lpdaCV: Crossvalidation procedure for lpda evaluation

In lpda: Linear Programming Discriminant Analysis

Crossvalidation procedure for lpda evaluation

Description

lpdaCV evaluates the error rate classification with a crossvalidation procedure

Usage

lpdaCV(data, group, scale = FALSE, pca = FALSE, PC = 2, Variability = NULL,
                   CV = "ktest", ntest = 10, R = 10, f1 = NULL, f2 = NULL)
                   
## S3 method for class 'lpdaCV'
print(x, ...)

Arguments

data	Matrix containing data. Individuals in rows and variables in columns
group	Vector with the variable group
scale	Logical indicating if it is required standardize data.
pca	Logical indicating if a reduction of dimension is required
PC	Number of Principal Components (PC) for PCA. By default it is 2. When the number of PC is not decided, it can be determined choosing the desired proportion of explained variability (Variability parameter) or choosing the maximum number of errors allowed in the training set (Error.max).
Variability	Parameter for Principal Components (PC) selection. This is the desired proportion of variability explained for the PC of the variables.
CV	Crossvalidation mode: loo "leave one out" or ktest: that leaves k in the test set.
ntest	Number of samples to evaluate in the test-set.
R	Number of times that the error is evaluated.
f1	Vector with weights for individuals of the first group. If NULL they are equally weighted.
f2	Vector with weights for individuals of the second group. If NULL they are equally weighted.
x	An object of class "lpdaCV", a result of a call to lpdaCV or lpdaCV.3D.
...	Other arguments passed.

Value

lpdaCV returns the prediction error rate classification.

Author(s)

Maria Jose Nueda, [e-mail]

See Also

lpda

Examples

### RNAseq is a 3-dimensional array
  data(RNAseq)
  data = RNAseq[,,3]
  group = as.factor(rep(c("G1","G2"), each = 10))
  lpdaCV(data, group, pca = TRUE, CV = "ktest", ntest = 2)

  

lpda documentation built on April 4, 2025, 4:24 a.m.