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Table 4 Marginal and conditional coefficients of variation (R2) for OC-COPD models and Lasso-penalized GLMM variants. The two-step LassoGLMM method, in columns 1 and 2, is presented here. The original LassoGLMM, in columns 3 and 4, omits the first step of correlation-based variable screening, adding all OTUs to the LassoGLMM. The GLMM with correlated genera, in columns 5 and 6, uses the correlation-based variable screening step, adding only those variables that are correlated with the outcome to the model, but modifies the second step to not include the lasso penalty. Each method column contains the marginal and conditional R2 that represent fit of the fixed effects and entire model, respectively

From: Measuring associations between the microbiota and repeated measures of continuous clinical variables using a lasso-penalized generalized linear mixed model

 

Two-step LassoGLMM

Original LassoGLMM

GLMM with correlated genera

 

Marginal R2

Conditional R2

Marginal R2

Conditional R2

Marginal R2

Conditional R2

BUN (O2)

0.58

0.60

No non zero coefficients

All correlated variables were in Two-step LassoGLMM

IGM (O3)

0.19

0.89

No non zero coefficients

All correlated variables were in Two-step LassoGLMM

SGOT (O7)

0.22

0.84

No non zero coefficients

0.50

0.59

SGPT (O8)

0.44

0.75

No non zero coefficients

All correlated variables were in Two-step LassoGLMM

Cholesterol (O9)

0.80

0.93

0.95

0.98

0.99

1.00