wssa: Word Set Significane Analysis (WSSA) for a given word set in...

In kkdey/WEAVER: Word Enrichment Analysis using Vector Representations

Description

Performs a word set significance analysis in a corpus for a given topic of interest, specified by the header_set, with a control_set forming the background.

Usage

wssa(model, model_boot = NULL, word_set, header_set, control_set = NULL,
  num_controls = 5000, fgsea.control = list())

Arguments

model	A word2vec VectorSpace model ouput.
model_boot	If not NULL, a list of resampled Vectospace model outputs generated by applying word2vec on resampled corpus.Defaults to NULL, which ignores the resampling.
word_set	A set of words (a vector of words) whose significance is to be tested.
header_set	a word or a vector of words defining a topic of interest.
control_set	A vector of words forming the background. Defaults to NULL in which case the function chooses a random set of words from the vocabulary.
num_controls	The number of control words chosen for background if control_set is NULL.
fgsea.control	The control parameters for the GSEA model fitting using the fgsea() package.

Value

Returns an output of the significance analysis (p-values and expected score) for the word_set of interest in the context of the header_set with the control_set as background.

kkdey/WEAVER documentation built on May 8, 2019, 9:24 a.m.