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SocialFacts brings a tool to produce easily multiple indicators
commonly used in social sciences : - Odds Ratio, with p.value and CI -
Average Marginal Effects, with CI - A cramer’s V table, to check
correlation between your categorical variables - A gt table with Yule’s
Q value compute against the vars_dep argument. - A new tool
called Average Attributable Fraction, described by Ferguson and al. here
: We use the original graphPAF library, and facilitate it’s
use to produce a table which resume all of this tools in one place.
You can install the development version of SocialFacts like so:
devtools::install_github("Geminy3/SocialFacts")of install it via CRAN :
install.packages("SocialFacts")This is a basic example which shows you how to solve a common problem:
library(SocialFacts)
## basic example code
### Fit a logistic model
model <- glm(var_dep ~ some + var + you + have, data = data, family = binomial(...))
### You can check the correlation between your categorical data
get_cramer(var = c("var_1", "var_2"), metric = 'cramer', data = data)
### To compute and store the AAF
resAAF <- get_AAF(model = model, nvar = 3, vars_dep = "var_dep", data = data)
### To get the summary table
resume_tab <- result_tab(result_tab(model = model, var_ref = "var_dep", var_names = c("var_1", "var_2"),
res_AAF = resAAF$res, data = data)
### To view the table
resume_tab
### To export the table
resume_tab |>
gtsave(filename = "~/the/path/to/store/it/TAB_RECAP.png", expand = 10)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.
Health stats visible at Monitor.