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Getting Started with Report Creation

Introduction

This vignette explains how to make publication-grade DAGassist reports in LaTeX, Word, Excel, and plaintext. Since this package’s primary utility is as a robustness check for observational studies, most of its functionality revolves around creating reports.

Install

install.packages("pak")
pak::pak("grahamgoff/DAGassist")

Setup

In addition to loading DAGassist, we will load the following baseline packages:

modelsummary to build the model comparison table for LaTeX, Word, Excel, and plaintext.
- LaTeX uses broom as a fallback for report generation
knitr to build intermediate .md for Word and plaintext report generation.
rmarkdown to convert .md files to .docx files for Word report generation.
writexl to export Excel files.

Essentially, to export:

LaTeX only needs modelsummary
Excel needs modelsummary and writexl
plaintext needs modelsummary and knitr
Word needs modelsummary, knitr, and rmarkdown

#load DAGassist
library(DAGassist) 
#load libraries to help export
library(modelsummary)
library(writexl)
library(knitr)
library(rmarkdown)

Example DAG

The data, which simulates the DAG relationships:

head(df)
#>            Y          D          H          A           G          F
#> 1 -4.3598990 -1.7293707 -1.2908129 -0.3906503 -0.15030748 -0.5116037
#> 2  1.6129050  1.0748492  0.2956294  0.6771872 -0.32775713  0.2369379
#> 3  1.5096451  1.0922811 -1.1613337  1.1254048 -1.44816529 -0.5415892
#> 4  1.1554071 -0.5867292  1.3752785 -0.5173670 -0.69728458  1.2192276
#> 5  0.2920729 -2.0225129 -1.1232830 -1.8569842  2.59849023  0.1741359
#> 6  3.1428430  3.0900447  1.1675898  2.6936976 -0.03741501 -0.6152683
#>             C           B
#> 1 -0.99579872 -0.56047565
#> 2 -1.03995504 -0.23017749
#> 3 -0.01798024  1.55870831
#> 4 -0.13217513  0.07050839
#> 5 -2.54934277  0.12928774
#> 6  1.04057346  1.71506499

Creating a report

Suppose you wrote an article uses model original as its main finding, and you want to ensure that your specification captures the total effect of D on Y, without mediator or collider bias.

original <- lm(Y ~ D + G + H + F + A + B + C, data = df)

DAGassist can generate a report assessing the causal roles of the variables in original.

DAGassist(dag = dag_model, #specify a dagitty or ggdag object
          formula = lm(Y ~ D + G + H + F + A + B + C, data = df), #provide your formula
          type = "text", #output type
          out = out_txt) #a temporary directory, for the purpose of this vignette

cat(readLines(out_txt), sep = "\n") # show the output

DAGassist Report:

Roles

Variable	Role	`X`	`Y`	`CON`
A	confounder			x
B	confounder			x
C	confounder			x
D	exposure	x
F	confounder			x
G	other
H	other
Y	outcome		x

Models

Term	Original	Minimal 1	Minimal 2	Minimal 3	Minimal 4	Canonical
D	1.034***	0.977***	0.969***	1.038***	1.024***	1.034***
	(0.032)	(0.020)	(0.023)	(0.036)	(0.041)	(0.032)
G	0.397***					0.397***
	(0.032)					(0.032)
H	0.486***	0.522***		0.495***		0.486***
	(0.032)	(0.028)		(0.031)		(0.032)
F	0.618***	0.606***	0.593***			0.618***
	(0.037)	(0.036)	(0.040)			(0.037)
A	-0.111**			0.158***	0.169***	-0.111**
	(0.043)			(0.047)	(0.051)	(0.043)
B	-0.008			-0.161***	-0.162***	-0.008
	(0.039)			(0.045)	(0.049)	(0.039)
C	0.037		0.420***		0.391***	0.037
	(0.042)		(0.040)		(0.045)	(0.042)
Num.Obs.	1000	1000	1000	1000	1000	1000
R2	0.836	0.809	0.769	0.762	0.724	0.836

Notes

p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001. Controls (minimal): {F, H} Controls (canonical): {A, B, C, F, G, H}.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.