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

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.

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$summary
#>   start end cors associations num firstonly
#> a     1  10  0.0            0  10      TRUE
#> b    11  13  0.8            2   3      TRUE
##
##predictor data frame and binary response vector
##
pen.data <- x$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
  )
#> lambda= 6.875322 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -24.20415 
#> lambda= 11.1245 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -28.54894 
#> lambda= 4.249182 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -22.03816 
#> lambda= 2.626139 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -18.19819 
#> lambda= 1.623043 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -17.03062 
#> lambda= 0.8529987 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -18.12784 
#> lambda= 1.725982 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.01081 
#> lambda= 2.069811 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.55344 
#> lambda= 1.867201 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.60849 
#> lambda= 1.779923 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.57613 
#> lambda= 1.686663 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.547 
#> lambda= 1.746585 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.4307 
#> lambda= 1.718333 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.42749 
#> lambda= 1.732438 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.39798 
#> lambda= 1.725515 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.38869 
#> lambda= 1.728971 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.38343 
#> lambda= 1.727124 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.37896 
#> lambda= 1.726418 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.37797 
#> lambda= 1.725803 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.37783 
#> lambda= 1.726111 	1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950cvl= -16.37648 
cc <-
  output[which.max(output[, "cvl"]), -1:-3]  ##non-zero b.* are true positives