plot_wordlist: 2D- or 3D-Plot of a list of words
In codymarquart/LSAfun: Applied Latent Semantic Analysis (LSA) Functions

Description Usage Arguments Details Value Author(s) References See Also Examples

2D or 3D-Plot of mutual word similarities to a given list of words

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plot_wordlist(x,connect.lines=0,method="PCA",dims=3,
   axes=F,box=F,cex=1,alpha=0.5,col="black",
   tvectors=tvectors,breakdown=TRUE,...)

`x`	a character vector of `length(x) > 1` that contains multiple words
`dims`	the dimensionality of the plot; set either `dims = 2` or `dims = 3`
`method`	the method to be applied; either a Principal Component Analysis (`method="PCA"`) or a Multidimensional Scaling (`method="MDS"`)
`connect.lines`	(3d plot only) the number of closest associate words each word is connected with via line. Setting `connect.lines="all"` will draw all connecting lines and will automatically apply `alpha="shade"`; it will furthermore override the `start.lines` argument
`axes`	(3d plot only) whether axes shall be included in the plot
`box`	(3d plot only) whether a box shall be drawn around the plot
`cex`	(2d Plot only) A numerical value giving the amount by which plotting text should be magnified relative to the default.
`tvectors`	the semantic space in which the computation is to be done (a numeric matrix where every row is a word vector)
`breakdown`	if `TRUE`, the function `breakdown` is applied to the input
`alpha`	(3d plot only) a vector of one or two numerics between 0 and 1 specifying the luminance of `start.lines` (first entry) and `connect.lines` (second entry). Specifying only one numeric will pass this value to both kinds of lines. With setting `alpha="shade"`, the luminance of every line will be adjusted to the cosine between the two words it connects.
`col`	(3d plot only) a vector of one or two characters specifying the color of `start.lines` (first entry) and `connect.lines` (second entry). Specifying only one colour will pass this colour to both kinds of lines. With setting `col ="rainbow"`, the colour of every line will be adjusted to the cosine between the two words it connects. Setting `col ="rainbow"` will alsp apply `alpha="shade"`
`...`	additional arguments which will be passed to `plot3d` (in a three-dimensional plot only)

Computes all pairwise similarities within a given list of words. On this similarity matrix, a Principal Component Analysis (PCA) or a Multidimensional Sclaing (MDS) is applied to get a two- or three-dimensional solution that best captures the similarity structure. This solution is then plottet.
For creating pretty plots showing the similarity structure within this list of words best, set connect.lines="all" and col="rainbow"

see plot3d: this function is called for the side effect of drawing the plot; a vector of object IDs is returned.
plot_neighbors also gives the coordinate vectors of the words in the plot as a data frame

Fritz GÃ¼nther

Landauer, T.K., & Dumais, S.T. (1997). A solution to Plato's problem: The Latent Semantic Analysis theory of acquisition, induction and representation of knowledge. Psychological Review, 104, 211-240.

Mardia, K.V., Kent, J.T., & Bibby, J.M. (1979). Multivariate Analysis, London: Academic Press.

cosine, neighbors, multicos, plot_neighbors, plot3d, princomp

data(wonderland)

## Standard Plot

words <- c("alice","hatter","queen","knight","hare","cheshire") 
            
plot_wordlist(words,tvectors=wonderland,method="MDS",dims=2)