R: Summary of a PTA-k modes analysis

summary.solutions.PTAk {PTAk}

R Documentation

Summary of a PTA-k modes analysis

Description

Print a summary listing of the decomposition obtained.

Usage

summary.solutions.PTAk(solution,testvar=1,dontshow="*")
summary.solutions.FCAk(solution,testvar=1,dontshow="*")
summary(object,testvar=1,dontshow="*")

Arguments

`solution`	a `solutions.PTAk` object
`testvar`	control within `nTens` used Principal Tensor with minimum percent of variability explained
`dontshow`	boolean criterion to remove Principal Tensors from the summary, or default is a character "" equivalent to the criterion: `!substr(solutiont[[length(solution)]][["vsnam"]],1,1)==""`

Details

The function prints a listing of the decomposition with historical order (instead of traditional singular value order). It is useful before any plots or reconstruction, a screeplot (using plot.PTAk) will be also useful. It is useful before any plots r reconstruction, a screeplot (using plot.solutions.PTAk) will be also useful. summary.solutions.FCAk is alike summary.solutions.PTAk but testvar operates on the variability of the lack of complete independence.

Value

prints on the prompt

Note

At the moment can be used for solutions.PCAn, solutions.CANDPRA, better summaries will be in the next release.

Author(s)

Didier Leibovici didier@fmrib.ox.ac.uk

References

Leibovici D (2000) Multiway Multidimensional Analysis for Pharmaco-EEG Studies.(submitted) http://www.fmrib.ox.ac.uk/~didier/cv/recentpub.html

Examples

 data(crimerate)
 crimerate.mat <- sweep(crimerate,2,apply(crimerate,2,mean))
 crimerate.mat <- sweep(crimerate.mat,2,sqrt(apply(crimerate,2,var)),FUN="/")
 cri.svd <- SVDgen(crimerate.mat)
 summary(cri.svd,testvar=0)
  plot(cri.svd,scree=TRUE)
  par(new=TRUE)
  RiskJack.plot(cri.svd,nbvs=1:7,mod=NULL,max=NULL,rescaled=TRUE,
        axes=FALSE,ann=FALSE)
  par(new=FALSE)

  # or equivalently

  plot(cri.svd,scree=TRUE,type="b",lty=3,RiskJack=1) #set mod=NULL or c(1,2)
  ###
   data(crimerate)
   criafc <- FCAmet(crimerate,chi2=TRUE)
   cri.afc <- SVDgen(criafc$data,criafc$met[[2]],criafc$met[[1]])
    summary(cri.afc)
   plot(cri.afc,scree=TRUE)
   plot(cri.afc,scree=TRUE,type="b",lty=3,RiskJack=1,method="FCA")