dualScale-package: Dual Scaling for Multiple Choice Data

Description Details Author(s) References See Also Examples

Description

This package includes dsMC and dsFC for several versions of dual scaling of multiple-choice data.

Details

Package: ds
Type: Package
Version: 0.9
Date: 2014-01-24
License: GPL2

With dsMC and dsFC a ds class object is created. It can be analyzed later with print.ds, plot.ds or summary.ds

Author(s)

Jose G. Clavel, Shizuhiko Nishisato and Antonio Pita

Maintainer: Jose G. Clavel <dualScale@gmail.com>

References

Nishisato and Clavel (2014). Nishisato (2007)

See Also

dsMC, dsFC, dsCHECK, plot.ds, print.ds, summary.ds

Examples

1
2
3
4
5
data(singapore)
ciuca<-dsFC(singapore,2,6)
plot(ciuca)
bea<-dsMC(singapore)
print(bea)

Example output

Loading required package: matrixcalc
Loading required package: ff
Loading required package: bit
Attaching package bit
package:bit (c) 2008-2012 Jens Oehlschlaegel (GPL-2)
creators: bit bitwhich
coercion: as.logical as.integer as.bit as.bitwhich which
operator: ! & | xor != ==
querying: print length any all min max range sum summary
bit access: length<- [ [<- [[ [[<-
for more help type ?bit

Attaching package: 'bit'

The following object is masked from 'package:base':

    xor

Attaching package ff
- getOption("fftempdir")=="/work/tmp/tmp/RtmpJkax8v"

- getOption("ffextension")=="ff"

- getOption("ffdrop")==TRUE

- getOption("fffinonexit")==TRUE

- getOption("ffpagesize")==65536

- getOption("ffcaching")=="mmnoflush"  -- consider "ffeachflush" if your system stalls on large writes

- getOption("ffbatchbytes")==16777216 -- consider a different value for tuning your system

- getOption("ffmaxbytes")==536870912 -- consider a different value for tuning your system


Attaching package: 'ff'

The following objects are masked from 'package:bit':

    clone, clone.default, clone.list

The following objects are masked from 'package:utils':

    write.csv, write.csv2

The following objects are masked from 'package:base':

    is.factor, is.ordered

Loading required package: vcd
Loading required package: MASS
Loading required package: grid
Loading required package: colorspace
Loading required package: lattice
Loading required package: Matrix
Warning messages:
1: In (f_t/t(sve[, i]) %*% sve[, i])^0.5 * sve[, i] :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

2: In (f_t/t(sve[, i]) %*% sve[, i])^0.5 * sve[, i] :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

3: In (f_t/t(sve[, i]) %*% sve[, i])^0.5 * sve[, i] :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

4: In (f_t/t(sve[, i]) %*% sve[, i])^0.5 * sve[, i] :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

5: In (f_t/t(sve[, i]) %*% sve[, i])^0.5 * sve[, i] :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

6: In (f_t/t(sve[, i]) %*% sve[, i])^0.5 * sve[, i] :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

7: In (f_t/t(sve[, i]) %*% sve[, i])^0.5 * sve[, i] :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.

8: In (f_t/t(sve[, i]) %*% sve[, i])^0.5 * sve[, i] :
  Recycling array of length 1 in array-vector arithmetic is deprecated.
  Use c() or as.vector() instead.


Prior Analysis: dsFC(X = singapore, Crit = 2, dim = 6)
[1] 1 2
Type of Graph: Asymmetric Plot I of dimensions 1 2
Cumulative Delta: 100%

Command:
 dsMC(X = singapore)

Type of Analysis:
 Ordinay Dual Scaling 

Results:
 Component Eigenvalue SingValue   Alpha Delta CumDelta
         1      0.648     0.805   0.819 32.38     32.4
         2      0.441     0.664   0.577 22.03     54.4
         3      0.317     0.563   0.282 15.85     70.3
         4      0.214     0.462  -0.227 10.68     80.9
         5      0.184     0.429  -0.476  9.21     90.2
         6      0.116     0.340  -1.548  5.79     95.9
         7      0.053     0.230  -4.985  2.64     98.6
         8      0.028     0.168 -10.407  1.42    100.0

Distribution of Information Over 8 Components:
    q.1   q.2   q.3   q.4  Avge
1 0.861 0.702 0.364 0.663 0.648
2 0.742 0.190 0.019 0.812 0.441
3 0.012 0.454 0.737 0.065 0.317
4 0.076 0.197 0.517 0.065 0.214
5 0.166 0.264 0.264 0.042 0.184
6 0.006 0.130 0.071 0.256 0.116
7 0.071 0.039 0.024 0.077 0.053
8 0.065 0.024 0.004 0.020 0.028

Inter Item Correlation for Component 1 :
    [,1]  [,2]  [,3]  [,4] 
q.1 1.000 0.801 0.386 0.703
q.2 0.801 1.000 0.330 0.479
q.3 0.386 0.330 1.000 0.398
q.4 0.703 0.479 0.398 1.000

Inter Item Correlation for Component 2 :
    [,1]  [,2]   [,3]   [,4] 
q.1 1.000  0.123  0.106 0.653
q.2 0.123  1.000 -0.097 0.266
q.3 0.106 -0.097  1.000 0.061
q.4 0.653  0.266  0.061 1.000

Inter Item Correlation for Component 3 :
    [,1]   [,2]   [,3]  [,4]  
q.1  1.000 -0.073 0.018  0.250
q.2 -0.073  1.000 0.270 -0.170
q.3  0.018  0.270 1.000  0.181
q.4  0.250 -0.170 0.181  1.000

Inter Item Correlation for Component 4 :
    [,1]   [,2]   [,3]   [,4]  
q.1  1.000  0.490 -0.318 -0.113
q.2  0.490  1.000 -0.156 -0.345
q.3 -0.318 -0.156  1.000  0.204
q.4 -0.113 -0.345  0.204  1.000

Inter Item Correlation for Component 5 :
    [,1]   [,2]   [,3]   [,4]  
q.1  1.000 -0.051  0.066 -0.558
q.2 -0.051  1.000 -0.336  0.286
q.3  0.066 -0.336  1.000  0.052
q.4 -0.558  0.286  0.052  1.000

Inter Item Correlation for Component 6 :
    [,1]   [,2]   [,3]   [,4]  
q.1  1.000 -0.144 -0.191  0.119
q.2 -0.144  1.000  0.221 -0.477
q.3 -0.191  0.221  1.000 -0.410
q.4  0.119 -0.477 -0.410  1.000

Inter Item Correlation for Component 7 :
    [,1]   [,2]   [,3]   [,4]  
q.1  1.000 -0.191 -0.003 -0.618
q.2 -0.191  1.000 -0.396 -0.161
q.3 -0.003 -0.396  1.000 -0.150
q.4 -0.618 -0.161 -0.150  1.000

Inter Item Correlation for Component 8 :
    [,1]   [,2]   [,3]   [,4]  
q.1  1.000 -0.756 -0.185 -0.682
q.2 -0.756  1.000  0.059  0.368
q.3 -0.185  0.059  1.000 -0.145
q.4 -0.682  0.368 -0.145  1.000

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