| Title: | Average Attributable Fraction and Other Relevant Social Sciences Indicators |
| Version: | 1.3.0 |
| Description: | Compute Average Attributable Fraction (AAF) and produce a GT table with Odds.ratio, Average Marginal Effects and AAF, with confidence interval and p.value. Also compute other metrics such as Yule's Q and Cramer's V. For more details, see Fergusion and al. (2024) <doi:10.1007/s10654-024-01129-1>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.3 |
| Depends: | R (≥ 4.1.0) |
| Imports: | averisk, broom.helpers, dplyr, graphPAF (≥ 2.0.1), gt, psych, gtsummary, rcompanion, scales, stats, stringr, weights, questionr, katex, marginaleffects |
| Suggests: | knitr, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| URL: | https://github.com/Geminy3/SocialFacts, https://geminy3.github.io/SocialFacts/ |
| BugReports: | https://github.com/Geminy3/SocialFacts/issues |
| NeedsCompilation: | no |
| Packaged: | 2026-07-17 10:13:38 UTC; alioscha |
| Author: | Alioscha Massein 
[aut, cre, cph],
Brice Lefèvre
[aut] |
| Maintainer: | Alioscha Massein <alioscha.massein@cnrs.fr> |
| Repository: | CRAN |
| Date/Publication: | 2026-07-24 10:30:16 UTC |
get_cramer
Description
Get Cramer or YuleQ's tab
Usage
cramer_tab(var = NULL, metric = "cramer", data = NULL, weight = NULL)
Arguments
var |
A list of variable name you want to compute "metric" on |
metric |
Choose between 'Cramer' or 'YuleQ' parameter to select one of this metrics |
data |
The data table you want to compute 'metric' parameter on |
weight |
The weight vector if there is one in the dataframe |
Value
A list of gt information
Examples
data <- graphPAF::Hordaland_data
cramer_tab(c("y", "urban.rural"), metric = 'cramer', data = data)
get_AAF
Description
Get the AAF
Usage
get_AAF(
model = NULL,
nvar = NULL,
name_var = NULL,
vars_dep = NULL,
data = NULL,
nbootstrap = 50,
nperm = 100
)
Arguments
model |
The model you want to compute AAF on |
nvar |
You can choose this |
name_var |
A list of explanatory variables you want to compute AAF on |
vars_dep |
A string with the name of the variable you want to explain |
data |
The data you used to fit the model (with the variable in name_var and vars_dep) |
nbootstrap |
An integer with the n bootsrap replication you want. By default, 50. |
nperm |
Number of permutation between modalities and variable to compute the AAF |
Value
A sf table with the result of the AAF computation
Examples
set.seed(9876)
data <- data.frame(
"var_dep" = as.factor(sample(c(rep_len(c(0, 1), length.out = 1000)), size = 100)),
"var_1" = as.factor(sample(c(rep_len(c(1, 2, 3), length.out = 1000)), size = 100)),
"var_2" = as.factor(sample(c(rep_len(c(3, 1, 2), length.out = 100)), size = 100))
)
glmmodel <- glm(var_dep ~ var_1 + var_2,
data = data, family = binomial("logit"))
res <- get_AAF(model = glmmodel, nvar = 3, vars_dep = "var_dep", data = data,
nbootstrap = 5)
res <- get_AAF(model = glmmodel,
name_var = c("var_1", "var_2"),
vars_dep = "var_dep", data = data, nbootstrap = 5)
get_list
Description
Get a list with the names of the variables pass as parameter
Usage
get_list(name_var = NULL)
Arguments
name_var |
A list of variable you want to pass a name for a list |
Value
a list of zero named after the variable in name_var parameter
Examples
vec <- get_list(name_var = c("var1", "var2"))
Get Yule's Q
Description
Get Yule's Q
Usage
get_yuleq(
vars_dep = NULL,
name_var = c(),
data = NULL,
source = "",
alpha = 0.01,
weight = NULL
)
Arguments
vars_dep |
A string with name of the dependant variable to use as one variable for the 2x2 table |
name_var |
A vector of explanatory variable you want to compute Yule's Q on |
data |
A dataframe with the variable named before |
source |
A string with the name of the data source |
alpha |
A integer with the alpha value for fisher.test and IC |
weight |
A vector with weigths corresponding to the dataframe individuals weights |
Value
A gt table with the Yule's computed for the variable in name_var
Examples
data <- graphPAF::Hordaland_data
get_yuleq(vars_dep = "y", name_var = c("y", "urban.rural"),
data = data, source = "dataSource - Year",
alpha = 0.01)
result_tab
Description
Get the result table of OR, AME and AAF
Usage
result_tab(
model = NULL,
var_ref = NULL,
var_names = NULL,
res_AAF = NULL,
source = "",
data = data.frame()
)
Arguments
model |
The model used to perform statistical analysis |
var_ref |
The variable to explain in the statistical model |
var_names |
A list with the name of the variable you want to show on the contingency tables |
res_AAF |
the result of the AAF computation with |
source |
Data source information to add to the plots |
data |
The data used for the statistical analysis |
Value
A gt table
Examples
set.seed(9876)
data <- data.frame(
"var_dep" = as.factor(sample(c(rep_len(c(0, 1), length.out = 1000)), size = 100)),
"var_1" = as.factor(sample(c(rep_len(c(1, 2, 3), length.out = 1000)), size = 100)),
"var_2" = as.factor(sample(c(rep_len(c(3, 1, 2), length.out = 100)), size = 100))
)
glmmodel <- glm(var_dep ~ var_1 + var_2, data = data, family = binomial("logit"))
res <- get_AAF(model = glmmodel, nvar = 3, vars_dep = "var_dep", data = data,
nbootstrap = 2)
res_tab <- result_tab(model = glmmodel, var_ref = "var_dep",
var_names = c("var_1", "var_2"),
res_AAF = res$res, source = "TESTDATA", data = data)