Description Usage Arguments Details Value Author(s) References Examples
Estimation of error variance using Bootstrap-refitted cross validation method in ultrahigh dimensional dataset.
1 |
x |
a matrix of markers or explanatory variables, each column contains one marker and each row represents an individual. |
y |
a column vector of response variable. |
a |
value of alpha, range is 0<=a<=1 where, a=1 is LASSO penalty and a=0 is Ridge penalty.If variable selection method is LASSO then providing value to a is compulsory. For other methods a should be NULL. |
b |
number of bootstrap samples. |
d |
number of variables to be selected from x. |
method |
variable selection method, user can choose any method among "spam", "lasso", "lsr" |
In this method, bootstrap samples are taken from the original datasets and then RCV (Fan et al., 2012) method is applied to each of these bootstrap samples.
Error variance |
Sayanti Guha Majumdar <sayanti23gm@gmail.com>, Anil Rai, Dwijesh Chandra Mishra
Fan, J., Guo, S., Hao, N. (2012).Variance estimation using refitted cross-validation in ultrahigh dimensional regression. Journal of the Royal Statistical Society, 74(1), 37-65
Ravikumar, P., Lafferty, J., Liu, H. and Wasserman, L. (2009). Sparse additive models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 71(5), 1009-1030
Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of Royal Statistical Society, 58, 267-288
1 2 3 4 5 6 7 8 9 10 11 12 | ## data simulation
marker <- as.data.frame(matrix(NA, ncol =500, nrow = 200))
for(i in 1:500){
marker[i] <- sample(1:3, 200, replace = TRUE, prob = c(1, 2, 1))
}
pheno <- marker[,1]*1.41+marker[,2]*1.41+marker[,3]*1.41+marker[,4]*1.41+marker[,5]*1.41
pheno <- as.matrix(pheno)
marker<- as.matrix(marker)
## estimation of error variance
var <- bsrcv(marker,pheno,1,10,5,"lasso")
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