mdatools (version 0.9.1)

plsdares: PLS-DA results

Description

plsdares is used to store and visualize results of applying a PLS-DA model to a new data.

Usage

plsdares(plsres, cres)

Arguments

plsres

PLS results for the data.

cres

Classification results for the data.

Value

Returns an object of plsdares class with fields, inherited from classres and plsres.

Details

Do not use plsdares manually, the object is created automatically when one applies a PLS-DA model to a new data set, e.g. when calibrate and validate a PLS-DA model (all calibration and validation results in PLS-DA model are stored as objects of plsdares class) or use function predict.plsda.

The object gives access to all PLS-DA results as well as to the plotting methods for visualisation of the results. The plsidares class also inherits all properties and methods of classres and plsres classes.

If no reference values provided, classification statistics will not be calculated and performance plots will not be available.

See Also

Methods for plsda objects:

print.plsda	shows information about the object.
summary.plsda	shows statistics for results of classification.
plot.plsda	shows plots for overview of the results.

Methods, inherited from classres class:

showPredictions.classres	show table with predicted values.
plotPredictions.classres	makes plot with predicted values.
plotSensitivity.classres	makes plot with sensitivity vs. components values.
plotSpecificity.classres	makes plot with specificity vs. components values.
plotPerformance.classres	makes plot with both specificity and sensitivity values.

Methods, inherited from plsres class:

plotPredictions.plsres	shows predicted vs. measured plot.
plotXScores.plsres	shows scores plot for x decomposition.
plotXYScores.plsres	shows scores plot for x and y decomposition.
plotRMSE.regres	shows RMSE plot.
plotXVariance.plsres	shows explained variance plot for x decomposition.
plotYVariance.plsres	shows explained variance plot for y decomposition.
plotXCumVariance.plsres	shows cumulative explained variance plot for y decomposition.
plotYCumVariance.plsres	shows cumulative explained variance plot for y decomposition.
plotXResiduals.plsres	shows T2 vs. Q plot for x decomposition.
plotYResiduals.regres	shows residuals plot for y values.

See also plsda - a class for PLS-DA models, predict.plsda applying PLS-DA model for a new dataset.

Examples

# NOT RUN {
### Examples for PLS-DA results class

library(mdatools)

## 1. Make a PLS-DA model with full cross-validation, get
## calibration results and show overview

# make a calibration set from iris data (3 classes)
# use names of classes as class vector
x.cal = iris[seq(1, nrow(iris), 2), 1:4] 
c.cal = iris[seq(1, nrow(iris), 2), 5]

model = plsda(x.cal, c.cal, ncomp = 3, cv = 1, info = 'IRIS data example')
model = selectCompNum(model, 1)

res = model$calres

# show summary and basic plots for calibration results
summary(res)
plot(res)

## 2. Apply the calibrated PLS-DA model to a new dataset

# make a new data
x.new = iris[seq(2, nrow(iris), 2), 1:4] 
c.new = iris[seq(2, nrow(iris), 2), 5]

res = predict(model, x.new, c.new)
summary(res)
plot(res)

## 3. Show performance plots for the results
par(mfrow = c(2, 2))
plotSpecificity(res)
plotSensitivity(res)
plotMisclassified(res)
plotMisclassified(res, nc = 2)
par(mfrow = c(1, 1))

## 3. Show both class and y values predictions
par(mfrow = c(2, 2))
plotPredictions(res)
plotPredictions(res, ncomp = 2, nc = 2)
plotPredictions(structure(res, class = "plsres"))
plotPredictions(structure(res, class = "plsres"), ncomp = 2, ny = 2)
par(mfrow = c(1, 1))

## 4. All plots from ordinary PLS results can be used, e.g.:
par(mfrow = c(2, 2))
plotXYScores(res)
plotYVariance(res, type = 'h')
plotXVariance(res, type = 'h')
plotXResiduals(res)
par(mfrow = c(1, 1))

# }