textreg (version 0.1.3)

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

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.)

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.

Details

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

Examples

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