get_AAF

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 integer parameter if you prefer to add explanatory variables in the order they appear in the model. You don't have to use name_var parameter.

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"))
# \donttest{
res <- get_AAF(model = glmmodel, nvar = 3, vars_dep = "var_dep", data = data,
               nbootstrap = 5)
#> Using random variable
#> COMPUTE AAF
res <- get_AAF(model = glmmodel,
               name_var = c("var_1", "var_2"),
              vars_dep = "var_dep", data = data, nbootstrap = 5)
#> Using named variable
#> COMPUTE AAF
# }