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fmds (version 0.1.5)

food: Food data

Description

38 subjects places 45 food items in categories based on similarity. The dissimilarities are the proportions of combinations NOT placed in the same category.

Usage

food

Arguments

Format

45 x 45 dissimilarity matrix

  • V1: dissimilarities for V1.

  • V2: dissimilarities for V2.

  • V3: dissimilarities for V3.

  • V4: dissimilarities for V4.

  • V5: dissimilarities for V5.

  • V6: dissimilarities for V6.

  • V7: dissimilarities for V7.

  • V8: dissimilarities for V8.

  • V9: dissimilarities for V9.

  • V10: dissimilarities for V10.

  • V11: dissimilarities for V11.

  • V12: dissimilarities for V12.

  • V13: dissimilarities for V13.

  • V14: dissimilarities for V14.

  • V15: dissimilarities for V15.

  • V16: dissimilarities for V16.

  • V17: dissimilarities for V17.

  • V18: dissimilarities for V18.

  • V19: dissimilarities for V19.

  • V20: dissimilarities for V20.

  • V21: dissimilarities for V21.

  • V22: dissimilarities for V22.

  • V23: dissimilarities for V23.

  • V24: dissimilarities for V24.

  • V25: dissimilarities for V25.

  • V26: dissimilarities for V26.

  • V27: dissimilarities for V27.

  • V28: dissimilarities for V28.

  • V29: dissimilarities for V29.

  • V30: dissimilarities for V30.

  • V31: dissimilarities for V31.

  • V32: dissimilarities for V32.

  • V33: dissimilarities for V33.

  • V34: dissimilarities for V34.

  • V35: dissimilarities for V35.

  • V36: dissimilarities for V36.

  • V37: dissimilarities for V37.

  • V38: dissimilarities for V38.

  • V39: dissimilarities for V39.

  • V40: dissimilarities for V40.

  • V41: dissimilarities for V41.

  • V42: dissimilarities for V42.

  • V43: dissimilarities for V43.

  • V44: dissimilarities for V44.

  • V45: dissimilarities for V45.

References

Ross and Murphy (1999). Food for thought: Cross-classification and category organization in a complex real-world domain. Cognitive psychology, 38(4), 495-553. Brusco and Stahl (2000). Using Quadratic Assignment Methods to Generate Initial Permutations for Least-Squares Unidimensional Scaling of Symmetric Proximity Matrices. Journal of Classification, 17(2).