Collate regression models into a standardised tibble
collate_models.RdCollate regression models into a standardised tibble
Examples
# Use the R Swiss data for examples with a random treatment
set.seed(1234)
swiss <- dplyr::bind_cols(swiss,
treatment = rbinom(seq(nrow(swiss)),1, 0.5)) |>
dplyr::mutate(Education = Education + 0.15 * treatment)
reg1 <- lm(formula = Education ~ treatment, swiss)
reg2 <- lm(formula = Education ~ treatment + Catholic, swiss)
reg3 <- lm(formula = Education ~ treatment * Catholic, swiss)
models <- list(reg1,
reg2,
reg3)
collate_models(models = models)
#> # A tibble: 9 × 20
#> modelID term estimate std.error statistic p.value conf.low conf.high
#> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 (Intercept) 10.1 2.02 5.02 8.70e-6 6.06 14.2
#> 2 1 treatment 1.81 2.83 0.641 5.25e-1 -3.88 7.50
#> 3 2 (Intercept) 11.6 2.42 4.77 2.05e-5 6.68 16.4
#> 4 2 treatment 1.94 2.82 0.687 4.95e-1 -3.75 7.63
#> 5 2 Catholic -0.0364 0.0342 -1.06 2.93e-1 -0.105 0.0326
#> 6 3 (Intercept) 11.7 2.75 4.24 1.15e-4 6.12 17.2
#> 7 3 treatment 1.72 4.05 0.425 6.73e-1 -6.44 9.89
#> 8 3 Catholic -0.0388 0.0468 -0.830 4.11e-1 -0.133 0.0555
#> 9 3 treatment:Cat… 0.00530 0.0695 0.0762 9.40e-1 -0.135 0.146
#> # ℹ 12 more variables: r.squared <dbl>, adj.r.squared <dbl>, sigma <dbl>,
#> # statistic_model <dbl>, p.value_model <dbl>, df <dbl>, logLik <dbl>,
#> # AIC <dbl>, BIC <dbl>, deviance <dbl>, df.residual <int>, nobs <int>