summary.PTAk {PTAk}

Summary of a PTA-k modes analysis

Description

Print a summary listing of the decomposition obtained.

Usage

## S3 method for class 'PTAk'
summary(object,testvar=1,dontshow="*", ...)
## S3 method for class 'FCAk'
summary(object,testvar=0.5,dontshow="*", ...)

Arguments

object

an object inheriting from class PTAk, representing a generalised singular value decomposition

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(solution[[length(solution)]][["vsnam"]],1,1)=="*"

...

summary generic additional arguments not used here

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.PTAk) will be also useful. summary.FCAk is alike summary.PTAk but testvar operates on the variability of the lack of complete independence.

Value

prints on the prompt with an invisible return of the summary table

Note

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

Author(s)

Didier G. Leibovici [e-mail]

References

Leibovici D (2000) Multiway Multidimensional Analysis for Pharmaco-EEG Studies.(submitted) https://www.researchgate.net/publication/216807619_Multiway_Multidimensional_Analysis_for_Pharmaco-EEG_Studies

See Also

plot.PTAk

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)
  RiskJackplot(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")