Description Usage Arguments Details Value See Also Examples

`lscomb`

calculates Stress levels for all combinations of p out of
n categories

1 2 |

`data` |
Dataset; the first column must be the ID of the unit of comparison,
the other columns must be categories; see dataset requirements |

`points` |
Number of categories that should be included in the model (p < n, wherein n equals the number of categories in the dataset). |

`method` |
Specifies the similarity index used to compute the similarity
matrix, choose between " |

`single` |
If |

`comments` |
If |

`type` |
Specifies the type of MDS model used (for more details see
the package ' |

`weight` |
If |

This function is applicable to co-occurrence data. It shows the resulting
Stress values for all combinations of p out of n categories. The output
consists in a table showing which categories have been included and the
resulting Stress values. The table is sorted such that the lowest Stress
level occurs at the top. The MDS model is computed using the package
'`smacof`

' (Mair, De Leeuw, Borg, & Groenen). The first column of the
input matrix `data`

should contain the ID of the unit of comparison
and the following columns the categories for which the similarity matrices
and MDS maps are calculated (see also the description of `simi`

). Note
that the purpose of this function is to assist modeling by helping to
identify potential problems. It is not, however, meant to be used for
excluding categories based solely on measures of fit and without substantial
justification.

Matrix showing the categories included and the Stress values of the respective MDS models.

`smacofSym`

for details on calculating MDS
representations, `simi`

for details on calculating
similarity matrices.

1 2 3 4 5 6 7 | ```
## Calculate Stress values for 5 out of 7 SDGs
data(SDG_coocurrence)
SDG_coocurrence <- SDG_coocurrence[,-2] # Drop second column
input <- SDG_coocurrence[,1:8] # For computational reasons, we will work
# with 7 SDGs.
stress <- lscomb(input, points = 5)
stress
``` |

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