LASSO2_reg {csmpv}

LASSO2 Variable Selection and Regular Regression Modeling

Description

This function performs variable selection with LASSO2 but ignores the shrunk LASSO coefficients and builds a regular regression model.

Usage

LASSO2_reg(
  data = NULL,
  standardization = FALSE,
  columnWise = TRUE,
  biomks = NULL,
  outcomeType = c("binary", "continuous", "time-to-event"),
  Y = NULL,
  time = NULL,
  event = NULL,
  nfolds = 10,
  outfile = "nameWithPath"
)

Arguments

data

A data matrix or a data frame where samples are in rows, and features/traits are in columns.

standardization

A logical variable to indicate if standardization is needed before variable selection. The default is FALSE.

columnWise

A logical variable to indicate if column-wise or row-wise normalization is needed. The default is TRUE, which performs column-wise normalization. This is only meaningful when "standardization" is TRUE.

biomks

A vector of potential biomarkers for variable selection. They should be a subset of column names in "data".

outcomeType

The outcome variable type. There are three choices: "binary" (default), "continuous", and "time-to-event".

Y

The outcome variable name when the outcome type is either "binary" or "continuous".

time

The time variable name when the outcome type is "time-to-event".

event

The event variable name when the outcome type is "time-to-event".

nfolds

The number of folds for cross-validation. The default value is 10.

outfile

A string for the output file including the path, if necessary, but without the file type extension.

Details

The first part of LASSO2_reg involves variable selection with LASSO2, typically based on the mean lambda.1se from 10 iterations of n-fold cross-validation-based LASSO regression. In each iteration, a lambda.1se refers to the largest value of lambda such that the error is within 1 standard error of the minimum. However, if only one or no variable is selected, the cross-validation results are ignored, and a minimum of two remaining variables is ensured through full-data lambda simulations. The second part of LASSO2_reg involves ignoring the shrunk LASSO coefficients and building a regular regression model. It is suitable for three types of outcomes: continuous, binary, and time-to-event.

Value

A list is returned with the same output as from confirmVars.

fit

A model with selected variables for the given outcome variable.

allplot

A list with all plots.

Author(s)

Aixiang Jiang

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent (2010), Journal of Statistical Software, Vol. 33(1), 1-22, doi:10.18637/jss.v033.i01.

Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent, Journal of Statistical Software, Vol. 39(5), 1-13, doi:10.18637/jss.v039.i05.

Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

Therneau, T., Grambsch, P., Modeling Survival Data: Extending the Cox Model. Springer-Verlag, 2000.

Kassambara A, Kosinski M, Biecek P (2021). survminer: Drawing Survival Curves using 'ggplot2'_. R package version 0.4.9,

Examples

# Load in data sets:
data("datlist", package = "csmpv")
tdat = datlist$training

# The function saves files locally. You can define your own temporary directory. 
# If not, tempdir() can be used to get the system's temporary directory.
temp_dir = tempdir()
# As an example, let's define Xvars, which will be used later:
Xvars = c("highIPI", "B.Symptoms", "MYC.IHC", "BCL2.IHC", "CD10.IHC", "BCL6.IHC")

# The function can work with three different outcome types. 
# Here, we use time-to-event as an example:
# tlr = LASSO2_reg(data = tdat, biomks = Xvars,
#                  outcomeType = "time-to-event",
#                  time = "FFP..Years.",event = "Code.FFP",
#                  outfile = paste0(temp_dir, "/survivalLASSO2_reg"))
# You might save the files to the directory you want.
# To delete the "temp_dir", use the following:
unlink(temp_dir)