csmpv (version 1.0.5)

LASSO2_predict: Predict and Validate LASSO2 Model Scores

Description

This function predicts model scores, positive group probabilities, and risk scores for different outcome types based on a LASSO2 object and a new data set. It also performs validation of predictions when the true outcome variable is available.

Usage

LASSO2_predict(
  lassoObj,
  newdata = NULL,
  newY = FALSE,
  u = 2,
  outfile = "nameWithPath"
)

Value

A vector of predicted values is returned.

Arguments

lassoObj

A LASSO2 object.

newdata

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

newY

The outcome variable for the new data set.

u

A single numeric follow-up time for survival outcomes.

outfile

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

Author

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.

Harrell Jr F (2023). rms: Regression Modeling Strategies_. R package version 6.7-1, <https://CRAN.R-project.org/package=rms>

Harrell Jr F (2023). Hmisc: Harrell Miscellaneous_. R package version 5.1-1, <https://CRAN.R-project.org/package=Hmisc>

Examples

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

# 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:
# tl = LASSO2(data = tdat, biomks = Xvars,
#             outcomeType = "time-to-event",
#             time = "FFP..Years.",event = "Code.FFP",
#             outfile = paste0(temp_dir, "/survivalLASSO2"))
# To predict the model in a new data set:
# ptl = LASSO2_predict(tl, newdata = vdat,
#                     outfile = paste0(temp_dir, "/pred_LASSO2_time_to_event"))
# You might save the files to the directory you want.

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