View source: R/certificate.of.exclusion.R
Certificate of exclusion from the selected variables set using SES or MMPC | R Documentation |
Information on why one ore more variables were not selected.
certificate.of.exclusion(xIndex, sesObject = NULL, mmpcObject = NULL) certificate.of.exclusion2(xIndex, mmpc2object)
xIndex |
A numerical vector with the indices of the predictor variables. |
sesObject |
If you ran SES, wald.ses or perm.ses, give the whole SES object here, otherwise leave it NULL. |
mmpcObject |
If you ran MMPC, wald.mmpc or prm.mmpc, give the whole MMPC object here, otherwise leave it NULL. |
mmpc2object |
If you ran mmpc2, give the whole MMPC object here. |
A list with the conditioning variables (if any), the test statistic and the logarithm of the p-value. In case a variable has been selected a message appears.
Michail Tsagris
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr
MMPC
set.seed(123) #simulate a dataset with continuous data dataset <- matrix(runif(100 * 100, 1, 100), ncol = 100) #define a simulated class variable target <- 3 * dataset[, 10] + 2 * dataset[, 100] + 3 * dataset[, 20] + rnorm(100, 0, 5) # define some simulated equivalences dataset[, 15] <- dataset[, 10] + rnorm(100, 0, 2) dataset[, 100] <- dataset[, 100] + rnorm(100, 0, 2) dataset[, 20] <- dataset[, 100] + rnorm(100, 0, 2) # run the SES algorithm mod1 <- SES(target, dataset, max_k = 5, threshold = 0.05, test = "testIndFisher", hash = TRUE, hashObject = NULL); mod2 <- MMPC(target, dataset, max_k = 5, threshold = 0.05, test = "testIndFisher", hash = TRUE, hashObject = NULL); certificate.of.exclusion(c(10, 15, 30, 45, 20), mod1) certificate.of.exclusion(c(10, 15, 30, 45, 20), NULL, mod2)
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