pams function implements profile analysis via multidimensional scaling as described by Davison, Davenport, and Bielinski (1995) and Davenport, Ding, and Davison (1995).
A data matrix or data frame; rows represent individuals, columns represent scores; missing scores are not allowed.
Number of dimensions to be extracted from the data.
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.
dimensional.configuration - A matrix that provides prototypical profiles of dimensions extracted from the data.
weights.matrix - A matrix that includes the subject correspondence weights for all dimensions, level parameters, and the subject fit measure which is the proportion of variance in the subject's actual profiles accounted for by the prototypical profiles.
Davenport, E. C., Ding, S., & Davison, M. L. (1995). PAMS: SAS Template.
Davison, M. L., Davenport, E. C., & Bielinski, J. (1995). PAMS: SPSS Template.
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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
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