| AQI {CASMI} | R Documentation |
AQI Index
Description
A quantitative measure of dataset quality. The AQI Index score indicates the degree that how features are associated with the outcome in a dataset. (synonyms of "feature": "variable" "factor" "attribute")
For more information, please refer to the corresponding publication: Shi, J., Zhang, J. and Ge, Y. (2019), "An Association-Based Intrinsic Quality Index for Healthcare Dataset Ranking" <doi:10.1109/ICHI.2019.8904553>
Usage
AQI(data, alpha.filter = 0.2)
Arguments
data |
data frame (features as columns and observations as rows). The outcome variable (Y) MUST be the last column. It requires at least one features and only one outcome. Both the features (Xs) and the outcome (Y) MUST be discrete (if not naturally discrete, you may try the 'autoBin.binary' function in the same package). |
alpha.filter |
level of significance for the mutual information test of independence in step 2 (<doi:10.1109/ICHI.2019.8904553>). By default, 'alpha.filter = 0.2'. |
Value
The AQI Index score.
Examples
## Generate a toy dataset: "data"
n <- 10000
set.seed(1)
x1 <- rbinom(n, 3, 0.5) + 0.2
set.seed(2)
x2 <- rbinom(n, 2, 0.8) + 0.5
set.seed(3)
x3 <- rbinom(n, 5, 0.3)
set.seed(4)
error <- round(runif(n, min=-1, max=1))
y <- x1 + x3 + error
data <- data.frame(cbind(x1, x2, x3, y))
colnames(data) <- c("feature1", "feature2", "feature3", "Y")
## Calculate the AQI score of "data"
AQI(data)