A data frame containing regression weights for three different simulated statistical models.
Format
A data frame with 11 rows and 3 columns:
- wt_lin
Linear regression weights for continuous outcomes
- wt_prb
Logistic regression weights for binary outcomes (log-odds scale)
- wt_cnt
Poisson regression weights for count outcomes (log scale)
Row names represent 10 CpG probe identifiers plus "Intercept" term.
Model types demonstrated:
Linear: Continuous phenotype prediction
Logistic: Binary classification (disease status, etc.)
Poisson: Count-based outcomes (cell counts, etc.)
Examples
data(wts_df)
str(wts_df)
#> 'data.frame': 11 obs. of 3 variables:
#> $ wt_lin: num -0.00908 -0.00116 0.00598 -0.00756 0.00122 ...
#> $ wt_prb: num 0.165 -0.405 -0.116 -0.226 0.315 ...
#> $ wt_cnt: num 0.0509 0.02584 0.04204 -0.09988 -0.00494 ...
# Extract weights vectors
linear_weights <- setNames(wts_df$wt_lin, rownames(wts_df))
logistic_weights <- setNames(wts_df$wt_prb, rownames(wts_df))
poisson_weights <- setNames(wts_df$wt_cnt, rownames(wts_df))