ddsimcares: Results of DD-SIMCA one-class classification

In mdatools: Multivariate Data Analysis for Chemometrics

Results of DD-SIMCA one-class classification

Description

@description ddsimcares is used to store results for DD-SIMCA one-class classification. Do not create this object manually, it will be created automatically by applying DD-SIMCA model.

Usage

ddsimcares(pcares, outcomes, classname, indices, numbers, alpha, c.ref = NULL)

Arguments

pcares	results of PCA decomposition of data (class pcares).
outcomes	outcomes of DD-SIMCA classification procedure.
classname	short text (up to 20 symbols) with class name.
indices	list with the object indices (members, strangers, unknown).
numbers	list with the object numbers in each subset (members, strangers, unknown).
alpha	significance level used for making the predictions.
c.ref	optional, vector with reference classes.

Details

Class ddsimcares inherits all properties and methods of class pcares, and has additional properties and functions for representing of classification results and other DD-SIMCA outcomes.

Do not create a ddsimcares object manually, it is created automatically when a DD-SIMCA model is developed (see ddsimca) or when the model is applied to a new data (see predict.ddsimca). The object can be used to show summary and plots for the results.

Value

Returns an object (list) of class ddsimcares with the same fields as pcares plus additional field simcares which is a list with all DD-SIMCA outcomes and related properties:

See Also

Methods specific for ddsimcares objects:

print.ddsimcares	shows information about the object.
summary.ddsimcares	shows statistics for results of classification.
as.data.frame.ddsimcares	converts DD-SIMCA results into data frame.
as.matrix.ddsimcares	converts summary of DD-SIMCA results into matrix.
writeCSV.ddsimcares	saves DD-SIMCA results into a CSV file.
plotAcceptance.ddsimcares	shows acceptance plot (q/q0 vs h/h0) with decision and outlier boundaries.
plotExtremes.ddsimcares	shows extremes plot.
plotAliens.ddsimcares	shows aliens plot.
plotDistances.ddsimcares	shows plot with individual distances (q, h or f).

Methods, inherited from ldecomp class:

plotScores.ldecomp	makes scores plot.
plotVariance.ldecomp	makes explained variance plot.
plotCumVariance.ldecomp	makes cumulative explained variance plot.

Check also ddsimca and pcares.

Examples

## make a DD-SIMCA model for Iris setosa class and show results for calibration set
library(mdatools)

data = iris[, 1:4]
class = iris[, 5]

# take every second of first 50 objects (setosa) as calibration set
se = data[seq(1, 50, by = 2), ]

# take the rest as test set
ind.test = c(seq(2, 50, by = 2), 51:150)
x.test = data[ind.test, ]
c.test = class[ind.test]

# make DD-SIMCA model and set optimal number of components to 1
model = ddsimca(se, 'setosa', scale = TRUE)
model = selectCompNum(model, 1)

# apply model to test set
r = predict(model, x.test, c.test)
print(r)

# show summary
summary(r)

# show plots
par(mfrow = c(2, 2))
plotAcceptance(r)
plotFoMs(r)
plotExtremes(r)
plotSelectivityArea(r)

mdatools documentation built on March 6, 2026, 5:08 p.m.