vmu | R Documentation |
Computes the metric vector model of unfolding (VMU) on rectangular input data (preferences, ratings) with the individuals (rows) represented as vectors in the biplot. There is also the option to fix the column coordinates.
vmu(delta, ndim = 2, center = TRUE, scale = FALSE, col.coord = NULL)
## S3 method for class 'vmu'
plot(x, ...)
delta |
Data frame or matrix of preferences, ratings, dissimilarities |
ndim |
Number of dimensions |
center |
If |
scale |
If |
col.coord |
Optional fixed coordinates for the column objects in |
x |
Object of class |
... |
Additional arguments passed to |
conf.row |
Row coordinates |
conf.col |
Column coordinates |
VAF |
variance accounted for |
Ingwer Borg and Patrick Mair
Borg, I., & Groenen, P. J. F. (2005). Modern Multidimensional Scaling (2nd ed.). Springer.
Borg, I., Groenen, P. J. F., & Mair, P. (2018). Applied Multidimensional Scaling and Unfolding (2nd ed.). Springer.
Tucker, L. R. (1960). Intra-individual and inter-individual multidimensionality. In H. Gulliksen & S. Messick (Eds.), Psychological scaling: Theory and applications (pp. 155-167). Wiley.
Mair, P, Groenen, P. J. F., De Leeuw, J. (2022). More on multidimensional scaling in R: smacof version 2. Journal of Statistical Software, 102(10), 1-47. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v102.i10")}
biplot
, unfolding
## VMU on portrait value questionnaire ratings
fit_vmu <- vmu(PVQ40agg) ## fit 2D VMU
fit_vmu
plot(fit_vmu, cex = c(1, 0.7)) ## call biplot from stats
## VMU with fixed column coordinates (circular)
tuv <- matrix(0, nrow = 10, ncol = 2)
alpha <- -360/10
for (i in 1:10){
alpha <- alpha+360/10
tuv[i, 1]<- cos(alpha*pi/180)
tuv[i, 2] <- sin(alpha*pi/180)
}
fit_vmu2 <- vmu(PVQ40agg, col.coord = tuv) ## fit 2D circular VMU
fit_vmu2
plot(fit_vmu2, cex = c(1, 0.7))
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