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

View source: R/plot_wordlist.r

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

1 2 3 |

`x` |
a character vector of |

`dims` |
the dimensionality of the plot; set either |

`method` |
the method to be applied; either a Principal Component Analysis ( |

`connect.lines` |
(3d plot only) the number of closest associate words each word is connected with via line. Setting |

`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 |

`alpha` |
(3d plot only) a vector of one or two numerics between 0 and 1 specifying the luminance of |

`col` |
(3d plot only) a vector of one or two characters specifying the color of |

`...` |
additional arguments which will be passed to |

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 Günther

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`

1 2 3 4 5 6 7 | ```
data(wonderland)
## Standard Plot
words <- c("alice","hatter","queen","knight","hare","cheshire")
plot_wordlist(words,tvectors=wonderland,method="MDS",dims=2)
``` |

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