Description Usage Arguments Details Value See Also Examples
lspoints
calculates Stress values for all combinations with n  1
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

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 cooccurrence data. It shows the resulting
Stress values when single categories are excluded. The output consists in a
table showing which categories have been excluded and the resulting Stress
values. The table is sorted such that the 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 excluded 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  ## Calculate Stress values for all combinations with n  1 (i.e., 16) SDGs
data(SDG_coocurrence)
SDG_coocurrence < SDG_coocurrence[,2] # Drop second column
stress < lspoints(SDG_coocurrence)
stress

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