textplot_scale1d.Rd
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).
textplot_scale1d(x, margin = c("documents", "features"), doclabels = NULL, sort = TRUE, groups = NULL, highlighted = NULL, alpha = 0.7, highlighted_color = "black")
x | the fitted or predicted scaling model object to be plotted |
---|---|
margin |
|
doclabels | a vector of names for document; if left NULL (the default), docnames will be used |
sort | if |
groups | either: a character vector containing the names of document variables to be used for grouping; or a factor or object that can be coerced into a factor equal in length or rows to the number of documents. See groups for details. |
highlighted | a vector of feature names to draw attention to in a
feature plot; only applies if |
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 |
highlighted_color | color for highlighted terms in |
a ggplot2 object
The groups
argument only applies when margin = "documents"
.
# 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_wordscores(ie_dfm, refscores, smooth = 1) # plot estimated word positions textplot_scale1d(ws, highlighted = c("minister", "have", "our", "budget")) # plot estimated document positions textplot_scale1d(predict(ws), doclabels = doclab, groups = docvars(data_corpus_irishbudget2010, "party")) ## wordfish wf <- textmodel_wordfish(dfm(data_corpus_irishbudget2010), dir = c(6,5)) # plot estimated document positions textplot_scale1d(wf, doclabels = doclab) textplot_scale1d(wf, doclabels = doclab, groups = docvars(data_corpus_irishbudget2010, "party")) # plot estimated word positions textplot_scale1d(wf, margin = "features", highlighted = c("government", "global", "children", "bank", "economy", "the", "citizenship", "productivity", "deficit")) ## correspondence analysis ca <- textmodel_ca(ie_dfm) # plot estimated document positions textplot_scale1d(ca, margin = "documents", doclabels = doclab, groups = docvars(data_corpus_irishbudget2010, "party")) # }