# R/print.ds.R In dualScale: Dual Scaling Analysis of Multiple Choice Data

#### Documented in print.ds

```print.ds <-
function(x,type='', ...)
{
cat("\nCommand:\n ")
print(x\$Call)
#
if(x\$tipo=="O"){Type<-c("Ordinay Dual Scaling")}
if(x\$tipo=="A"){Type<-c("Forced Classification")}
#if(type=="B"){Type<-c("Ignoring Criterion Item")}

cat("\nType of Analysis:\n", Type,"\n")
#
cat("\nResults:\n")
#
switch(x\$tipo,
O = {
if(is.null(x\$Out_O)){stop("\n Your ds object needs 'A', 'B' or 'C' printing modes!")}
print(x\$Out_O,row.names=FALSE,digits=3)
cat("\nDistribution of Information Over",x\$N.Comp, "Components:\n")
print(x\$Inf_O,row.names=TRUE)
for(i in 1:x\$N.Comp){
cat("\nInter Item Correlation for Component",i,":\n")
print(data.frame(q=x\$Rij_O[,,i]),row.names=x\$cn,digits=3)
}
},
A = {print(x\$Out_A,row.names=FALSE,digits=3)
cat("\nDistribution of Information Over",x\$N.Comp, "Components:\n")
print(x\$Inf_A,row.names=TRUE)
for(i in 1:x\$SolFC){
cat("\nInter Item Correlation for Component",i,":\n")
print(data.frame(q=x\$Rij_A[,,i]),row.names=x\$cn,digits=3)
}

})
#
if(type=='B'){print(x\$Out_B,row.names=FALSE, digits=3)}
#if(type=='C'){print(x\$Out_C,row.names=FALSE, digits=3)}
}
```

## Try the dualScale package in your browser

Any scripts or data that you put into this service are public.

dualScale documentation built on May 29, 2017, 9:29 a.m.