construct_model(
+ x = mtcars,
+ formula = am ~ mpg + (1 | vs),
+ method = "glmer",
+ method.args = list(family = binomial),
+ package = "lme4"
+)
+#> Generalized linear mixed model fit by maximum likelihood (Laplace
+#> Approximation) [glmerMod]
+#> Family: binomial ( logit )
+#> Formula: am ~ mpg + (1 | vs)
+#> Data: structure(list(mpg = c(21, 21, 22.8, 21.4, 18.7, 18.1, 14.3,
+#> 24.4, 22.8, 19.2, 17.8, 16.4, 17.3, 15.2, 10.4, 10.4, 14.7, 32.4,
+#> 30.4, 33.9, 21.5, 15.5, 15.2, 13.3, 19.2, 27.3, 26, 30.4, 15.8,
+#> 19.7, 15, 21.4), cyl = c(6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8,
+#> 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, 4, 4, 4, 8, 6, 8, 4),
+#> disp = c(160, 160, 108, 258, 360, 225, 360, 146.7, 140.8,
+#> 167.6, 167.6, 275.8, 275.8, 275.8, 472, 460, 440, 78.7, 75.7,
+#> 71.1, 120.1, 318, 304, 350, 400, 79, 120.3, 95.1, 351, 145,
+#> 301, 121), hp = c(110, 110, 93, 110, 175, 105, 245, 62, 95,
+#> 123, 123, 180, 180, 180, 205, 215, 230, 66, 52, 65, 97, 150,
+#> 150, 245, 175, 66, 91, 113, 264, 175, 335, 109), drat = c(3.9,
+#> 3.9, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,
+#> 3.07, 3.07, 3.07, 2.93, 3, 3.23, 4.08, 4.93, 4.22, 3.7, 2.76,
+#> 3.15, 3.73, 3.08, 4.08, 4.43, 3.77, 4.22, 3.62, 3.54, 4.11
+#> ), wt = c(2.62, 2.875, 2.32, 3.215, 3.44, 3.46, 3.57, 3.19,
+#> 3.15, 3.44, 3.44, 4.07, 3.73, 3.78, 5.25, 5.424, 5.345, 2.2,
+#> 1.615, 1.835, 2.465, 3.52, 3.435, 3.84, 3.845, 1.935, 2.14,
+#> 1.513, 3.17, 2.77, 3.57, 2.78), qsec = c(16.46, 17.02, 18.61,
+#> 19.44, 17.02, 20.22, 15.84, 20, 22.9, 18.3, 18.9, 17.4, 17.6,
+#> 18, 17.98, 17.82, 17.42, 19.47, 18.52, 19.9, 20.01, 16.87,
+#> 17.3, 15.41, 17.05, 18.9, 16.7, 16.9, 14.5, 15.5, 14.6, 18.6
+#> ), vs = c(0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0,
+#> 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1), am = c(1,
+#> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,
+#> 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1), gear = c(4, 4, 4, 3,
+#> 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3,
+#> 3, 3, 4, 5, 5, 5, 5, 5, 4), carb = c(4, 4, 1, 1, 2, 1, 4,
+#> 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, 1,
+#> 2, 2, 4, 6, 8, 2)), row.names = c("Mazda RX4", "Mazda RX4 Wag",
+#> "Datsun 710", "Hornet 4 Drive", "Hornet Sportabout", "Valiant",
+#> "Duster 360", "Merc 240D", "Merc 230", "Merc 280", "Merc 280C",
+#> "Merc 450SE", "Merc 450SL", "Merc 450SLC", "Cadillac Fleetwood",
+#> "Lincoln Continental", "Chrysler Imperial", "Fiat 128", "Honda Civic",
+#> "Toyota Corolla", "Toyota Corona", "Dodge Challenger", "AMC Javelin",
+#> "Camaro Z28", "Pontiac Firebird", "Fiat X1-9", "Porsche 914-2",
+#> "Lotus Europa", "Ford Pantera L", "Ferrari Dino", "Maserati Bora",
+#> "Volvo 142E"), class = "data.frame")
+#> AIC BIC logLik deviance df.resid
+#> 35.2503 39.6475 -14.6251 29.2503 29
+#> Random effects:
+#> Groups Name Std.Dev.
+#> vs (Intercept) 0.7896
+#> Number of obs: 32, groups: vs, 2
+#> Fixed Effects:
+#> (Intercept) mpg
+#> -8.7018 0.4085
+
+construct_model(
+ x = mtcars |> dplyr::rename(`M P G` = mpg),
+ formula = reformulate2(c("M P G", "cyl"), response = "hp"),
+ method = "lm"
+) |>
+ ard_regression() |>
+ dplyr::filter(stat_name %in% c("term", "estimate", "p.value"))
+#> {cards} data frame: 6 x 7
+#> variable variable_level context stat_name stat_label stat
+#> 1 M P G NA regressi… term term `M P G`
+#> 2 M P G NA regressi… estimate Coeffici… -2.775
+#> 3 M P G NA regressi… p.value p-value 0.213
+#> 4 cyl NA regressi… term term cyl
+#> 5 cyl NA regressi… estimate Coeffici… 23.979
+#> 6 cyl NA regressi… p.value p-value 0.003
+#> ℹ 1 more variable: fmt_fn
+
