Analyzing the results

R-squared (R2)

R-squared can only be obtained for a linear model, so we perform linear regression in R as follows:

{% code overflow="wrap" %}

# Create a model using linear regression
clinical_model <- lm(PHENO ~ Age + Sex + height + weight + physical_activity + meat + smoking + alcohol + father_cancer + mother_cancer + sibling_cancer + polyps + crohns_disease + ulcerative_colitis, data = merged_df, family = binomial)

# Compute R2
clinical_rsq <- summary(clinical_model)$r.squared

# Print R2
print(clinical_rsq)

{% endcode %}

Area under the curve (AUC)

AUC is computed in R as follows:

{% code overflow="wrap" %}

# Get predicted probabilities
probs_clinical <- predict(model_clinical, type = "response", newdata = merged_df)

# Compute AUC
auc_clinical <- roc(merged_df$PHENO, predict(model_clinical, type = "response"))$auc

# Print AUC
print(auc_clinical)

{% endcode %}

Evaluation metrics

R2	AUC
0.01503821	0.5708