MUVR2: MUVR2 with PLS and RF

In MUVR2: Multivariate Methods with Unbiased Variable Selection

MUVR2 with PLS and RF

Description

"Multivariate modelling with Unbiased Variable selection" using PLS and RF. Repeated double cross validation with tuning of variables in the inner loop.

Usage

MUVR2(
  X,
  Y,
  ID,
  scale = TRUE,
  nRep = 5,
  nOuter = 6,
  nInner,
  varRatio = 0.75,
  DA = FALSE,
  fitness = c("AUROC", "MISS", "BER", "RMSEP", "wBER", "wMISS"),
  method = c("PLS", " RF", "ANN", "SVM"),
  methParam,
  ML = FALSE,
  modReturn = FALSE,
  logg = FALSE,
  parallel = TRUE,
  weigh_added = FALSE,
  weighing_matrix = NULL,
  keep,
  ...
)

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)
scale	If TRUE, the predictor variable matrix is scaled to unit variance for PLS modeling.
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)
varRatio	Ratio of variables to include in subsequent inner loop iteration. (Defaults to 0.75)
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.)
method	Multivariate method. Supports 'PLS' and 'RF' (default)
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().
logg	Boolean for whether to sink model progressions to 'log.txt'
parallel	Boolean for whether to perform 'foreach' parallel processing (Requires a registered parallel backend; Defaults to 'TRUE')
weigh_added	To add a weighing matrix when it is classfication
weighing_matrix	The matrix used for get a miss classfication score
keep	Confounder variables can be added. NB: Variables (columns) must match column names.
...	additional argument

Value

A 'MUVR' object

Examples

data(freelive2)
nRep <- 2 # Number of MUVR2 repetitions
nOuter <- 3 # Number of outer cross-validation segments
varRatio <- 0.6 # Proportion of variables kept per iteration
method <- 'PLS' # Selected core modeling algorithm
regrModel <- MUVR2(X = XRVIP2,
                   Y = YR2,
                   nRep = nRep,
                   nOuter = nOuter,
                   varRatio = varRatio,
                   method = method,
                   modReturn = TRUE)

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