nmodel.set_variable_type(\"treatment=binary, income=right_skewed, age=normal\")\n\n# Binary with custom proportions (30% get treatment)\nmodel.set_variable_type(\"treatment=(binary,0.3)\")\n```\n\n### Your Own Data (be careful, not yet well tested)\n```python\nimport pandas as pd\n\n# Use your pilot data for realistic simulations\ndf = pd.read_csv('examples/cars.csv')\nmodel.upload_own_data(df) # Automatically preserves correlations\n```\n\n### Multiple Testing\n```python\n# Testing multiple effects? Control false positives\nmodel.find_power(\n sample_size=200, \n target_test=\"treatment,covariate,treatment:covariate\",\n correction=\"Benjamini-Hochberg\",\n scenarios=True # Test robustness too!\n)\n```\n\n### Test the single violation of assumptions.\n```python\n# Customize how much \"messiness\" to add in scenarios\nmodel.set_heterogeneity(0.2) # Effect sizes vary between people\nmodel.set_heteroskedasticity(0.15) # Violation of equal variance assumption\n\n# Then run scenario analysis\nmodel.find_sample_size(target_test=\"treatment\", scenarios=False)\n```\n\n### More precision\n```python\n# To make a more precise estimation, consider increasing the number of simulations.\nmodel.set_simulations(10000)\n\n# MCPower is already heavily optimized, but there is old code that allows for parallelization. Use it to speed up your largest simulations.\nmodel.set_parallel(True)\n\n```\n\n## Quick Reference\n\n| **Want to...** | **Use this** |\n|-----------------|--------------|\n| Find required sample size | `model.find_sample_size(target_test=\"effect_name\")` |\n| Check power for specific N | `model.find_power(sample_size=150, target_test=\"effect_name\")` |\n| **Test robustness** | **Add `scenarios=True` to either method** |\n| Test overall model | `target_test=\"overall\"` |\n| Test multiple effects | `target_test=\"effect1, effect2\"` or `\"all\"` |\n| Binary variables | `model.set_variable_type(\"var=binary\")` |\n| Correlated predictors | `model.set_correlations(\"corr(var1, var2)=0.4\")` |\n| Multiple testing correction | Add `correction=\"FDR\", or \"Holm\" pr \"Bonferroni\"`|\n\n## When to Use MCPower\n\n**\u2705 Use MCPower when you have:**\n- Interaction terms (`treatment*covariate`)\n- Binary or non-normal variables\n- Correlated predictors\n- Multiple effects to test\n- **Need to test assumption robustness**\n- Complex models where traditional power analysis fails\n\n**\u2705 Use Scenario Analysis when:**\n- Planning important studies (grants, dissertations)\n- Working with messy real-world data\n- Effect sizes are uncertain\n- Want conservative sample size estimates\n- Stakeholders need confidence in your numbers\n\n**\u274c Use traditional power analysis for:**\n- For models that are not yet implemented\n- When all assumptions are clearly met\n\n## What Makes Scenarios Different? (Be careful, unvalidated, preliminary scenarios)\n\n**Traditional power analysis assumes perfect conditions.** MCPower's scenarios add realistic \"messiness\":\n\n| **Scenario** | **What's Different** | **When to Use** |\n|-------------|---------------------|------------------|\n| **Optimistic** | Your exact settings | Best-case planning |\n| **Realistic** | Mild effect variations, small assumption violations | **Recommended for most studies** |\n| **Doomer** | Larger effect variations, stronger assumption violations | Conservative/worst-case planning |\n\n**Behind the scenes**, scenarios randomly vary:\n- Effect sizes between participants\n- Correlation strengths \n- Variable distributions\n- Assumption violations\n\nThis gives you a **range of realistic outcomes** instead of a single optimistic estimate.\n\u26a0\ufe0f **Important**: Scenario analysis and uploaded data features are experimental. \nUse with caution for critical decisions.\n\n<details>\n<summary><strong>\ud83d\udcda Advanced Features (Click to expand)</strong></summary>\n\n## Advanced Options\n\n### All Variable Types\n```python\nmodel.set_variable_type(\"\"\"\n treatment=binary, # 0/1 with 50% split\n ses=(binary,0.3), # 0/1 with 30% split \n age=normal, # Standard normal (default)\n income=right_skewed, # Positively skewed\n depression=left_skewed, # Negatively skewed\n response_time=high_kurtosis, # Heavy-tailed\n rating=uniform # Uniform distribution\n\"\"\")\n```\n\n### Complex Correlation Structures\n```python\nimport numpy as np\n\n# Full correlation matrix for 3 variables\ncorr_matrix = np.array([\n [1.0, 0.4, 0.6], # Variable 1 with others\n [0.4, 1.0, 0.2], # Variable 2 with others\n [0.6, 0.2, 1.0] # Variable 3 with others\n])\nmodel.set_correlations(corr_matrix)\n```\n\n### Performance Tuning\n```python\n# Adjust for your needs\nmodel.set_power(90) # Target 90% power instead of 80%\nmodel.set_alpha(0.01) # Stricter significance (p < 0.01)\nmodel.set_simulations(10000) # High precision (slower)\n```\n\n### Formula Syntax\n```python\n# These are equivalent:\n\"y = x1 + x2 + x1*x2\" # Assignment style\n\"y ~ x1 + x2 + x1*x2\" # R-style formula \n\"x1 + x2 + x1*x2\" # Predictors only\n\n# Interactions:\n\"x1*x2\" # Main effects + interaction (x1 + x2 + x1:x2)\n\"x1:x2\" # Interaction only\n\"x1*x2*x3\" # All main effects + all interactions\n```\n\n### Correlation Syntax\n```python\n# String format (recommended)\nmodel.set_correlations(\"corr(x1, x2)=0.3, corr(x1, x3)=-0.2\")\n\n# Shorthand format \nmodel.set_correlations(\"(x1, x2)=0.3, (x1, x3)=-0.2\")\n```\n\n</details>\n\n## Requirements\n\n- Python \u2265 3.7\n- NumPy, SciPy, scikit-learn, matplotlib\n- joblib (optional, for parallel processing)\n\n## Need Help?\n\n- **Issues**: [GitHub Issues](https://github.com/pawlenartowicz/MCPower/issues)\n- **Questions**: pawellenartowicz@europe.com\n\n## Aim for future (waiting for suggestions)\n- \u2705 Linear Regression\n- \u2705 Scenarios, robustness analysis\n- \ud83d\udea7 Logistic Regression (coming soon)\n- \ud83d\udea7 ANOVA (and factor as variables) (coming soon)\n- \ud83d\udea7 Guide about methods, corrections (coming soon)\n- \ud83d\udccb Rewriting to Cython (backend change)\n- \ud83d\udccb Mixed Effects Models\n- \ud83d\udccb 2 groups comparision with alternative tests\n- \ud83d\udccb Robust regression methods\n\n\n## License & Citation\n\nGPL v3. If you use MCPower in research, please cite:\n\n```bibtex\n@software{mcpower2025,\n author = {Pawel Lenartowicz},\n title = {MCPower: Monte Carlo Power Analysis for Statistical Models},\n year = {2025},\n url = {https://github.com/pawlenartowicz/MCPower}\n}\n```\n\n---\n\n**\ud83d\ude80 Ready to start?** Copy one of the examples above and adapt it to your study!\n",
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