lpda (version 1.2.0)

lpda.3D: Classification with lpda for 3way array data

Description

This function applies lpda methodology to classify individuals in two or more groups with original data (by applying lpda through the third dimension) or by applying lpda to the parafac scores.

Usage

lpda.3D(data, group, scale = FALSE,  pfac = FALSE, nfac = 2,
                     nstart = 10, seed=2, f1 = NULL, f2 = NULL)
# S3 method for lpda.3D
print(x, ...)

Value

lpda.3D returns an object of class "lpda.3D".

MOD

When pfac=FALSE, MOD is a list of lpda objects obtained from the application of lpda to each slide matrix of the third node. When pfac=TRUE, MOD is a list of two elements: (1) mod.pfac the parafac model obtained with Multiway package and (2) the lpda object, obtained from the application of lpda to the parafac scores.

data

Input array data

group

Input group vector

pfac

Input pfac argument

The functions predict and plot can be used to obtain the predicted classes and a plot in two dimensions with the distances to the computed hyperplane for the two classes.

Arguments

data

Array containing data. Individuals in the first mode, variables in the second mode and third mode with time or similar.

group

Vector with the variable group.

scale

Logical indicating if it is required standardize data.

pfac

Logical indicating if Parafac Analysis is required

nfac

Number of factors for Parafac Analysis.

nstart

Number of random starts for multiway analysis.

seed

A single value to reproduce same results in multiway methods. If NULL the start will be random.

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 "lpda.3D", a result of a call to lpda.3D.

...

Other arguments passed.

Author

Maria Jose Nueda,

References

Nueda MJ, Gandía C, Molina MD (2022) LPDA: A new classification method based on linear programming. PLoS ONE 17(7): e0270403. <https://doi.org/10.1371/journal.pone.0270403>

See Also

lpda, parafac

Examples

### RNAseq is a 3-dimensional array
  data(RNAseq)
  group = as.factor(rep(c("G1","G2"), each = 10))

## Strategy 1
  model3D = lpda.3D(RNAseq, group)
  summary(model3D)
  predict(model3D)
  plot(model3D, mfrow=c(2,2))

## Strategy 2: with parafac
  model3Ds2 = lpda.3D(RNAseq, group, pfac=TRUE, nfac=2)
  model3Ds2$MOD$mod.pfac$Rsq
  predict(model3Ds2)
  summary(model3Ds2)
  plot(model3Ds2, pfacscores=FALSE, main="Parafac Model", mfrow=c(1,1))
  plot(model3Ds2, pfacscores=TRUE, cex=1.5, main="Parafac components")
  legend("bottomright", levels(group), col=c(2,3), pch=20)