Providing a unified interface for IBOSS, Lowcon, OSS and other popular design-based subsampling methods.
Installation
You can install the development version of dbsubsampling from GitHub with:
# install.packages("devtools")
devtools::install_github("JieYinStat/dbsubsampling")Example
This is a basic example which shows you how to get subsample index, such as uniform sampling, OSMAC, IBOSS, Leverage, OSS, LowCon, IES and DDS:
library(dbsubsampling)
data_binary <- data_binary_class
data_numeric <- data_numeric_regression
# Uniform sampling
subsampling(y_name = "y", data = data_binary, n = 10, method = "Unif", seed = 123)
#> [1] 2463 2511 8718 2986 1842 9334 3371 4761 6746 9819
# OSMAC-A
subsampling(y_name = "y", data = data_binary, n = 10, pilot_n = 100, method = "OSMAC_A",
seed_1 = 123, seed_2 = 456)
#> [1] 5684 1620 5372 8297 8863 9783 6483 6103 2702 5735
# OSMAC-L
subsampling(y_name = "y", data = data_binary, n = 10, pilot_n = 100, method = "OSMAC_L",
seed_1 = 123, seed_2 = 456)
#> [1] 5813 1681 5372 8313 8863 9780 1630 6103 2702 5888
# IBOSS
subsampling(y_name = "y", data = data_numeric, n = 100, method = "IBOSS")
#> [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
# Leverage
subsampling(y_name = "y", data = data_numeric, n = 10, method = "Leverage", replace = TRUE,
seed = 123, shrinkage = 0.9)
#> [1] 8534 2958 2986 9334 4761 9819 2757 9145 3194 2888
# OSS
subsampling(y_name = "y", data = data_numeric, n = 30, method = "OSS")
#> [1] 8841 8961 1902 7512 48 9867 6547 9784 3392 3622 5780 6594 1890 1850 8335
#> [16] 1254 6204 1257 4611 3831 4782 4919 1579 3404 718 7189 2060 4899 590 1800
# LowCon
subsampling(y_name = "y", data = data_numeric, n = 10, method = "LowCon", seed = 123, theta = 1)
#> [1] 6032 6633 4180 5093 6005 7093 3621 4429 1715 7143
# IES
subsampling(y_name = "y", data = data_numeric, n = 10, method = "IES", seed = 123, q = 16)
#> [1] 2876 7890 4440 9400 9813 2499 4939 8165 2224 4628
# DDS
subsampling(y_name = "y", data = data_numeric, n = 10, method = "DDS", ratio = 0.85)
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
#> [1,] 7010 3375 4172 9019 4983 85 9454 7745 9810 9737You can get more detailed examples from the article column on the website.