lpda {lpda}

Computing discriminating hyperplane for two groups

Description

This function computes a discriminating hyperplane for two groups with original data (calling lpda.fit) or with principal components (calling lpda.pca)

Usage

lpda(data, group, scale = FALSE, pca = FALSE, PC = 2, Variability = NULL,
                 f1 = NULL, f2 = NULL)

Arguments

data

Matrix containing data. Individuals in rows and variables in columns

group

Vector with the variable group

scale

Logical indicating if data is standarised. When pca=TRUE data is always scaled.

pca

Logical indicating if Principal Components Analysis 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).

Variability

Parameter for Principal Components (PC) selection. This is the minimum desired proportion of variability explained for the PC of the variables. The analysis is always done with a minimum of 2 PCs. If it is NULL the PCA will be computed with PC parameter.

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.

Value

lpda returns an object of class "lpda".

The functionspredict 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.

coef

Hyperplane coefficients

data

Input data matrix when pca = FALSE and scores when pca = TRUE

group

Input group vector

scale

Input scale argument

pca

Input pca argument

loadings

Principal Components loadings. Showed when pca = TRUE

scores

Principal Components scores. Showed when pca = TRUE

var.exp

A matrix containing the explained variance for each component and the cumulative variance. Showed when pca = TRUE

PCs

Number of Principal Components in the analysis. Showed when pca = TRUE

Author(s)

Maria Jose Nueda, [e-mail]

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.pca, lpda.fit

Examples

######### palmdates example in lpda package:
data(palmdates)
group = as.factor( c(rep("Spanish",11),rep("Foreign",10)) )

# with concentration data:
model = lpda(data = palmdates$conc, group = group )
pred = predict(model)
table(pred$fitted, group)
plot(model, main = "Palmdates example")

model.pca = lpda(data = palmdates$conc, group = group, pca=TRUE, PC = 2)
plot(model.pca, PCscores = TRUE, main = "Palmdates example")

# with spectra data
model.pca = lpda(data = palmdates$spectra, group = group, pca=TRUE, Variability = 0.9)
model.pca$PCs # 4 PCs to explain 90% of the variability
plot(model.pca, PCscores = TRUE, main = "Spectra palmdates")