extract-methods function

Methods to extract information from objects of class "MPMData", "ESTATICSModel", "sESTATICSModel", "qMaps", "IRdata", "IRfluid" and "IRmixed".

The extract-methods extract and/or compute specified statistics from object of class "MPMData", "ESTATICSModel", "sESTATICSModel", "qMaps", "IRdata", "IRfluid" and "IRmixed". The [-methods can be used to reduce objects of class "MPMData", "ESTATICSModel", "sESTATICSModel", "qMaps", "IRdata", "IRfluid" and "IRmixed" such that they contain a subcube of data and results. methods

## S3 method for class 'MPMData'
extract(x, what, ...)
## S3 method for class 'ESTATICSModel'
extract(x, what, ...)
## S3 method for class 'sESTATICSModel'
extract(x, what, ...)
## S3 method for class 'qMaps'
extract(x, what, ...)
## S3 method for class 'IRdata'
extract(x, what, ...)
## S3 method for class 'IRfluid'
extract(x, what, ...)
## S3 method for class 'IRmixed'
extract(x, what, ...)
## S3 method for class 'MPMData'
x[i, j, k, ...]
## S3 method for class 'ESTATICSModel'
x[i, j, k, ...]
## S3 method for class 'sESTATICSModel'
x[i, j, k, ...]
## S3 method for class 'qMaps'
x[i, j, k, ...]
## S3 method for class 'IRdata'
x[i, j, k, tind, ...]
## S3 method for class 'IRfluid'
x[i, j, k, ...]
## S3 method for class 'IRmixed'
x[i, j, k, ...]

Arguments

• x: object of class "MPMData", "ESTATICSModel", "sESTATICSModel"

  or "qMaps".

• what: Character vector of of names of statistics to extract. See Methods Section for details.

• i: index vector for first spatial dimension

• j: index vector for second spatial dimension

• k: index vector for third spatial dimension

• tind: index vector for inversion times

• ...: additional parameters, currently unused.

Methods

• class(x) = "ANY": Returns a warning for extract

• class(x) = "MPMData": Depending the occurence of names in what a list with the specified components is returned

    * ddata: mpm data
    * sdim: dimension of image cube
    * nFiles: number of images / image files
    * t1Files: character - filenames of t1Files
    * pdFiles: character - filenames of pdFiles
    * mtFiles: character - filenames of mtFiles
    * model: Number of the ESTATICS model that can be used
    * maskFile: character - filenames of maskFile
    * mask: mask
    * TR: vector of TR values
    * TE: vector of TE values
    * FA: vector of FA values
  

• class(x) = "ESTATICSModel": Depending the occurence of names in what a list with the specified components is returned

    * modelCoeff: Estimated parameter maps
    * invCov: map of inverse covariance matrices
    * rsigma: map of residual standard deviations
    * isConv: convergence indicator map
    * isThresh: logical map indicating where `R2star==maxR2star`
    * sdim: image dimension
    * nFiles: number of images
    * t1Files: vector of T1 filenames
    * pdFiles: vector of PD filenames
    * mtFiles: vector of MT filenames
    * model: model used (depends on specification of MT files)
    * maskFile: filename of brain mask
    * mask: brain mask
    * sigma: standard deviation sigma
    * L: effective number of receiver coils L
    * TR: TR values
    * TE: TE values
    * FA: Flip angles (FA)
    * TEScale: TEScale
    * dataScale: dataScale
  

• class(x) = "sESTATICSModel": Depending the occurence of names in what a list with the specified components is returned

    * modelCoeff: Estimated parameter maps
    * invCov: map of inverse covariance matrices
    * rsigma: map of residual standard deviations
    * isConv: convergence indicator map
    * bi: Sum of weights map from AWS/PAWS
    * smoothPar: smooting parameters used in AWS/PAWS
    * smoothedData: smoothed mpmData
    * isThresh: logical map indicating where `R2star==maxR2star`
    * sdim: image dimension
    * nFiles: number of images
    * t1Files: vector of T1 filenames
    * pdFiles: vector of PD filenames
    * mtFiles: vector of MT filenames
    * model: model used (depends on specification of MT files)
    * maskFile: filename of brain mask
    * mask: brain mask
    * sigma: sigma
    * L: effective number of receiver coils L
    * TR: TR values
    * TE: TE values
    * FA: Flip angles (FA)
    * TEScale: TEScale
    * dataScale: dataScale
  

• class(x) = "qMaps": Depending the occurence of names in what a list with the specified components is returned

    * b1Map: b1Map
    * R1: Estimated map of R1
    * R2star: Estimated map of R2star
    * PD: Estimated map of PD
    * MT: Estimated map of delta (if MT-series was used)
    * model: Type of ESTATICS model used
    * t1Files: filenames T1
    * mtFiles: filenames MT
    * pdFiles: filenames PD
    * mask: brainmask
  

Returns

A list with components carrying the names of the options specified in argument what.

References

J. Polzehl and K. Tabelow (2023), Magnetic Resonance Brain Imaging: Modeling and Data Analysis Using R, 2nd Edition, Chapter 7, Springer, Use R! Series. doi:10.1007/978-3-031-38949-8_7.

Author(s)

Karsten Tabelow tabelow@wias-berlin.de

J"org Polzehl polzehl@wias-berlin.de