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Information-Based Optimal Subdata Selection for Big Data Linear Regression (IBOSS, Rcpp-c++-style by Wang)

Source: R/IBOSS.R
IBOSS.Rd

A subsampling method based on D-optiaml criterion inspired by optimal experimental design used for linear regression.

Usage

IBOSS(n, X)

Arguments

n

Subsample size.

X

A data.frame or matrix consists of explanatory variables.

Value

Subsample index.

References

HaiYing Wang, Min Yang & John Stufken (2019) Information-Based Optimal Subdata Selection for Big Data Linear Regression, Journal of the American Statistical Association, 114:525, 393-405, https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1408468, https://github.com/Ossifragus/IBOSS.

Examples

data <- data_numeric_regression
X <- data[-which(names(data) == "y")]
IBOSS(n = 100, X = X)
#>  [1]  183  226  395  419  584  666  711  758 1027 1144 1324 1445 1940 1946 1978
#> [16] 2018 2673 2982 3190 3395 3484 3612 3632 3638 3696 3816 3835 3896 3921 4256
#> [31] 4312 4405 4523 4551 4729 4938 5121 5226 5342 5410 5679 5770 5995 6089 6163
#> [46] 6170 6203 6250 6525 6964 6979 7053 7198 7407 7564 7633 7915 7935 7967 7992
#> [61] 8026 8088 8106 8156 8161 8267 8306 8501 8503 8521 8534 8694 8805 8841 9117
#> [76] 9211 9302 9364 9398 9456 9676 9946 9971 9989 1173 2344 5394 8438 8567 9239
#> [91] 1787 2104 2215 3121 7159 9133