find.threshold.C: Conduct permutation test on labeling to get null distribution...

In textreg: n-Gram Text Regression, aka Concise Comparative Summarization

Description

First determines what regularization will give null model on labeling. Then permutes labeling repeatidly, recording what regularization will give null model for permuted labeling. This allows for permutation-style inference on the relationship of the labeling to the text, and allows for appropriate selection of the tuning parameter.

Usage

find.threshold.C(corpus, labeling, banned = NULL, R = 0,
  objective.function = 2, a = 1, verbosity = 0,
  step.verbosity = verbosity, positive.only = FALSE,
  binary.features = FALSE, no.regularization = FALSE,
  positive.weight = 1, Lq = 2, min.support = 1, min.pattern = 1,
  max.pattern = 100, gap = 0, token.type = "word",
  convergence.threshold = 1e-04)

Arguments

corpus	A list of strings or a corpus from the tm package.
labeling	A vector of +1/-1 or TRUE/FALSE indicating which documents are considered relevant and which are baseline. The +1/-1 can contain 0 whcih means drop the document.
banned	List of words that should be dropped from consideration.
R	Number of times to scramble labling. 0 means use given labeling and find single C value.
objective.function	2 is hinge loss. 0 is something. 1 is something else.
a	What percent of regularization should be L1 loss (a=1) vs L2 loss (a=0)
verbosity	Level of output. 0 is no printed output.
step.verbosity	Level of output for line searches. 0 is no printed output.
positive.only	Disallow negative features if true
binary.features	Just code presence/absence of a feature in a document rather than count of feature in document.
no.regularization	Do not renormalize the features at all. (Lq will be ignored.)
positive.weight	Scale weight pf all positively marked documents by this value. (1, i.e., no scaling) is default) NOT FULLY IMPLEMENTED
Lq	Rescaling to put on the features (2 is standard). Can be from 1 up. Values above 10 invoke an infinity-norm.
min.support	Only consider phrases that appear this many times or more.
min.pattern	Only consider phrases this long or longer
max.pattern	Only consider phrases this short or shorter
gap	Allow phrases that have wildcard words in them. Number is how many wildcards in a row.
token.type	"word" or "character" as tokens.
convergence.threshold	How to decide if descent has converged. (Will go for three steps at this threshold to check for flatness.)

Details

Important: use the same parameter values as used with the original textreg call!

Value

A list of numbers (the Cs) R+1 long. The first number is always the C used for the _passed_ labeling. The remainder are shuffles.

Examples

data( testCorpora )
find.threshold.C( testCorpora$testI$corpus, testCorpora$testI$labelI, c(), R=5, verbosity=1 )

textreg documentation built on May 2, 2019, 8:34 a.m.