The plotting method for objects of the S3 class 'MultimodDiagnostic', which
are returned by the function multiSTM(), which performs a battery of
tests aimed at assessing the stability of the local modes of an STM model.
# S3 method for MultimodDiagnostic
plot(x, ind = NULL, topics = NULL, ...)An object of S3 class 'MultimodDiagnostic'. See
multiSTM.
An integer of list of integers specifying which plots to generate
(see details). If NULL (default), all plots are generated.
An integer or vector of integers specifying the topics for
which to plot the posterior distribution of covariate effect estimates. If
NULL (default), plots are generated for every topic in the S3 object.
Other arguments to be passed to the plotting functions.
Brandon M. Stewart (Princeton University) and Antonio Coppola (Harvard University)
This methods generates a series of plots, which are indexed as follows. If a
subset of the plots is required, specify their indexes using the ind
argument. Please note that not all plot types are available for every object
of class 'MultimodDiagnostic':
Histogram of Expected
Common Words: Generates a 10-bin histogram of the column means of
obj$wmat, a K-by-N matrix reporting the number of "top words" shared
by the reference model and the candidate model. The "top words" for a given
topic are defined as the 10 highest-frequency words.
Histogram of
Expected Common Documents: Generates a 10-bin histogram of the column means
of obj$tmat, a K-by-N matrix reporting the number of "top documents"
shared by the reference model and the candidate model. The "top documents"
for a given topic are defined as the 10 documents in the reference corpus
with highest topical frequency.
Distribution of .95
Confidence-Interval Coverage for Regression Estimates: Generates a histogram
of obj$confidence.ratings, a vector whose entries specify the
proportion of regression coefficient estimates in a candidate model that
fall within the .95 confidence interval for the corresponding estimate in
the reference model. This can only be generated if
obj$confidence.ratings is non-NULL.
Posterior
Distributions of Covariate Effect Estimates By Topic: Generates a square
matrix of plots, each depicting the posterior distribution of the regression
coefficients for the covariate specified in obj$reg.parameter.index
for one topic. The topics for which the plots are to be generated are
specified by the topics argument. If the length of topics is
not a perfect square, the plots matrix will include white space. The plots
have a dashed black vertical line at zero, and a continuous red vertical
line indicating the coefficient estimate in the reference model. This can
only be generated if obj$cov.effects is non-NULL.
Histogram of Expected L1-Distance From Reference Model: Generates a 10-bin
histogram of the column means of obj$lmat, a K-by-N matrix reporting
the L1-distance of each topic from the corresponding one in the reference
model.
L1-distance vs. Top-10 Word Metric: Produces a smoothed color
density representation of the scatterplot of obj$lmat and
obj$wmat, the metrics for L1-distance and shared top-words, obtained
through a kernel density estimate. This can be used to validate the metrics
under consideration.
L1-distance vs. Top-10 Docs Metric: Produces a
smoothed color density representation of the scatterplot of obj$lmat
and obj$tmat, the metrics for L1-distance and shared top-documents,
obtained through a kernel density estimate. This can be used to validate the
metrics under consideration.
Top-10 Words vs. Top-10 Docs Metric:
Produces a smoothed color density representation of the scatterplot of
obj$wmat and obj$tmat, the metrics for shared top-words and
shared top-documents, obtained through a kernel density estimate. This can
be used to validate the metrics under consideration.
Maximized Bound
vs. Aggregate Top-10 Words Metric: Generates a scatter plot with linear
trendline for the maximized bound vector (obj$lb) and a linear
transformation of the top-words metric aggregated by model
(obj$wmod/1000).
Maximized Bound vs. Aggregate Top-10 Docs
Metric: Generates a scatter plot with linear trendline for the maximized
bound vector (obj$lb) and a linear transformation of the top-docs
metric aggregated by model (obj$tmod/1000).
Maximized Bound
vs. Aggregate L1-Distance Metric: Generates a scatter plot with linear
trendline for the maximized bound vector (obj$lb) and a linear
transformation of the L1-distance metric aggregated by model
(obj$tmod/1000).
Top-10 Docs Metric vs. Semantic Coherence:
Generates a scatter plot with linear trendline for the reference-model
semantic coherence scores and the column means of object$tmat.
L1-Distance Metric vs. Semantic Coherence: Generates a scatter plot with
linear trendline for the reference-model semantic coherence scores and the
column means of object$lmat.
Top-10 Words Metric vs. Semantic
Coherence: Generates a scatter plot with linear trendline for the
reference-model semantic coherence scores and the column means of
object$wmat.
Same as 5, but using the limited-mass
L1-distance metric. Can only be generated if obj$mass.threshold != 1.
Same as 11, but using the limited-mass L1-distance metric. Can
only be generated if obj$mass.threshold != 1.
Same as
7, but using the limited-mass L1-distance metric. Can only be
generated if obj$mass.threshold != 1.
Same as 13, but
using the limited-mass L1-distance metric. Can only be generated if
obj$mass.threshold != 1.
Roberts, M., Stewart, B., & Tingley, D. (Forthcoming). "Navigating the Local Modes of Big Data: The Case of Topic Models. In Data Analytics in Social Science, Government, and Industry." New York: Cambridge University Press.
multiSTM
if (FALSE) {
# Example using Gadarian data
temp<-textProcessor(documents=gadarian$open.ended.response,
metadata=gadarian)
meta<-temp$meta
vocab<-temp$vocab
docs<-temp$documents
out <- prepDocuments(docs, vocab, meta)
docs<-out$documents
vocab<-out$vocab
meta <-out$meta
set.seed(02138)
mod.out <- selectModel(docs, vocab, K=3,
prevalence=~treatment + s(pid_rep),
data=meta, runs=20)
out <- multiSTM(mod.out, mass.threshold = .75,
reg.formula = ~ treatment,
metadata = gadarian)
plot(out)
plot(out, 1)
}
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