Dissimilarity Statistics

Usage

Dissimilarity(text.var, grouping.var = NULL, method = "prop", diag = FALSE, upper = FALSE, p = 2, ...)

Arguments

text.var

A text variable or word frequency matrix object.

grouping.var

The grouping variables. Default NULL generates one word list for all text. Also takes a single grouping variable or a list of 1 or more grouping variables.

method

Distance methods (see dist function). If "prop" (the default) the result is 1 - "binary".

diag

logical. If TRUE returns the diagonals of the matrix. If method = "prop" diagonals will not be returned.

upper

logical. If TRUE returns the upper triangle of the matrix.

p

The power of the Minkowski distance.

...

Other arguments passed to wfm.

Examples

## <strong>Not run</strong>: 
# with(DATA, Dissimilarity(state, list(sex, adult)))
# with(DATA, Dissimilarity(state, person, diag = TRUE))
# 
# ## Clustering: Dendrogram
# (x <- with(pres_debates2012, Dissimilarity(dialogue, list(person, time))))
# fit <- hclust(x)
# plot(fit)
# ## draw dendrogram with red borders around the 3 clusters
# rect.hclust(fit, k=3, border=c("red", "purple", "seagreen"))
# 
# ## Clustering: Dendrogram with p.values
# library(pvclust)
# wfm.mod <- with(pres_debates2012, wfm(dialogue, list(person, time)))
# fit <- suppressMessages(pvclust(wfm.mod, method.hclust="ward",
#     method.dist="euclidean"))
# plot(fit)
# pvrect(fit, alpha=.95)
# 
# ## Multidimentional Scaling
# ## Based on blog post from Bodong Chen
# ## http://bodongchen.com/blog/?p=301
# 
# ## Fit it: 2-D
# (diss <- with(pres_debates2012, Dissimilarity(dialogue, list(person, time),
#     method = "euclidean")))
# fit <- cmdscale(diss, eig = TRUE, k = 2)
# 
# ## Plot it 2-D
# points <- data.frame(x = fit$points[, 1], y = fit$points[, 2])
# ggplot(points, aes(x = x, y = y)) +
#     geom_point(data = points, aes(x = x, y = y, color = rownames(points))) +
#     geom_text(data = points, aes(x = x, y = y - 0.2, label = row.names(points)))
# 
# ## Fit it: 3-D
# library(scatterplot3d)
# fit <- cmdscale(diss, eig = TRUE, k = 3)
# 
# points <- data.frame(colSplit(names(fit$points[, 1])))
# library(qdapTools)
# points$colors <- points$X1 %l% data.frame(levels(points$X1),
#     qcv(yellow, yellow, blue, yellow, red, yellow))
# points$shape <- points$X2 %l% data.frame(levels(points$X2), c(15, 17, 19))
# 
# ## Plot it: 3-D
# scatterplot3d(fit$points[, 1], fit$points[, 2], fit$points[, 3],
#     color = points$colors, pch = points$shape,
#     main = "Semantic Space Scaled to 3D", xlab = "x", ylab = "y",
#     zlab = "z", type = "h")
# 
# legend("bottomright", title="Person",
#    qcv(Obama, Romney, Other), fill=qcv(blue, red, yellow))
# legend("topleft",  paste("Time", 1:3), pch=c(15, 17, 19))
# 
# ## Compare to Cosine Similarity
# cos_sim <- function(x, y) x %*% y / sqrt(x%*%x * y%*%y)
# mat <- matrix(rbinom(500, 0:1, .45), ncol=10)
# v_outer(mat, cos_sim)
# 
# v_outer(with(DATA, wfm(state, person)), cos_sim)
# with(DATA, Dissimilarity(state, person))
# ## <strong>End(Not run)</strong>

See also