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Free MS DOS versions of the MDSX Library of programs with supporting documentation are available download where indicated
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Program: |
As named by originator |
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Source: |
Source of original program (numerical sub-routines etc. updated by MDSX staff) |
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Data: |
Usually: Way and Mode (see Carroll & Arabie) |
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Model: |
As specified by originator's documentation |
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Transform: |
Transformation Function, also called Level of Measurement |
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Int/External |
External (with user-supplied configuration), or internal (derived from input data) analysis |
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Hierarchy: |
Single program, or hierarchy of models/phases |
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Representation: |
How mode/s are represented in the configuration according to the model |
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TUG: |
Section of The User's Guide to Multidimensional Scaling in which the program is discussed |
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N.B. All programs are also separately documented in the User Manual |
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Program name & [source]
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Description |
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PRELIMINARY
Downloads
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Information about the MDSX series; Running MDSX programs; Attribution; |
BBDIAM
Source: Brusco
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Branch and Bound DIAMeter clustering
DATA: 2-Way 1-Mode
MODEL: Partition Clustering
TRANSFORMATION: Ordinal
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Exclusive clusters
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CANDECOMP
Bell Labs
Downloads
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CANonical DECOMPosition
DATA: internal analysis of a set of 3- to 7-way data matrix of (dis) similarity matrices,
MODEL: Product: Vector, Scalar Products, Factor, Composition
TRANSFORMATION: metric/linear.
INT/EXTERNAL: I & E
HIERARCHY: n
REPRESENTATION: Unidimensional Scale values for each way
TUG: 7.1.1, 7.2.2
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CONPAR
Brusco
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CONcordance PARtitioning
DATA: Internal analysis of a set of three-way (2-mode) data matrix consisting of a set of (dis)similarity matrices
MODEL: A two part model: 1) Subjects are partitioned into homogeneous clusters, using BBDIAM (q.v.) and an aggregate dissimilarity matrix produced for each cluster.. 2)Each cluster's data rae scaled using MINISSA Distance scalings
TRANSFORMATION: Ordinal
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: separate scalings
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CORRESP
Brier
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CORRESPondence analysis
DATA: 2-Way 2-mode table (set of profiles)
MODEL: Chi-square distance
TRANSFORMATION: Ordinal
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: N-mode point
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HICLUS
Johnson
Downloads
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HIerarchical CLUStering
DATA: two-way (1 mode) (dis) similarity data
MODEL: hierarchical clustering scheme (ultrametric : Diameter & Connectedness solutions)
TRANSFORMATION: ordinal, non-metric monotonic
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Inclusive partitions / Tree
TUG: 6.1.6
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INDSCAL-S
Bell Labs
Basic 3-way model
Downloads
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INdividual Differences SCALing - Symmetric
DATA: internal analysis of a three-way (2-mode) data matrix consisting of a set of (dis) similarity matrices
MODEL: Weighted Euclidean Distance / product composition
TRANSFORMATION: metric/ linear
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Stimulus Points, subject weights
TUG: 7.1.1, 7.2, A7.2
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MDPREF
Bell Labs
Downloads
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MultiDimensional PREFerence Scaling
DATA: internal analysis of two-way, 2-mode data of either a set of paired comparisons matrices or a rectangular, row-conditional matrix of ratings/rankings
MODEL: Product
TRANSFORMATION: linear
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Stimulus Points, subject vectors
TUG: 5.3.2, 6.2.2
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MDSORT
Takane
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MultiDimensional SORTing
DATA: two-way, 2-mode categorical data (set of sortings)
MODEL: product
TRANSFORMATION: linear
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Point
TUG: (Coxon 1999)
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MINI-RSA
Roskam
Downloads
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(Michigan-Israel-Netherlands Integrated = MINI)
Rectangular Smallest space Analysis
DATA: internal analysis of two-way, 2-mode data in a rectangular (row-conditional) matrix of (preference) rankings or ratings
MODEL: Euclidean distance (Unfolding) model
TRANSFORMATION: ordinal
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Point-point
TUG: 5.3.3.1, 6.2.3
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MINISSA (N)
Roskam
Basic Nonmetric Model
Downloads
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[MINI] Smallest Space Analysis (Nijmegen version)
DATA: internal analysis of a two-way (1mode) symmetric matrix of (dis) similarities
MODEL: Euclidean distance
TRANSFORMATION: Ordinal also called non-metric monotonic
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Point
TUG: Ch. 3
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MRSCAL
Roskam
Downloads
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MetRic SCALing
DATA: internal analysis of a two-way (1 mode) symmetric matrix of (dis) similarities
MODEL: Minkowski r-metric (default Euclidean distance model)
TRANSFORMATION: Linear and Log-interval
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Point
TUG: 6.1.4
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PARAMAP
Bell Labs
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PARAmetric MAPping
DATA: Two-way, 2-mode data (Profiles)
MODEL: Smoothness
TRANSFORMATION: Continuity
INT/EXTERNAL: I, E
HIERARCHY: n
REPRESENTATION: Points (1 mode)
TUG: 5.2.2, 6.1.5
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PINDIS
Roskam Lingoes
Downloads
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Procrustean INdividual DIfferences Scaling (Hierarchy)
DATA: Two-way 2-mode Configuration Co-ordinates
MODEL: Hierarchy of Procrustean Fitting models (general distance and general vector)
TRANSFORMATIONS: Similarity, then progressively more complex
INT/EXTERNAL: I, E
HIERARCHY: y (6 models)
REPRESENTATION: Point (dist.), Vector
TUG: 7.3 - 7.6, A7.1
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PREFMAP
Bell Labs
Downloads
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PREFerence MAPping (Hierarchy)
DATA: external (and quasi-internal) analysis of two-way, 2-mode row- conditional data (usually a preference measure)
MODEL: 3 general distance and 1 vector models
TRANSFORMATION: Linear, ordinal
INT/EXTERNAL: E, I
HIERARCHY: y 4
REPRESENTATION: Point, vector
TUG: 4.2, 4.4, 6.2.1, 7.5
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PROCRUSTES
Roskam Lingoes
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PROCRUSTEan Similarity (=PINDIS0)
DATA: 2-way 2-mode Configuration Co-ordinates
MODEL: Euclidean Distance
TRANSFORMATION: Similarity (Reflection, Rotation, Uniform re-scaling)
INT/EXTERNAL: I,E
HIERARCHY: n
REPRESENTATION: Point (dist.)
TUG: A7.1
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PRO-FIT
Bell Labs
Downloads
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PROperty FITting
DATA: external analysis of a configuration, using 2-way 2-mode rectangular matrix of property ratings or rankings
MODEL: scalar products (vector)
TRANSFORMATION: either a linear or continuity
INT/EXTERNAL: E
HIERARCHY: n
REPRESENTATION: vector
TUG: 5.22, 6.2.1
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TRISOSCAL
Roskam-Prentice
Downloads
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TRIadic Similarities Ordinal SCALing
DATA: internal analysis of a set of triadic (dis) similarity measures
MODEL: Minkowski distance
TRANSFORMATION: Ordinal
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: point
TUG: 2.1.4, 6.1.3
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CONJOINT (Formerly UNICON)
Roskam
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CONJOINT measurement (UNICON, ADDIT, MONANOVA)
DATA: N-way table
MODEL: Simple composition models
TRANSFORMATION: Ordinal
INT/EXTERNAL: I
HIERARCHY: n
REPRESENTATION: Unidimensional scale values for each way
TUG: 5.3.1, 6.1.8
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WOMBATS
[Coxon-Sykes]
Downloads
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Work Out Measures Before Attempting To Scale (Utility)
DATA: From rectangular raw data matrix
Computes one or more measures of dis/similarity , and
Outputs in a user-chosen matrix format
(for input into MDSX and other programs)
MODEL: Dis/similarity Measures
TUG: 2.2
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Programs in the original MDSX (SV3.2) Library not included, but still available:
MINICPA (GLR: [MINI] Conditional Proximity Analysis).
MVNDS (BL: Shepard’s Maximum Variance Non-Dimensional Scaling)
PARAMAP (BL: Carroll’s PARAmetric MAPping)
UNICON (GLR: UNIdimensional CONjoint measurement)
CONCOR (Arabie: CONvergence of iterated CORrelations: blockmodel analysis through the production of the image matrices)
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