plsRcox (version 1.7.7)

Xmicro.censure_compl_imp: Imputed Microsat features

Description

This dataset provides imputed microsat specifications. Imputations were computed using Multivariate Imputation by Chained Equations (MICE) using predictive mean matching for the numeric columns, logistic regression imputation for the binary data or the factors with 2 levels and polytomous regression imputation for categorical data i.e. factors with three or more levels.

Format

A data frame with 117 observations on the following 40 variables.

D18S61

a numeric vector

D17S794

a numeric vector

D13S173

a numeric vector

D20S107

a numeric vector

TP53

a numeric vector

D9S171

a numeric vector

D8S264

a numeric vector

D5S346

a numeric vector

D22S928

a numeric vector

D18S53

a numeric vector

D1S225

a numeric vector

D3S1282

a numeric vector

D15S127

a numeric vector

D1S305

a numeric vector

D1S207

a numeric vector

D2S138

a numeric vector

D16S422

a numeric vector

D9S179

a numeric vector

D10S191

a numeric vector

D4S394

a numeric vector

D1S197

a numeric vector

D6S264

a numeric vector

D14S65

a numeric vector

D17S790

a numeric vector

D5S430

a numeric vector

D3S1283

a numeric vector

D4S414

a numeric vector

D8S283

a numeric vector

D11S916

a numeric vector

D2S159

a numeric vector

D16S408

a numeric vector

D6S275

a numeric vector

D10S192

a numeric vector

sexe

a numeric vector

Agediag

a numeric vector

Siege

a numeric vector

T

a numeric vector

N

a numeric vector

M

a numeric vector

STADE

a factor with levels 0 1 2 3 4

References

plsRcox, Cox-Models in a high dimensional setting in R, Frederic Bertrand, Philippe Bastien, Nicolas Meyer and Myriam Maumy-Bertrand (2014). Proceedings of User2014!, Los Angeles, page 152.

Deviance residuals-based sparse PLS and sparse kernel PLS regression for censored data, Philippe Bastien, Frederic Bertrand, Nicolas Meyer and Myriam Maumy-Bertrand (2015), Bioinformatics, 31(3):397-404, doi:10.1093/bioinformatics/btu660.

Examples

# \donttest{
data(Xmicro.censure_compl_imp)
X_train_micro <- Xmicro.censure_compl_imp[1:80,]
X_test_micro <- Xmicro.censure_compl_imp[81:117,]
rm(X_train_micro,X_test_micro)
# }