MUVR2_EN: MUVR2 with EN

In MUVR2: Multivariate Methods with Unbiased Variable Selection

MUVR2 with EN

Description

"Multivariate modelling with Unbiased Variable selection" using Elastic Net (EN). Repeated double cross validation with tuning of variables using Elastic Net.

Usage

MUVR2_EN(
  X,
  Y,
  ID,
  alow = 1e-05,
  ahigh = 1,
  astep = 11,
  alog = TRUE,
  nRep = 5,
  nOuter = 6,
  nInner,
  NZV = TRUE,
  DA = FALSE,
  fitness = c("AUROC", "MISS", "BER", "RMSEP", "wBER", "wMISS"),
  methParam,
  ML = FALSE,
  modReturn = FALSE,
  parallel = TRUE,
  keep = NULL,
  weigh_added = FALSE,
  weighing_matrix = NULL,
  ...
)

Arguments

X	Predictor variables. NB: Variables (columns) must have names/unique identifiers. NAs not allowed in data. For multilevel, only the positive half of the difference matrix is specified.
Y	Response vector (Dependent variable). For classification, a factor (or character) variable should be used. For multilevel, Y is calculated automatically.
ID	Subject identifier (for sampling by subject; Assumption of independence if not specified)
alow	alpha tuning: lowest value of alpha
ahigh	alpha tuning: highest value of alpha
astep	alpha tuning: number of alphas to try from low to high
alog	alpha tuning: Whether to space tuning of alpha in logarithmic scale (TRUE; default) or normal/arithmetic scale (FALSE)
nRep	Number of repetitions of double CV. (Defaults to 5)
nOuter	Number of outer CV loop segments. (Defaults to 6)
nInner	Number of inner CV loop segments. (Defaults to nOuter-1)
NZV	Boolean for whether to filter out near zero variance variables (defaults to TRUE)
DA	Boolean for Classification (discriminant analysis) (By default, if Y is numeric -> DA=FALSE. If Y is factor (or character) -> DA=TRUE)
fitness	Fitness function for model tuning (choose either 'AUROC' or 'MISS' (default) for classification; or 'RMSEP' (default) for regression.)
methParam	List with parameter settings for specified MV method (see function code for details)
ML	Boolean for multilevel analysis (defaults to FALSE)
modReturn	Boolean for returning outer segment models (defaults to FALSE). Setting modReturn=TRUE is required for making MUVR predictions using predMV().
parallel	Boolean for whether to perform 'foreach' parallel processing (Requires a registered parallel backend; Defaults to 'TRUE')
keep	A group of confounders that you want to manually set as non-zero
weigh_added	weigh_added
weighing_matrix	weighing_matrix
...	Pass additional arguments

Value

A MUVR object

Examples

data("freelive2")
nRep <- 2 # Number of MUVR2 repetitions
nOuter <- 4 # Number of outer cross-validation segments
regrModel <- MUVR2_EN(X = XRVIP2,
                      Y = YR2,
                      nRep = nRep,
                      nOuter = nOuter,
                      modReturn = TRUE)

MUVR2 documentation built on Sept. 16, 2024, 9:06 a.m.