s.GLMNET: GLM with Elastic Net Regularization [C, R, S]

Description

Train an elastic net model

Usage

s.GLMNET(x, y = NULL, x.test = NULL, y.test = NULL, x.name = NULL,
  y.name = NULL, grid.resample.rtset = rtset.resample("kfold", 5),
  grid.search.type = c("exhaustive", "randomized"),
  grid.randomized.p = 0.1, intercept = TRUE, nway.interactions = 0,
  family = NULL, alpha = seq(0, 1, 0.2), lambda = NULL,
  nlambda = 100, which.cv.lambda = c("lambda.1se", "lambda.min"),
  penalty.factor = NULL, weights = NULL, ipw = TRUE, ipw.type = 2,
  upsample = FALSE, upsample.seed = NULL, res.summary.fn = mean,
  save.grid.run = FALSE, metric = NULL, maximize = NULL,
  .gs = FALSE, save.gs.mod = FALSE, n.cores = rtCores,
  print.plot = TRUE, plot.fitted = NULL, plot.predicted = NULL,
  plot.theme = getOption("rt.fit.theme", "lightgrid"), question = NULL,
  rtclass = NULL, verbose = TRUE, outdir = NULL,
  save.mod = ifelse(!is.null(outdir), TRUE, FALSE), ...)

Arguments

Numeric vector or matrix / data frame of features i.e. independent variables

Numeric vector of outcome, i.e. dependent variable

x.test

Numeric vector or matrix / data frame of testing set features Columns must correspond to columns in x

y.test

Numeric vector of testing set outcome

x.name

Character: Name for feature set

y.name

Character: Name for outcome

grid.resample.rtset

List: Output of rtset.resample defining gridSearchLearn parameters. Default = rtset.resample("kfold", 5)

grid.search.type

String: Type of grid search to perform: "exhaustive" or "randomized". Default = "exhaustive"

grid.randomized.p

Float (0, 1): If grid.search.type = "randomized", randomly run this proportion of combinations. Default = .1

intercept

Logical: If TRUE, include intercept in the model. Default = TRUE

nway.interactions

Integer: Include n-way interactions. This integer defined the n: formula = y ~^n

family

Error distribution and link function. See stats::family

alpha

Float [0, 1]: The elasticnet mixing parameter: a = 0 is the ridge penalty, a = 1 is the lasso penalty

lambda

Float vector: Best left to NULL, cv.glmnet will compute its own lambda sequence

weights

Numeric vector: Weights for cases. For classification, weights takes precedence over ipw, therefore set weights = NULL if using ipw. Note: If weight are provided, ipw is not used. Leave NULL if setting ipw = TRUE. Default = NULL

ipw

Logical: If TRUE, apply inverse probability weighting (for Classification only). Note: If weights are provided, ipw is not used. Default = TRUE

ipw.type

Integer 0, 1, 2 1: class.weights as in 0, divided by max(class.weights) 2: class.weights as in 0, divided by min(class.weights) Default = 2

upsample

Logical: If TRUE, upsample cases to balance outcome classes (for Classification only) Caution: upsample will randomly sample with replacement if the length of the majority class is more than double the length of the class you are upsampling, thereby introducing randomness

upsample.seed

Integer: If provided, will be used to set the seed during upsampling. Default = NULL (random seed)

res.summary.fn

Function: Used to average resample runs. Default = mean

metric

String: Metric to minimize, or maximize if maximize = TRUE during grid search. Default = NULL, which results in "Balanced Accuracy" for Classification, "MSE" for Regression, and "Coherence" for Survival Analysis.

maximize

Logical: If TRUE, metric will be maximized if grid search is run. Default = FALSE

n.cores

Integer: Number of cores to use. Defaults to available cores reported by future::availableCores(), unles option rt.cores is set at the time the library is loaded

print.plot

Logical: if TRUE, produce plot using mplot3 Takes precedence over plot.fitted and plot.predicted

plot.fitted

Logical: if TRUE, plot True (y) vs Fitted

plot.predicted

Logical: if TRUE, plot True (y.test) vs Predicted. Requires x.test and y.test

plot.theme

String: "zero", "dark", "box", "darkbox"

question

String: the question you are attempting to answer with this model, in plain language.

rtclass

String: Class type to use. "S3", "S4", "RC", "R6"

verbose

Logical: If TRUE, print summary to screen.

outdir

Path to output directory. If defined, will save Predicted vs. True plot, if available, as well as full model output, if save.mod is TRUE

save.mod

Logical. If TRUE, save all output as RDS file in outdir save.mod is TRUE by default if an outdir is defined. If set to TRUE, and no outdir is defined, outdir defaults to paste0("./s.", mod.name)

...

Additional arguments

Details

s.GLMNET runs glmnet::cv.glmnet for each value of alpha, for each resample in grid.resample.rtset. Mean values for min.lambda and MSE (Regression) or Accuracy (Classification) are aggregated for each alpha and resample combination

Description

Usage

Arguments

Details

See Also