MUVR2 (version 0.1.0)

MUVR2_EN: 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,
  ...
)

Value

A MUVR object

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

Examples

# \donttest{
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)
# }