LASSO_plus_XGBtraining {csmpv}

LASSO_plus_XGBtraining: Variable Selection and XGBoost Modeling

Description

This function performs variable selection using LASSO_plus, followed by building an XGBoost model. LASSO_plus is a method for variable selection, and XGBoost is a gradient boosting algorithm for modeling.

Usage

LASSO_plus_XGBtraining(
  data = NULL,
  standardization = FALSE,
  columnWise = TRUE,
  biomks = NULL,
  outcomeType = c("binary", "continuous", "time-to-event"),
  Y = NULL,
  time = NULL,
  event = NULL,
  topN = 10,
  nrounds = 5,
  nthread = 2,
  gamma = 1,
  max_depth = 3,
  eta = 0.3,
  outfile = "nameWithPath",
  height = 6
)

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 is to do 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 "data" column names.

outcomeType

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

Y

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

time

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

event

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

topN

An integer to indicate how many variables we intend to select.

nrounds

Max number of boosting iterations.

nthread

Number of parallel threads used to run XGBoost.

gamma

Minimum loss reduction required to make a further partition on a leaf node of the tree.

max_depth

Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit.

eta

The learning rate for the XGBoost model.

outfile

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

height

An integer to indicate the forest plot height in inches.

Value

A list is returned:

XGBoost_model

An XGBoost model

XGBoost_model_score

Model scores for the given training data set. For a continuous outcome variable, this is a vector of the estimated continuous values; for a binary outcome variable, this is a vector representing the probability of the positive class; for time-to-event outcome, this is a vector of risk scores

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.

Tianqi Chen and Carlos Guestrin, "XGBoost: A Scalable Tree Boosting System", 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016, https://arxiv.org/abs/1603.02754

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 continuous as an example:
clpxfit = LASSO_plus_XGBtraining(data = tdat, biomks = Xvars,
          outcomeType = "continuous", Y = "Age", topN = 5,
          outfile = paste0(temp_dir, "/continuous_LASSO_plus_XGBoost"))
# You might save the files to the directory you want.

# To delete the "temp_dir", use the following:
unlink(temp_dir)