mtab

The goal of mtab is to help create data structures from regression models which can be used to create publication ready tables.

Installation

You can install the development version of mtab from GitHub with:

# install.packages("devtools")
devtools::install_github("conig/mtab")

Example

This is a basic example which shows you how to create a hierarchical regression table using {mtab}:

library(mtab)

Titantic <- data.frame(datasets::Titanic) |> 
  tidyr::pivot_wider(values_from = "Freq", names_from = "Survived")

Titantic
#> # A tibble: 16 × 5
#>    Class Sex    Age      No   Yes
#>    <fct> <fct>  <fct> <dbl> <dbl>
#>  1 1st   Male   Child     0     5
#>  2 2nd   Male   Child     0    11
#>  3 3rd   Male   Child    35    13
#>  4 Crew  Male   Child     0     0
#>  5 1st   Female Child     0     1
#>  6 2nd   Female Child     0    13
#>  7 3rd   Female Child    17    14
#>  8 Crew  Female Child     0     0
#>  9 1st   Male   Adult   118    57
#> 10 2nd   Male   Adult   154    14
#> 11 3rd   Male   Adult   387    75
#> 12 Crew  Male   Adult   670   192
#> 13 1st   Female Adult     4   140
#> 14 2nd   Female Adult    13    80
#> 15 3rd   Female Adult    89    76
#> 16 Crew  Female Adult     3    20

m1 <- glm(cbind(Yes, No) ~ Sex, data = Titantic, family = "binomial")
m2 <- glm(cbind(Yes, No) ~ Sex + Class, data = Titantic, family = "binomial")
m3 <- glm(cbind(Yes, No) ~ Sex * Class, data = Titantic, family = "binomial")
m4 <- glm(cbind(Yes, No) ~ Sex * Class + Age, data = Titantic, family = "binomial")
tabby <- h_tab(m1, m2, m3, m4)

Tables can be formatted with a range of functions including kable() or papaja::apa_table

tabby |> 
   knitr::kable(escape = FALSE)

Term	OR [95% CI]	lnOR	SE	z	p	Likelihood Ratio Test
Model 1
(Intercept)	0.27 [0.24, 0.30]	-1.31	0.06	-22.33	< .001
SexFemale	10.15 [8.05, 12.86]	2.32	0.12	19.38	< .001
Model 2						$\chi^2$(3) = 106.08, $p$ = < .001
(Intercept)	0.70 [0.54, 0.92]	-0.35	0.14	-2.60	.009
SexFemale	11.26 [8.60, 14.85]	2.42	0.14	17.41	< .001
Class2nd	0.39 [0.26, 0.56]	-0.95	0.19	-4.90	< .001
Class3rd	0.19 [0.14, 0.26]	-1.66	0.17	-9.88	< .001
ClassCrew	0.41 [0.30, 0.56]	-0.88	0.16	-5.61	< .001
Model 3						$\chi^2$(3) = 65.18, $p$ = < .001
(Intercept)	0.53 [0.38, 0.71]	-0.64	0.16	-4.10	< .001
SexFemale	67.09 [26.71, 225.74]	4.21	0.53	7.92	< .001
Class2nd	0.31 [0.18, 0.52]	-1.17	0.27	-4.40	< .001
Class3rd	0.40 [0.27, 0.58]	-0.92	0.20	-4.72	< .001
ClassCrew	0.55 [0.39, 0.77]	-0.61	0.18	-3.43	.001
SexFemale:Class2nd	0.66 [0.17, 2.18]	-0.42	0.64	-0.65	.515
SexFemale:Class3rd	0.06 [0.02, 0.16]	-2.80	0.56	-4.98	< .001
SexFemale:ClassCrew	0.35 [0.07, 1.93]	-1.06	0.82	-1.29	.196
Model 4						$\chi^2$(1) = 20.34, $p$ = < .001
(Intercept)	1.46 [0.85, 2.49]	0.38	0.27	1.38	.166
SexFemale	68.93 [27.43, 232.02]	4.23	0.53	7.97	< .001
Class2nd	0.29 [0.17, 0.49]	-1.23	0.27	-4.58	< .001
Class3rd	0.36 [0.24, 0.53]	-1.02	0.20	-5.14	< .001
ClassCrew	0.56 [0.40, 0.80]	-0.57	0.18	-3.23	.001
AgeAdult	0.35 [0.22, 0.55]	-1.05	0.23	-4.57	< .001
SexFemale:Class2nd	0.64 [0.16, 2.13]	-0.45	0.65	-0.69	.488
SexFemale:Class3rd	0.06 [0.02, 0.16]	-2.86	0.56	-5.08	< .001
SexFemale:ClassCrew	0.34 [0.07, 1.87]	-1.09	0.82	-1.33	.185