PTAk (version 2.0.0)

summary.PTAk: Summary of a PTA-k modes analysis

Description

Print a summary listing of the decomposition obtained.

Usage

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

Value

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

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

Author

Didier G. Leibovici

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.

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")