# chisquare-method: Perform chisquare-text. In polmineR: Verbs and Nouns for Corpus Analysis

 chisquare R Documentation

## Perform chisquare-text.

### Description

Perform Chisquare-Test based on a table with counts

### Usage

chisquare(.Object)

## S4 method for signature 'features'
chisquare(.Object)

## S4 method for signature 'context'
chisquare(.Object)

## S4 method for signature 'cooccurrences'
chisquare(.Object)


### Arguments

 .Object A features object, or an object inheriting from it (context, cooccurrences).

### Details

The basis for computing for the chi square test is a contingency table of observationes, which is prepared for every single token in the corpus. It reports counts for a token to inspect and all other tokens in a corpus of interest (coi) and a reference corpus (ref):

 coi ref TOTAL count token o_{11} o_{12} r_{1} other tokens o_{21} o_{22} r_{2} TOTAL c_{1} c_{2} N

Based on the contingency table, expected values are calculated for each cell, as the product of the column and margin sums, divided by the overall number of tokens (see example). The standard formula for calculating the chi-square test is computed as follows.

X^{2} = \sum{\frac{(O_{ij} - E_{ij})^2}{O_{ij}}}

Results from the chisquare test are only robust for at least 5 observed counts in the corpus of interest. Usually, results need to be filtered accordingly (see examples).

### Value

Same class as input object, with enriched table in the stat-slot.

Andreas Blaette

### References

Manning, Christopher D.; Schuetze, Hinrich (1999): Foundations of Statistical Natural Language Processing. MIT Press: Cambridge, Mass., pp. 169-172.

Kilgarriff, A. and Rose, T. (1998): Measures for corpus similarity and homogeneity. Proc. 3rd Conf. on Empirical Methods in Natural Language Processing. Granada, Spain, pp 46-52.

Other statistical methods: ll(), pmi(), t_test()

### Examples

use("polmineR")
library(data.table)
m <- partition(
"GERMAPARLMINI", speaker = "Merkel", interjection = "speech",
regex = TRUE, p_attribute = "word"
)
f <- features(m, "GERMAPARLMINI", included = TRUE)
f_min <- subset(f, count_coi >= 5)
summary(f_min)

## Not run:

# A sample do-it-yourself calculation for chisquare:

# (a) prepare matrix with observed values
o <- matrix(data = rep(NA, 4), ncol = 2)
o[1,1] <- as.data.table(m)[word == "Weg"][["count"]]
o[1,2] <- count("GERMAPARLMINI", query = "Weg")[["count"]] - o[1,1]
o[2,1] <- size(f)[["coi"]] - o[1,1]
o[2,2] <- size(f)[["ref"]] - o[1,2]

# prepare matrix with expected values, calculate margin sums first

r <- rowSums(o)
c <- colSums(o)
N <- sum(o)

e <- matrix(data = rep(NA, 4), ncol = 2)
e[1,1] <- r[1] * (c[1] / N)
e[1,2] <- r[1] * (c[2] / N)
e[2,1] <- r[2] * (c[1] / N)
e[2,2] <- r[2] * (c[2] / N)

# compute chisquare statistic

y <- matrix(rep(NA, 4), ncol = 2)
for (i in 1:2) for (j in 1:2) y[i,j] <- (o[i,j] - e[i,j])^2 / e[i,j]
chisquare_value <- sum(y)

as(f, "data.table")[word == "Weg"][["chisquare"]]

## End(Not run)


polmineR documentation built on Nov. 2, 2023, 5:52 p.m.