pensim-package {pensim}

Functions and data for simulation of high-dimensional data and parallelized repeated penalized regression

Description

Simulation of continuous, correlated high-dimensional data with time-to-event or binary response, and parallelized functions for Lasso, Ridge, and Elastic Net penalized regression model training and validation by split-sample or nested cross-validation. See the help page for opt.nested.crossval() for the most extensive usage examples.

Details

Package:	pensim
Type:	Package
License:	GPL (>=2)
LazyLoad:	yes

Model training and validation by Lasso, Ridge, and Elastic Net penalized regression. This package also contains a function for simulation of correlated high-dimensional data with binary or time-to-event response.

Author(s)

Levi Waldron

Maintainer: Levi Waldron <[e-mail]>

References

Waldron L, Pintilie M, Tsao M-S, Shepherd FA, Huttenhower C*, Jurisica I*: Optimized application of penalized regression methods to diverse genomic data. Bioinformatics 2011, 27:3399-3406. (*equal contribution)

See Also

penalized-package

Examples

set.seed(9)
## 
## create some data,  with one of a group of five correlated variables
## having an association with the binary outcome:
## 
x <- create.data(
  nvars = c(10, 3),
  cors = c(0, 0.8),
  associations = c(0, 2),
  firstonly = c(TRUE, TRUE),
  nsamples = 50,
  response = "binary",
  logisticintercept = 0.5
)
x$summary
##
##predictor data frame and binary response vector
##
pen.data <- x$data[, -match("outcome", colnames(x$data))]
response <- x$data[, match("outcome", colnames(x$data))]
## lasso regression.  Note that epsilon=1e-2 is passed onto optL1,  and
## reduces the precision of the tuning compared to the default 1e-10.
output <-
  opt1D(
    nsim = 1,
    nprocessors = 1,
    penalized = pen.data,
    response = response,
    epsilon = 1e-2
  )
cc <-
  output[which.max(output[, "cvl"]), -1:-3]  ##non-zero b.* are true positives