It allows the observations to be ordered by different methods (default percentile rank), and in consideration of domains grouping indicators.
Usage
vulnerability_index(
data,
direct = NULL,
inverse = NULL,
table = NULL,
weighted = TRUE,
domains = "default",
level = NULL,
ordered = TRUE,
method = "percent_rank",
complete = TRUE,
na.rm = FALSE
)Arguments
- data
A dataframe containing the variables that will be analyzed.
- direct
A vector indicating the name of the variables that contribute directly to the vulnerability index.
- inverse
A vector indicating the name of the variables that contribute inversely to the vulnerability index.
- table
A dataframe indicating the variables "Codename", "Direction", "Domain" and "Geographic_scale" as a minimum. If this argument is specified, it is no longer necessary to specify the direct and inverse arguments.
- weighted
A logical value indicating whether the "Domain" information is used for the index calculation.
- domains
A vector indicating the names of the domains grouping the variables of interest. If the argument "table" containing the variable "Domain" is used, only "default" is indicated in this argument.
- level
A vector indicating the level of analysis. It currently supports 3 values:"departamental", "provincial", and "distrital".
- ordered
A logical value indicating whether the cases analyzed will be ordered from highest to lowest.
- method
A vector indicating the analysis method used for the index. Currently only "percent_rank" is supported.
- complete
If true, the results table will show all the variables that the dataframe has in addition to the computations performed.
- na.rm
A logical value indicating whether cases where missing values are eliminated.
Value
A tibble containing the variables analyzed by the function.
Examples
if (FALSE) {
Sigma_Dom1 <- matrix(rep(c(1, runif(4, 0.65, 0.9)), 4),
4, 4, byrow = TRUE)
Sigma_Dom2 <- matrix(rep(c(1, runif(4, 0.65, 0.9)), 4),
4, 4, byrow = TRUE)
Dom1 <- as.data.frame(MASS::mvrnorm(100, rep(0, 4), Sigma_Dom1))
Dom2 <- as.data.frame(MASS::mvrnorm(100, rep(0, 4), Sigma_Dom2))
colnames(Dom2) <- paste0("V", 5:8)
df_example <- data.frame(distr = paste0("distr", 1:100))
df_example <- cbind(df_example, Dom1, Dom2)
table_var <- data.frame(
Codename = paste0("V", 1:8),
Direction = "Direct",
Domain = c(rep("Dom1", 4), rep("Dom2", 4)),
Geographic_scale = "Distrital"
)
vulnerability_index(df_example, table = table_var,
level = "distrital", na.rm = TRUE,
method = "pca")
vulnerability_index(df_example, table = table_var,
level = "distrital", na.rm = TRUE,
method = "percent_rank")
}