quanteda (version 0.9.9-50)

textplot_scale1d: plot a fitted scaling model

Description

Plot the results of a fitted scaling model, from (e.g.) a predicted textmodel_wordscores model or a fitted textmodel_wordfish or textmodel_ca model. Either document or feature parameters may be plotted: an ideal point-style plot (estimated document position plus confidence interval on the x-axis, document labels on the y-axis) with optional renaming and sorting, or as a plot of estimated feature-level parameters (estimated feature positions on the x-axis, and a measure of relative frequency or influence on the y-axis, with feature names replacing plotting points with some being chosen by the user to be highlighted).

Usage

textplot_scale1d(x, margin = c("documents", "features"), doclabels = NULL,
  sort = TRUE, groups = NULL, highlighted = NULL, alpha = 0.7,
  highlighted_color = "black")

Arguments

x

the fitted or predicted scaling model object to be plotted

margin

"documents" to plot estimated document scores (the default) or "features" to plot estimated feature scores by a measure of relative frequency

doclabels

a vector of names for document; if left NULL (the default), docnames will be used

sort

if TRUE (the default), order points from low to high score. If a vector, order according to these values from low to high. Only applies when margin = "documents".

groups

optional grouping variable for sorting categories of the documents. Only applies when margin = "documents".

highlighted

a vector of feature names to draw attention to in a feature plot; only applies if margin = "features"

alpha

A number between 0 and 1 (default 0.5) representing the level of alpha transparency used to overplot feature names in a feature plot; only applies if margin = "features"

highlighted_color

color for highlighted terms in highlighted

Value

a ggplot2 object

See Also

textmodel_wordfish, textmodel_wordscores, coef.textmodel

Examples

## Not run: ------------------------------------
# ie_dfm <- dfm(data_corpus_irishbudget2010)
# doclab <- apply(docvars(data_corpus_irishbudget2010, c("name", "party")), 
#                 1, paste, collapse = " ")
# 
# ## wordscores
# refscores <- c(rep(NA, 4), -1, 1, rep(NA, 8))
# ws <- textmodel(ie_dfm, refscores, model="wordscores", smooth = 1)
# pred <- predict(ws)
# # plot estimated word positions
# textplot_scale1d(pred, margin = "features", 
#                  highlighted = c("minister", "have", "our", "budget"))
# # plot estimated document positions
# textplot_scale1d(pred, margin = "documents",
#                  doclabels = doclab,
#                  groups = docvars(data_corpus_irishbudget2010, "party"))
# 
# ## wordfish
# wfm <- textmodel_wordfish(dfm(data_corpus_irishbudget2010), dir = c(6,5))
# # plot estimated document positions
# textplot_scale1d(wfm, doclabels = doclab)
# textplot_scale1d(wfm, doclabels = doclab,
#                  groups = docvars(data_corpus_irishbudget2010, "party"))
# # plot estimated word positions
# textplot_scale1d(wfm, margin = "features", 
#                  highlighted = c("government", "global", "children", 
#                                  "bank", "economy", "the", "citizenship",
#                                  "productivity", "deficit")) 
## ---------------------------------------------