Type: Package
Title: Generalized Spline Mixed Effect Models for Longitudinal Breath Data
Version: 1.0
Date: 2022-03-07
Description: Automated analysis and modeling of longitudinal 'omics' data (e.g. breath 'metabolomics') using generalized spline mixed effect models. Including automated filtering of noise parameters and determination of breakpoints.
Depends: R (≥ 3.5.0)
LazyData: true
License: GPL-3
RoxygenNote: 7.1.2
Encoding: UTF-8
Imports: methods, lawstat, nlme, graphics, RColorBrewer, stats, base, grDevices, qpdf
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-03-15 21:43:06 UTC; gittakirana
Author: Anne-Christin Hauschild ORCID iD [aut, cre], Sandy P. Eckel [ths, com], Jan Baumbach [ths]
Maintainer: Anne-Christin Hauschild <achauschild@googlemail.com>
Repository: CRAN
Date/Publication: 2022-03-21 18:40:04 UTC

An S4 class to represent a 'Gouderman' LDO object, that was generated by the generalized gauderman algorithm.

Description

An S4 class to represent a 'Gouderman' LDO object, that was generated by the generalized gauderman algorithm.

Slots

name

character Name of the new 'generalized-Gauderman' adjusted longitudinal data

dataFrames

list List of 'generalized-Gauderman' modified data. One data.frame for each component.

peaknames

character Vector of component names contained in this object.

k

numeric Updated times for the breaks of the spline model.

times

matrix Vector of updated time values.

newTimeVars

character The names of the newly defined time variables of the generalized 'Gauderman' model.

ids

character Vector of identifiers for the experiments

labels

factor Vector of class labels for each experiment

An S4 class to represent the result of the linear mixed effect modeling on a gauderman LDO.

Description

An S4 class to represent the result of the linear mixed effect modeling on a gauderman LDO.

Slots

name

character Name of the new 'generalized-Gauderman' adjusted longitudinal model.

gaudermanLDO

GaudermanLDO 'Generalized-Gauderman' adjusted longitudinal data object.

models

list List of models generated for each component.

labels

factor Vector of class labels for each experiment

pvalues

matrix Matrix of p-values for the intercept as well as all slops of the spline model for each component.

correctedpvalues

matrix Matrix of corrected p-values for the intercept as well as all slops of the spline model for each component.

modelparameter

matrix Model parameter for each component.

An S4 class to represent a 'LoBrA' Data Object (LDO). It stores multiple time series data for muliple experiements and multiple Components. It allows repeated measurements of a component, irregular sampling, and unequal temporal spacing of the time points.

Description

An S4 class to represent a 'LoBrA' Data Object (LDO). It stores multiple time series data for muliple experiements and multiple Components. It allows repeated measurements of a component, irregular sampling, and unequal temporal spacing of the time points.

Slots

name

character Name of the 'LDO' object

dataMatrices

list List of matrices of component measurement. It contains a measurement for each time point and each experiment.

backgroundMatrices

list List of matrices of background measurements. It contains a measurement for each time point and each experiment.

peaknames

character Character vector of Component names

times

numeric Vector of times for each time point in the data

ids

character Vector of identifiers for the experiments

labels

factor Vector of class labels for each experiment

An S4 class to represent a model selection result based on an 'LDO'.

Description

An S4 class to represent a model selection result based on an 'LDO'.

Slots

ldo

LDO 'LDO' object the model selection is based on.

potentialBreaks

numeric Vector of numeric values that were considered as potential break points in the model selection.

splinetype

character Type of spline used.

qualityMeasure

character Quality measures used during the model selection ('AIC', 'BIC' or 'LogLik')

modelList

list List of all models tested.

quality

list List of quality matrices, one matrix for each quality measure used. Each matrix contains the quality for each spline tested for each component.

breaks

list For each tested spline, this list contains a vector of breaks.

An S4 class to represent the screening of metabolites in an 'LDO'.

Description

An S4 class to represent the screening of metabolites in an 'LDO'.

Slots

ldo

LDO LDO object the screening is based on.

experimentIntercept

list List of experiment intercepts.

experimentResiduals

list List of experiment Residuals.

interceptPvalues

matrix Matrix of experiment intercept p-values.

residualPvalues

matrix Matrix of experiment Residual p-values.

selectedPeaks

matrix Matrix of logical values. Each entry indicates whether a specific component is significant according to a specific test.

LoBrA: A package for modeling longitudinal breath data

Description

The LoBrA package provides important data objects and functions to analyze longitudinal metabolomic (breath) data.

Introduction

Novel metabolomic technologies paved the way for longitudinal analysis of exhaled air and online monitoring of fast progressing diseases. This package implements an automated analysis approach of longitudinal data from different omics technologies, such as ion mobility spectrometry of human exhaled air and demonstrates how including temporal signals increases the statistical power in biomarker identification. It can handel multiple irregular 4D time series data. More precisely, it can simultaniously handel the data of multiple experiements each observing multiple components. Therefore, it allows repeated measurements of a component, irregular sampling, and unequal temporal spacing of the time points.

LoBrA Analysis

A typical LoBrA analysis is will comprise the following steps

1. Background Screening: Using the function screening and selectComponents to select the Components that most likely do not originate from background noise.

2. Model Selection: First, a set of spline models based on different number of splits and split positions are generated by the function lobraModelSelection. Subsequently, these models are evaluated using different quality criteria, i.e. 'AIC', 'BIC' and 'LogLik'. Finally, the most appropriate model is selected.

3. Evaluation of the non-background components on the selected model, using the longitudinal 'Gouderman' linear mixed effect model in function modelGoudermanLongitudinal.

Author(s)

Maintainer: Anne-Christin Hauschild [Copyright holder]

Authors:

Transformation of a single longitudinal data matrix into 'LoBrA' Data Object.

Description

Real signals and background noise originating from experimental settings or random events

Usage

as.LOBdataset(
  longData,
  name = "",
  id = "id",
  time = "time",
  type = "type",
  class = "class",
  bg = FALSE
)

Arguments

longData

Matrix of longitudinal data containing all components

name

name of the dataset

id

name to identify the experiment id column

time

name to identify the time column

type

name to identify the type column

class

name to identify the class column

bg

indicates whether the data table contains background data

Value

'LoBrA' data object

Examples

## Not run: 
 
  data(LoBraExample)
  name="Longitudinal Test Dataset"
  ldo<-as.LOBdataset(longDataExample, name, bg=TRUE)

'LoBrA' Data Object (LDO) for Example data set

Description

'LoBrA' example LDO created by the function 'createExampleData' and converted to an LDO by 'as.LOBdataset' function. It consist of a single matrix for all experiments, time points, types (background, experiment), class and the intensity values of all components created. The artificial data consist of 20 experiments and 100 components with 18 measurements (3 background, 15 sample). The 10 experiments are each associated to on of 2 groups (ONE and TWO). The components comprise 70 noise components and 30 components that randomly vary in their trajectories in one of three segments. Random noise is added to all intercepts, propagated and added to each time point for all samples and components separately.

Usage

components

Format

A vector of selected components from the longitudinal example data set.

Author(s)

Anne-Christin Hauschild hauschild@uni-marburg.de

Simulate background noise peaks

Description

Simulating background noise signals originating from experimental settings or random events

Usage

createBGComponents(
  components,
  samples,
  labels,
  timepoints = 15,
  bg = 3,
  mean = 5,
  sd = 3,
  experimentSD = 2,
  randomnoise = 0.1,
  plotting = FALSE
)

Arguments

components

number of background components to be created

samples

number of experiments

labels

name of each experiment

timepoints

number of sample measurements

bg

number of background measurements

mean

mean value of noise components

sd

standard deviation value of noise for this component

experimentSD

standard deviation value of each experiment for this component

randomnoise

random variation changing at each time point

plotting

Indicator whether the component will be plotted (TRUE) or not (FALSE)

Value

matrix of background components

Simulate background measurements

Description

Simulating background noise signals originating from experimental settings or random events

Usage

createBGData(samples = 10, bg = 3, mean = 0, sd = 1, randomnoise = 0.1)

Arguments

samples

number of experiments

bg

number of background measurements

mean

mean value of noise for this component

sd

standard deviation value of noise for this component

randomnoise

random variation changing at each time point

Value

matrix of background measurements

Create example data set for 'LoBrA'

Description

Real signals and background noise originating from experimental settings or random events

Usage

createExampleData(
  components = c(70, 10, 10, 10),
  samples = 10,
  classes = 2,
  bg = 3,
  timepoints = rep(5, 3),
  myfile = NA
)

Arguments

components

vector numbers of background and informative components to be created.

samples

number of experiments per class

classes

number of classes

bg

number of background measurements

timepoints

number of sample measurements for each spline

myfile

filename of the pdf file created. Note: '.pdf' is added automatically.

Value

final matrix of example data.

Examples

## Not run: 
 
  components = c(21,3,3,3)
  samples = 10
  classes = 2;
  bg = 3; 
  timepoints = rep(5,3)
  p=TRUE;
  longDataExample <- createExampleData(components, samples, classes, bg, 
                                       timepoints)
  dim(longDataExample)

Create the Gouderman Data Arrangement.

Description

Using the Gouderman methodology to create the Gouderman-Data Arrangement.

Usage

createGoudermanData(selectedLDO, breaks, center, timeperiod = NA, range = NA)

Arguments

selectedLDO

Longitudinal Data Object, containing all selected metabolites to be used for the final Gouderman model.

breaks

break points for the spline model

center

Time point that corresponds to the center time t0. The algorithm will test whether there is a significant difference between the groups at this point.

timeperiod

If the user defines the time period or segment, in the spline to be tested. Note, a 3 break point spline has 4 segments.

range

If the user defines a range, the algorithm will test whether there is a significant difference between the groups in this range.

Value

The function returns a 'GaudermanLDO' object. For more information @seealso 'GaudermanLDO' .

Examples

## Not run: 
 
  data(LoBraExample)
  selectedLDO <- selectComponents(ldo, components)
  breaks<- c(8, 12)
  center<- 12
  timeperiod <- 2;
  gaudermanLDOexample <- createGoudermanData(selectedLDO, breaks, center, timeperiod)

Simulate informative peaks

Description

This function simulates signals correlated to different informative events.

Usage

createInformativeComponents(
  components,
  samples,
  labels,
  timepoints = c(5, 5, 5),
  bg = 3,
  mean = 5,
  sd = 3,
  segment = 1,
  slopeSD = 2,
  randomnoise = 0.5,
  plotting = FALSE
)

Arguments

components

number of background components to be created

samples

number of experiments

labels

label of each experiment

timepoints

number of sample measurements

bg

number of background measurements

mean

mean value of noise for the intercept of this components

sd

standard deviation value of noise for the intercept of this component

segment

indicating the segment, that will have an informative event (changing slope for one class)

slopeSD

standard deviation value for the generated slope of for this component (mean is zero, therefore, the slope can be either negative or positive)

randomnoise

random variation changing at each time point

plotting

logical value, (default is FALSE), if TRUE the function will plot the created time series.

Value

matrix of informative components

Get colors for the plotting function.

Description

Get colors for the plotting function.

Usage

getColor(label, size)

Arguments

label

class labels of the samples

size

size of the color vector to be created

Value

col vector of colors created

Create Peak Matrices for Generalized 'Gauderman' linear mixed effect regression (LMER) Model with parameterized Times

Description

Create Peak Matrices for Generalized 'Gauderman' linear mixed effect regression (LMER) Model with parameterized Times

Usage

getGeneralizedGaudermanDataFrame(
  peakmatrix,
  sampleIds,
  classes,
  center,
  timeperiod,
  gaudermanRange,
  k
)

Arguments

peakmatrix

Peak matrix to be converted.

sampleIds

Ids of samples in the matrix

classes

Classes of samples

center

Time point that corresponds to the center time t0. The algorithm will test whether there is a significant difference between the groups at this point.

timeperiod

defines the time period or segment, in the spline to be tested. Note, a 3 break point spline has 4 segments.

gaudermanRange

range to be tested for a significant difference between the groups.

k

break points for the generalized 'Gauderman' spline model.

Value

Return the new peak matrix data frame for this peak.

Extract the optimal spline model parameters from the ModelSelection Object.

Description

The method calculates which spline model and parameters worked best with respect to the median of the specified quality measure. The median is calculated among all component models.

Usage

getOptimalSpline(
  lobraModelSelectionObject,
  qualityMeasure = "AIC",
  summeryfun = stats::median
)

Arguments

lobraModelSelectionObject

LDOmodelselection created by the 'lobraModelSelection' function. It stores all evaluated Spline models to chose from.

qualityMeasure

Quality measure to be used to select the optimal spline.

summeryfun

Define the Summery function to be used. Default value is set to stats::median. Other possible functions would be mean, for instance.

Value

The function returns a 'lobraModelSelectionObject' that contains the optimal model according to the specified quality measure. @seealso plot.modelSelectionEvaluation

Examples

## Not run: 
 
  data(LoBraExample)
  selectedLDO <- selectComponents(ldo, components)
  potentialBreaks=c(8, 12)
  nknots=c(1, 2)
  qualityMeasure=c("AIC", "BIC")
  ldoSelect<- lobraModelSelection(selectedLDO, potentialBreaks, nknots, qualityMeasure)
  
  optimalAIC<-getOptimalSpline(ldoSelect, qualityMeasure="AIC", summeryfun=stats::median)
  message(optimalAIC@breaks);
  
  optimalBIC<-getOptimalSpline(ldoSelect, qualityMeasure="BIC", summeryfun=base::mean)
  hist(unlist(optimalBIC@quality));

Testing differences of groups with respect to a specific value and test.

Description

Testing differences of groups with respect to a specific value and test.

Usage

getPvalue(y, group, test)

Arguments

y

Values to be tested

group

corresponding groups whose difference we want to test

test

specific test to be used. Can be each of the following 'bf', 'levene' or 'bartlett'.

'LoBrA' Data Object (LDO) for Example data set

Description

'LoBrA' example LDO created by the function 'createExampleData' and converted to an LDO by 'as.LOBdataset' function. It consist of a single matrix for all experiments, time points, types (background, experiment), class and the intensity values of all components created. The artificial data consist of 20 experiments and 100 components with 18 measurements (3 background, 15 sample). The 10 experiments are each associated to on of 2 groups (ONE and TWO). The components comprise 70 noise components and 30 components that randomly vary in their trajectories in one of three segments. Random noise is added to all intercepts, propagated and added to each time point for all samples and components separately.

Usage

ldo

Format

A matrix representing 20 experiments. It contains values for 100 variables at 18 time points for each experiment. Object of class LDO.

Author(s)

Anne-Christin Hauschild hauschild@uni-marburg.de

Evaluation of different spline variants.

Description

The model selection method evaluates which spline models achieve the best quality among all tested metabolites.

Usage

lobraModelSelection(
  selectedLDO,
  potentialBreaks = c(),
  nknots = c(0, 1, 2),
  splinetype = "linear",
  qualityMeasure = c("AIC", "BIC", "logLik")
)

Arguments

selectedLDO

LDO containing all selected metabolites to be used for the model selection.

potentialBreaks

Vector of all possible knots to be used for the spline modeling.

nknots

Vector of number of spline knots to be used. Therefore, 0 ~ no spline, 1 ~ spline with one knot, 2 ~ spline with two knots, etc.

splinetype

spline type default is 'linear'. (Currently only linear is supported.)

qualityMeasure

Vector of quality measures to be used. Possible options are 'AIC', 'BIC', and 'logLik'.

Value

LDOmodelselection Object. For each quality measure the model list contains a list of models for each spline tested. Additionally, the output contains a matrix of qualities for each Spline Component pair. And finally there is a list of breaks for each spline tested.

Examples

## Not run: 
 
  data(LoBraExample)
  potentialBreaks <- c(8,12)
  selectedLDO <- selectComponents(ldo, components)
  ldoSelect<- lobraModelSelection(selectedLDO, potentialBreaks, nknots=c( 1, 2))
  length(ldoSelect@ldo@peaknames)

'LoBrA' Example Data Set

Description

'LoBrA' example data set created by the function 'createExampleData'. #' It consist of a single matrix for all experiments, time points, types (background, experiment), class and the intensity values of all components created. The artificial data consist of 20 experiments and 100 components with 18 measurements (3 background, 15 sample). The 10 experiments are each associated to on of 2 groups (ONE and TWO). The components comprise 70 noise components and 30 components that randomly vary in their trajectories in one of three segments. Random noise is added to all intercepts, propagated and added to each time point for all samples and components separately.

Usage

longDataExample

Format

A matrix representing 20 experiments. It contains values for 100 variables at 18 time points for each experiment.

id

Experiment identifier

time

Time Point of Measurement

type

Type of Measurement (e.g. Background, or Sample measurement for each experiment)

class

Class or Group id of the sample/ experiment

bgcomponent-x

70 random variables that represent the background noise of the experiments

components-x-x

30 components that randomly vary in their trajectories in one of three time periods, (1:4-8, 2:9-13, 3:14-18).

...

Author(s)

Anne-Christin Hauschild hauschild@uni-marburg.de

Fitting the Gouderman LME Model with using Gouderman-Data Arrangement.

Description

Uses the linear mixed effects modeling to build the final 'Gauderman' model. The 'Gauderman' modification enables the exact calculation of the significance of a specified section of the spline model.

Usage

modelGoudermanLongitudinal(mygaudermanLDO, correctionMethod = "bonferroni")

Arguments

mygaudermanLDO

GaudermanLDO data object, created by the generalized 'Gauderman' algorithm (GGA).

correctionMethod

correction for p-values. Possible methods: 'holm', 'hochberg', 'hommel', 'bonferroni', 'BH', 'BY', 'fdr', 'none'

Value

'GaudermanModelEvaluation' Results of the evaluation of the Fitted linear mixed effect models for the defined time periods.

Examples

  
  data(LoBraExample)
  selectedLDO <- selectComponents(ldo, components)
  gaudermanLDOexample <- createGoudermanData(selectedLDO, breaks=c(8, 12), center=12, timeperiod=2)
  evalResult<- modelGoudermanLongitudinal(gaudermanLDOexample)
  message(evalResult@correctedpvalues<0.005)

Plotting helper function to plot a single generalized gouderman Model

Description

Plotting helper function to plot a single generalized gouderman Model

Usage

plotGaudermanModel(
  data,
  labels,
  ul,
  tempmodel,
  colores,
  maincol,
  breaks,
  main,
  ylab,
  xlab
)

Arguments

data

data matrix used to fit the model

labels

class labels for all samples

ul

unique class labels

tempmodel

model to be plotted

colores

predefined colors for the single samples

maincol

predefined colors for the fitted spline

breaks

break points of the spline to be plotted

main

main title of the plot

ylab

y label of the plot

xlab

x label of the plot

Plotting the 'Gouderman' LME Model and Results.

Description

Plotting the 'Gouderman' LME Model and Results.

Usage

plotGoudermanLongitudinalResults(
  evaluationresult,
  main = "Mixed Effect Spline Model Evaluation",
  ylab = "Value",
  xlab = "Time",
  peaknames = NULL
)

Arguments

evaluationresult

'GaudermanModelEvaluation' data object, created by the modelGoudermanLongitudinal function.

main

title of the plot

ylab

y axis label

xlab

x axis label

peaknames

selection of peaks to be plotted

Value

No return value

Examples

  
  wd <- tempdir()
  data(LoBraExample)
  selectedLDO <- selectComponents(ldo, components)
  gaudermanLDOexample <- createGoudermanData(selectedLDO, breaks=c(8, 12), center=12, timeperiod=2)
  evalResult<- modelGoudermanLongitudinal(gaudermanLDOexample)
  # Plot all peaks
  filename<- file.path(wd, "finalModelEvaluation.pdf") ;
  oldpar <- par("mfrow")
  grDevices::pdf(filename, width=16, height=8);
    graphics::par(mfrow=c(1,1));
    plotGoudermanLongitudinalResults(evalResult);
  par(mfrow = oldpar)
  grDevices::dev.off();
  
  #Plot a selection of Peaks
  peaknames<- evalResult@gaudermanLDO@peaknames;
  filename<- file.path(wd, "finalModelEvaluation-components.pdf") ;
  oldpar <- par("mfrow")
  grDevices::pdf(filename, width=20, height=8);
    graphics::par(mfrow=c(2,5));
    plotGoudermanLongitudinalResults(evalResult, main="", peaknames=peaknames);
  par(mfrow = oldpar)
  grDevices::dev.off();

Plotting the screening results.

Description

For each peak two box plots are created. The first plot shows a boxplot of the Sample Intercept Comparison of the sample and the background, and the corresponding p-values. The second plot shows a boxplot of the Residual Comparison of the sample and the background, and the corresponding p-values.

Usage

plotLDOScreening(
  ldoscreen,
  plotAll = FALSE,
  correctionmethod = "levene",
  decs = 3,
  ask = FALSE,
  peaknames = rownames(ldoscreen@selectedPeaks)
)

Arguments

ldoscreen

LDO screening result

plotAll

Select all components to be plotted. Default plots only the selected peaks using the correction method.

correctionmethod

Version of correction method to be used to select the peaks. Valid values are 'bf', 'levene', and 'bartlett'.

decs

decimal numbers of p-values to be plotted.

ask

logical. Modifies the graphical parameter ask in par (If TRUE (and the R session is interactive) the user is asked for input, before a new figure is drawn. As this applies to the device, it also affects output by packages grid and lattice. It can be set even on non-screen devices but may have no effect there.)

peaknames

Defining a list of peaks to be plotted. By default all peaks will be plotted.

Value

No return value

Examples

## Not run: 
 

  wd <- tempdir()
  data(LoBraExample)
  ldos<-screening(ldo, method= c('levene'), alpha =0.05, criteria=c(1,1))
  filename<- file.path(wd, "screeningresults.pdf") 
  grDevices::pdf(filename, width=16, height=8)
  plotLDOScreening(ldos)
  grDevices::dev.off();

Plotting function for a longitudinal data matrix (Internal Function)

Description

Plotting function for a longitudinal data matrix (Internal Function)

Usage

plotTimeSeries(
  myMatrix,
  main = "",
  labels = NA,
  ylab = "Expression",
  xlab = "Time Point",
  legend = "",
  col = 1:dim(myMatrix)[1]
)

Arguments

myMatrix

longitudinal data matrix to be plotted

main

Title of the plot

labels

class labels of samples

ylab

Label of y axis

xlab

Label of x axis

legend

of plot

col

vector of colors for plot

Value

No return value

Plotting results of Model Evaluation and Selection.

Description

Plotting the results of Model Evaluation and Selection. The plot shows a vertical boxplot for each spline tested starting with the best average fit according to the selected quality measure. The label of each spline can be found on the left, the median quality measure on the right. The x-axis denotes the selected quality measure.

Usage

plotmodelSelectionEvaluation(
  lobraModelSelectionObject,
  qualityMeasure,
  title = NULL
)

Arguments

lobraModelSelectionObject

Object of type LDOmodelselection that was created during the model evaluation. @seealso 'lobraModelSelection'

qualityMeasure

List of quality measures to be visualized.

title

Title of the plot.

Value

No return value

Examples

## Not run: 
 

wd <- tempdir()
data(LoBraExample)
selectedLDO <- selectComponents(ldo, components)
ldoSelect<- lobraModelSelection(selectedLDO, potentialBreaks=c(8, 12), nknots=c(1, 2))

filename<- file.path(wd, "evaluateBestSplineAIC.pdf") ;
grDevices::pdf(filename, width=16, height=8);
  plotmodelSelectionEvaluation(ldoSelect, "AIC", "Best Spline Models");
grDevices::dev.off();
  
qualityMeasure=c("AIC", "BIC", "logLik")
filename<- file.path(wd, "evaluateBestSplineAllMeasures.pdf") ;
grDevices::pdf(filename, width=16, height=8);
oldpar <- par("mfrow")
par(mfrow=c(3,1))
  plotmodelSelectionEvaluation(ldoSelect, qualityMeasure);
par(mfrow = oldpar)
grDevices::dev.off();

Creating the power set of a set.

Description

Creating the power set of a set.

Usage

powerSet(set)

Arguments

set

Set of numbers of potential spline break points.

Value

Returns power set of the given set.

Screening of background or confounding components

Description

Background noise signals originating from experimental settings or random events can hugely influence the signal pattern of the breath. Background data enables the detailed evaluation and differentiation of the compounds originating primarily from the background or confounding factors as compared to those from the sample itself. The method assumes that all compounds of interest show a larger variation in the sample as compared to the background noise.

Usage

screening(
  ldo,
  method = c("bf", "levene", "bartlett"),
  alpha = 0.05,
  criteria = c(1, 1)
)

Arguments

ldo

Longitudinal Data Object

method

list of tests to perform, standard values: 'bf', 'levene' or 'bartlett'). 'bf' relates to "Brown-Forsythe" Levene-type procedure, 'levene' uses classical "Levene's" procedure and 'bartlett' applies Bartlett's test.

alpha

A numeric value to defining the cutoff to select peaks.

criteria

indicators which criteria to use for screening decision.

Value

Returns an object of type 'LDOscreening' containing the original 'ldo' object and the results of the screening. The variable 'selectedPeaks' contains a matrix including the results (TRUE = Significant, FALSE = not Significant) of the specified tests ('bf', 'levene', 'bartlett').

Examples

## Not run: 
 

  data(LoBraExample)
  method= c('bf', 'levene', 'bartlett')
  alpha =0.05
  criteria=c(1,1)
  ldos<-screening(ldo, method, alpha, criteria)
  components <- ldos@selectedPeaks[,"levene"]
  components <- names(components)[components]
  selectedLDO <- selectComponents(ldo, components)

Create a new 'LDO' Object that only contains the selected components.

Description

Create a new 'LDO' Object that only contains the selected components.

Usage

selectComponents(ldo, components, name = paste(ldo@name, " selected"))

Arguments

ldo

Longitudinal Data Object

components

Component names to select for the new ldo object. Only elements from this list that overlap with the peak names in the given ldo, are utilized.

name

Name of newly created 'LDO' object.

Value

new ldo object only containing the selected components.