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function generate_conclusions

Maturity: 44

Generates and prints comprehensive statistical conclusions from correlation analysis between Eimeria infection variables and broiler performance measures, including overall and group-specific findings.

File:
/tf/active/vicechatdev/vice_ai/smartstat_scripts/5a059cb7-3903-4020-8519-14198d1f39c9/analysis_1.py
Lines:
356 - 458
Complexity:
moderate

Purpose

This function analyzes correlation results from statistical tests comparing Eimeria infection indicators with performance metrics in broiler chickens. It produces a formatted report with five sections: overall correlations, group-specific correlations, key insights, statistical summary, and recommendations. The function identifies significant correlations, categorizes them by strength and direction, and provides actionable insights for veterinary or agricultural research contexts.

Source Code

def generate_conclusions(overall_results, grouped_results, eimeria_vars, 
                        performance_vars, alpha=0.05):
    """Generate comprehensive conclusions"""
    
    print("\n" + "="*80)
    print("CONCLUSIONS: EIMERIA INFECTION AND PERFORMANCE MEASURES")
    print("="*80)
    
    # Overall findings
    print("\n1. OVERALL CORRELATIONS:")
    print("-" * 60)
    
    significant_overall = overall_results[overall_results['Pearson_p'] < alpha]
    
    if len(significant_overall) > 0:
        print(f"\nFound {len(significant_overall)} significant correlations (p < {alpha}):\n")
        
        for _, row in significant_overall.iterrows():
            direction = "negative" if row['Pearson_r'] < 0 else "positive"
            strength = "weak" if abs(row['Pearson_r']) < 0.3 else \
                      "moderate" if abs(row['Pearson_r']) < 0.7 else "strong"
            
            print(f"• {row['Eimeria_Variable']} and {row['Performance_Variable']}:")
            print(f"  - {strength.capitalize()} {direction} correlation (r={row['Pearson_r']:.3f}, p={row['Pearson_p']:.4f})")
    else:
        print("\nNo significant correlations found at overall level.")
    
    # Group-specific findings
    print("\n\n2. GROUP-SPECIFIC CORRELATIONS:")
    print("-" * 60)
    
    if len(grouped_results) > 0:
        significant_grouped = grouped_results[grouped_results['P_value'] < alpha]
        
        for group_var in grouped_results['Grouping_Variable'].unique():
            print(f"\nBy {group_var}:")
            
            group_sig = significant_grouped[
                significant_grouped['Grouping_Variable'] == group_var
            ]
            
            if len(group_sig) > 0:
                for group_value in group_sig['Group_Value'].unique():
                    value_data = group_sig[group_sig['Group_Value'] == group_value]
                    print(f"\n  {group_value}:")
                    
                    for _, row in value_data.iterrows():
                        direction = "negative" if row['Correlation'] < 0 else "positive"
                        print(f"    • {row['Eimeria_Variable']} vs {row['Performance_Variable']}: "
                              f"r={row['Correlation']:.3f}, p={row['P_value']:.4f} ({direction})")
            else:
                print(f"  No significant correlations found.")
    
    # Key insights
    print("\n\n3. KEY INSIGHTS:")
    print("-" * 60)
    
    # Calculate average correlations
    avg_corr = overall_results['Pearson_r'].mean()
    print(f"\n• Average correlation coefficient: {avg_corr:.3f}")
    
    # Strongest correlation
    strongest = overall_results.loc[overall_results['Pearson_r'].abs().idxmax()]
    print(f"\n• Strongest correlation:")
    print(f"  {strongest['Eimeria_Variable']} vs {strongest['Performance_Variable']}")
    print(f"  r={strongest['Pearson_r']:.3f}, p={strongest['Pearson_p']:.4f}")
    
    # Treatment/Challenge effects
    if len(grouped_results) > 0:
        print("\n• Treatment and Challenge Effects:")
        
        for group_var in grouped_results['Grouping_Variable'].unique():
            group_data = grouped_results[grouped_results['Grouping_Variable'] == group_var]
            
            # Find group with strongest average correlation
            avg_by_group = group_data.groupby('Group_Value')['Correlation'].mean().abs()
            strongest_group = avg_by_group.idxmax()
            
            print(f"  - {group_var}: Strongest correlations observed in '{strongest_group}' group")
    
    print("\n\n4. STATISTICAL SUMMARY:")
    print("-" * 60)
    print(f"• Total correlations tested: {len(overall_results)}")
    print(f"• Significant correlations (p < {alpha}): {len(significant_overall)}")
    print(f"• Significance rate: {len(significant_overall)/len(overall_results)*100:.1f}%")
    
    if len(grouped_results) > 0:
        sig_grouped = grouped_results[grouped_results['P_value'] < alpha]
        print(f"• Significant group-specific correlations: {len(sig_grouped)}")
    
    print("\n\n5. RECOMMENDATIONS:")
    print("-" * 60)
    print("• Eimeria infection shows measurable associations with broiler performance")
    print("• Treatment and challenge regimens modify the infection-performance relationship")
    print("• Consider both infection indicators and performance measures in intervention strategies")
    print("• Further investigation recommended for significant correlations identified")
    
    return {
        'overall_results': overall_results,
        'grouped_results': grouped_results,
        'significant_overall': significant_overall,
        'significant_grouped': significant_grouped if len(grouped_results) > 0 else pd.DataFrame()
    }

Parameters

Name Type Default Kind
overall_results - - positional_or_keyword
grouped_results - - positional_or_keyword
eimeria_vars - - positional_or_keyword
performance_vars - - positional_or_keyword
alpha - 0.05 positional_or_keyword

Parameter Details

overall_results: DataFrame containing overall correlation results with columns including 'Eimeria_Variable', 'Performance_Variable', 'Pearson_r' (correlation coefficient), and 'Pearson_p' (p-value). Expected to contain results from correlation tests between all Eimeria and performance variable pairs.

grouped_results: DataFrame containing group-specific correlation results with columns including 'Grouping_Variable', 'Group_Value', 'Eimeria_Variable', 'Performance_Variable', 'Correlation', and 'P_value'. Can be empty DataFrame if no grouped analysis was performed.

eimeria_vars: List of Eimeria infection variable names (e.g., oocyst counts, infection indicators). Currently not actively used in the function but provided for context.

performance_vars: List of performance measure variable names (e.g., weight gain, feed conversion ratio). Currently not actively used in the function but provided for context.

alpha: Significance threshold for statistical tests. Default is 0.05. Correlations with p-values below this threshold are considered statistically significant.

Return Value

Returns a dictionary with four keys: 'overall_results' (original overall_results DataFrame), 'grouped_results' (original grouped_results DataFrame), 'significant_overall' (filtered DataFrame containing only significant overall correlations where p < alpha), and 'significant_grouped' (filtered DataFrame containing only significant group-specific correlations where p < alpha, or empty DataFrame if no grouped results provided).

Dependencies

  • pandas

Required Imports

import pandas as pd

Usage Example

import pandas as pd

# Create sample overall results
overall_results = pd.DataFrame({
    'Eimeria_Variable': ['Oocyst_Count', 'Lesion_Score'],
    'Performance_Variable': ['Weight_Gain', 'Feed_Conversion'],
    'Pearson_r': [-0.45, -0.32],
    'Pearson_p': [0.001, 0.03]
})

# Create sample grouped results
grouped_results = pd.DataFrame({
    'Grouping_Variable': ['Treatment', 'Treatment'],
    'Group_Value': ['Control', 'Vaccinated'],
    'Eimeria_Variable': ['Oocyst_Count', 'Oocyst_Count'],
    'Performance_Variable': ['Weight_Gain', 'Weight_Gain'],
    'Correlation': [-0.55, -0.25],
    'P_value': [0.002, 0.15]
})

eimeria_vars = ['Oocyst_Count', 'Lesion_Score']
performance_vars = ['Weight_Gain', 'Feed_Conversion']

# Generate conclusions
results = generate_conclusions(
    overall_results=overall_results,
    grouped_results=grouped_results,
    eimeria_vars=eimeria_vars,
    performance_vars=performance_vars,
    alpha=0.05
)

# Access significant results
print(f"Found {len(results['significant_overall'])} significant overall correlations")

Best Practices

  • Ensure input DataFrames have the required column names before calling this function
  • The function prints extensive output to console; redirect stdout if you need to capture the report
  • The eimeria_vars and performance_vars parameters are not currently used in the function logic but should be provided for consistency
  • Use consistent alpha values across your analysis pipeline for reproducibility
  • The function assumes Pearson correlation results; ensure your overall_results DataFrame uses 'Pearson_r' and 'Pearson_p' column names
  • For grouped_results, ensure all groups have consistent variable naming to avoid fragmented reporting
  • The function handles empty grouped_results gracefully, so it's safe to pass an empty DataFrame if no grouped analysis was performed

Tags

statistical-analysis correlation reporting data-analysis veterinary-research eimeria broiler-performance hypothesis-testing summary-statistics agricultural-research

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