rdCV | R Documentation |
Wrapper for repeated double cross-validation without variable selection
rdCV(
X,
Y,
ID,
nRep = 5,
nOuter = 6,
nInner,
DA = FALSE,
fitness = c("AUROC", "MISS", "RMSEP", "BER"),
method = c("PLS", "RF"),
methParam,
ML = FALSE,
modReturn = FALSE,
logg = FALSE
)
X |
Independent variables. NB: Variables (columns) must have names/unique identifiers. NAs not allowed in data. For ML, X is upper half only (X1-X2) |
Y |
Response vector (Dependent variable). For DA (classification), Y should be factor or character. For ML, Y is omitted. For regression, Y is numeric. |
ID |
Subject identifier (for sampling by subject; Assumption of independence if not specified) |
nRep |
Number of repetitions of double CV. |
nOuter |
Number of outer CV loop segments. |
nInner |
Number of inner CV loop segments. |
DA |
Logical for Classification (discriminant analysis) (Defaults do FALSE, i.e. regression). PLS is limited to two-class problems (see 'Y' above). |
fitness |
Fitness function for model tuning (choose either 'AUROC' or 'MISS'or 'BER' for classification; or 'RMSEP' (default) for regression.) |
method |
Multivariate method. Supports 'PLS' and 'RF' (default) |
methParam |
List with parameter settings for specified MV method (defaults to ???) |
ML |
Logical for multilevel analysis (defaults to FALSE) |
modReturn |
Logical for returning outer segment models (defaults to FALSE) |
logg |
Logical for whether to sink model progressions to 'log.txt' |
An object containing stuff...
data("freelive2")
nRep <- 2 # Number of MUVR2 repetitions
nOuter <- 4 # Number of outer cross-validation segments
varRatio <- 0.75 # Proportion of variables kept per iteration
method <- 'RF' # Selected core modeling algorithm
regrModel <- rdCV(X = XRVIP2,
Y = YR2,
nRep = nRep,
nOuter = nOuter,
method = method,
modReturn=TRUE)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.