pams: Profile Analysis via Multidimensional Scaling

Description Usage Arguments Details Value References See Also Examples

View source: R/pams.R

Description

The pams function implements profile analysis via multidimensional scaling as described by Davison, Davenport, and Bielinski (1995) and Davenport, Ding, and Davison (1995).

Usage

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Arguments

data

A data matrix or data frame; rows represent individuals, columns represent scores; missing scores are not allowed.

dim

Number of dimensions to be extracted from the data.

Details

The pams function computes similarity/dissimilarity indices based on Euclidean distances between the scores provided in the data, and then extracts dimensional coordinates for each score using multidimensional scaling. A weight matrix, level parameters, and fit measures are computed for each subject in the data.

Value

References

Davenport, E. C., Ding, S., & Davison, M. L. (1995). PAMS: SAS Template.

Davison, M. L., Davenport, E. C., & Bielinski, J. (1995). PAMS: SPSS Template.

See Also

cpa, pr

Examples

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## Not run: 
data(PS)
result <- pams(PS[,2:4], dim=2)
result

## End(Not run)

Example output

Loading required package: ggplot2
Loading required package: RColorBrewer
Loading required package: reshape
Loading required package: lavaan
This is lavaan 0.5-23.1097
lavaan is BETA software! Please report any bugs.
$weights.matrix
     weight1 weight2 level R.sq
[1,]     1.5 0.00000    70    1
[2,]     1.5 0.00000    40    1
[3,]     0.0 2.12132    70    1
[4,]     0.0 2.12132    40    1
[5,]    -1.5 0.00000    70    1
[6,]    -1.5 0.00000    40    1

$dimensional.configuration
    Dimension1 Dimension2
Neu  -6.666667  -2.357023
Psy   0.000000   4.714045
CD    6.666667  -2.357023

profileR documentation built on April 20, 2018, 1:04 a.m.