In miserman/lingmatch: Linguistic Matching and Accommodation

This example demonstrates a few ways to specify comparisons and groups in lingmatch.

Built with R r getRversion() on r format(Sys.time(),'%B %d %Y')

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Setup

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  dev = "CairoSVG",
  fig.ext = "svg"
)
library(lingmatch)

We'll generate some word category output, in a sort of experimental design that allows for all available comparison types:

Imagine in two studies we paired up participants, then had them have a series of interactions after reading one of a set of prompts:

# load lingmatch
library(lingmatch)

# first, we have simple representations (function word category use frequencies)
# of our prompts (3 prompts per study):
prompts <- data.frame(
  study = rep(paste("study", 1:2), each = 3),
  prompt = rep(paste("prompt", 1:3), 2),
  matrix(rnorm(3 * 2 * 7, 10, 4), 3 * 2, dimnames = list(NULL, names(lma_dict(1:7))))
)
prompts[1:5, 1:8]

# then, the same representation of the language the participants produced:
data <- data.frame(
  study = sort(sample(paste("study", 1:2), 100, TRUE)),
  pair = sort(sample(paste("pair", formatC(1:20, width = 2, flag = 0)), 100, TRUE)),
  prompt = sample(paste("prompt", 1:3), 100, TRUE),
  speaker = sample(c("a", "b"), 100, TRUE),
  matrix(rnorm(100 * 7, 10, 4), 100, dimnames = list(NULL, colnames(prompts)[-(1:2)]))
)
data[1:5, 1:8]

Matching with a standard

Sample means

Compare each row (here representing a turn in an conversation) with the sample's mean:

# the `lsm` (Language Style Matching) type specifies the columns to consider,
# and the metric to use (Canberra similarity)
lsm_mean <- lingmatch(data, mean, type = "lsm")

# look at comparison information
lsm_mean[c("comp.type", "comp")]

# and maybe the average similarity score
mean(lsm_mean$sim)

This could be considered a baseline for the sample.

Stored means

These LSM categories have some standard means stored internally, as found in the LIWC manual.

# compare with means from a set of tweets
lsm_twitter <- lingmatch(data, "twitter", type = "lsm")
lsm_twitter[c("comp.type", "comp")]
mean(lsm_twitter$sim)

# or the means of the set that is most similar to the current set
lsm_auto <- lingmatch(data, "auto", type = "lsm")
lsm_auto[c("comp.type", "comp")]
mean(lsm_auto$sim)

External means

If you have another set of data, you can also use its means as the comparison:

lsm_prmed <- lingmatch(data, colMeans(prompts[, -(1:2)]), type = "lsm")
lsm_prmed[c("comp.type", "comp")]
mean(lsm_prmed$sim)

Group means

You can also compare to means within groups. Here, studies might be considered groups:

lsm_topics <- lingmatch(data, group = study, type = "lsm")
lsm_topics[c("comp.type", "comp")]
tapply(lsm_topics$sim[, 2], lsm_topics$sim[, 1], mean)

This type of group variable is just splitting the data, and performing the same comparisons within splits.

Matching with other texts

The previous comparisons were all with standards, where the LSM score could be interpreted as indicating a more or less generic language style (as defined by the comparison and grouping).

Condition ID

Here, prompts constitute our experimental conditions. We have 3 unique prompt IDs, but 6 unique prompts, since each study had its own set, so we need the study and prompt ID to appropriately match prompts:

lsm <- lingmatch(data, prompts, group = c("study", "prompt"), type = "lsm")
lsm$comp.type
lsm$comp[, 1:6]
lsm$sim[1:10, ]

Here, the group argument is just pasting together the included variables, and using the resulting string to identify a single comparison for each text (acting as a condition ID).

Participant ID

Similarly, participants are only uniquely identified by pair ID and speaker ID (though this could just as well be a single column with unique IDs).

interlsm <- lingmatch(data, group = c("pair", "speaker"), type = "lsm")
interlsm$comp[1:10, ]
interlsm$sim[1:10, ]

Matching in sequence

Since participants are having interactions in sequence, we might compare each turn in sequence. The last entry in the group argument specifies the speaker:

seqlsm <- lingmatch(data, "seq", group = c("pair", "speaker"), type = "lsm")
seqlsm$sim[1:10, ]

The rownames of sim show the row numbers that are being compared, with some being aggregated if the same speaker takes multiple turns in a row. You could also just compare edges by adding agg = FALSE:

lingmatch(
  data, "seq",
  group = c("pair", "speaker"), type = "lsm", agg = FALSE
)$sim[1:10, ]

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miserman/lingmatch documentation built on Feb. 21, 2025, 3 p.m.