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

Built with R 4.4.0 on May 03 2024


Setup

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]
#>     study   prompt      ppron    ipron   article    adverb      conj     prep
#> 1 study 1 prompt 1  4.3998259 2.712729 18.260100 12.171985 17.554020 13.74145
#> 2 study 1 prompt 2 11.0212682 9.010699  3.476042  6.343701  9.610220 10.70595
#> 3 study 1 prompt 3  0.2509456 9.023202 12.049708 11.872618  6.256611 10.97474
#> 4 study 2 prompt 1  9.9777149 8.869178  2.547954 11.451805  9.936199 16.49420
#> 5 study 2 prompt 2 12.4862109 7.785202  7.911950  4.781826  6.692844 10.44815

# 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]
#>     study    pair   prompt speaker     ppron     ipron   article    adverb
#> 1 study 1 pair 01 prompt 3       a  5.560151 11.333167  8.750134 12.995852
#> 2 study 1 pair 01 prompt 2       b 10.597623 15.911564 20.095048  1.928010
#> 3 study 1 pair 01 prompt 1       b  8.455429  9.955480  8.321159  3.751488
#> 4 study 1 pair 01 prompt 2       b  9.078023  7.286095 15.348776 12.583830
#> 5 study 1 pair 01 prompt 3       a 11.568424  8.565738  9.107098 11.945809

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")]
#> $comp.type
#> [1] "mean"
#> 
#> $comp
#>     ppron     ipron   article    adverb      conj      prep   auxverb 
#>  9.672765  9.902429 10.812763  9.480402 10.038143  9.900312 10.487222

# and maybe the average similarity score
mean(lsm_mean$sim)
#> [1] 0.8344026

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")]
#> $comp.type
#> [1] "twitter"
#> 
#> $comp
#>         ppron ipron article adverb conj  prep auxverb
#> twitter  9.02   4.6    5.58   5.13 4.19 11.88    8.27
mean(lsm_twitter$sim)
#> [1] 0.7287252

# 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")]
#> $comp.type
#> [1] "auto: nytimes"
#> 
#> $comp
#>         ppron ipron article adverb conj  prep auxverb
#> nytimes  3.56  3.84    9.08   2.76 4.85 14.27    5.11
mean(lsm_auto$sim)
#> [1] 0.6528981

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")]
#> $comp.type
#> [1] "colMeans(prompts[, -(1:2)])"
#> 
#> $comp
#>     ppron     ipron   article    adverb      conj      prep   auxverb 
#>  8.788269  8.319490  9.005891  9.928840  9.000049 11.971418  8.663632
mean(lsm_prmed$sim)
#> [1] 0.8240766

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")]
#> $comp.type
#> [1] "study group mean"
#> 
#> $comp
#>             ppron    ipron  article   adverb      conj      prep  auxverb
#> study 1 10.387083 9.998128 10.89054 9.661167  9.701703  9.631466 10.81388
#> study 2  8.867258 9.794513 10.72506 9.276561 10.417533 10.203479 10.11886
tapply(lsm_topics$sim[, 2], lsm_topics$sim[, 1], mean)
#>   study 1   study 2 
#> 0.8397258 0.8295869

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$comp.type
#> [1] "prompts"
lsm$comp[, 1:6]
#>                       ppron     ipron   article    adverb      conj      prep
#> study 1 prompt 1  4.3998259  2.712729 18.260100 12.171985 17.554020 13.741453
#> study 1 prompt 2 11.0212682  9.010699  3.476042  6.343701  9.610220 10.705954
#> study 1 prompt 3  0.2509456  9.023202 12.049708 11.872618  6.256611 10.974742
#> study 2 prompt 1  9.9777149  8.869178  2.547954 11.451805  9.936199 16.494196
#> study 2 prompt 2 12.4862109  7.785202  7.911950  4.781826  6.692844 10.448152
#> study 2 prompt 3 14.5936464 12.515928  9.789592 12.951105  3.950401  9.464012
lsm$sim[1:10, ]
#>                  g1  canberra
#> 1  study 1 prompt 3 0.8012121
#> 2  study 1 prompt 2 0.6868380
#> 3  study 1 prompt 1 0.5301802
#> 4  study 1 prompt 2 0.8035201
#> 5  study 1 prompt 3 0.7463066
#> 6  study 1 prompt 1 0.6832114
#> 7  study 1 prompt 3 0.6006250
#> 8  study 1 prompt 2 0.8803764
#> 9  study 1 prompt 3 0.6254127
#> 10 study 1 prompt 1 0.6042073

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$comp[1:10, ]
#>               ppron     ipron   article    adverb      conj      prep   auxverb
#> pair 01 a  8.564288  9.949453  8.928616 12.470831  9.459870  9.933365 10.816552
#> pair 01 b  9.377025 11.051046 14.588328  6.087776  7.930458 12.884964 13.916264
#> pair 02 b 10.620362  7.332231 10.082283  7.130964  7.690392 13.076658  8.714698
#> pair 02 a 14.407274  8.340292  9.359569  7.083569 13.081901  7.773880 13.112972
#> pair 03 a  7.444269  3.900478 10.779339 12.156017  3.872598 11.340337  7.876518
#> pair 03 b 11.320499  8.552962  9.903812  9.912013  7.677445 13.555640  6.019170
#> pair 04 a 11.321233 10.526088 14.155240 16.248495 14.907900 12.206726  8.395467
#> pair 04 b 11.426692 13.527806  7.683133  7.759858  8.772567  4.136878 11.797016
#> pair 05 b 10.285925  8.686241 10.286741  8.083254  8.261563 11.011969 12.032082
#> pair 05 a  8.625059 12.040857 13.744285  8.449484 11.089647 10.483572 10.015284
interlsm$sim[1:10, ]
#>           g1  canberra
#> 1  pair 01 a 0.9042582
#> 2  pair 01 b 0.8056024
#> 3  pair 01 b 0.8388374
#> 4  pair 01 b 0.8598558
#> 5  pair 01 a 0.9206056
#> 6  pair 02 b 0.8881149
#> 7  pair 02 a 0.7460497
#> 8  pair 02 b 0.9080004
#> 9  pair 02 a 0.8404195
#> 10 pair 02 b 0.8080563

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, ]
#>                 group  canberra
#> 1 <-> 2, 3, 4 pair 01 0.8172320
#> 2, 3, 4 <-> 5 pair 01 0.8319373
#> 6 <-> 7       pair 02 0.7346971
#> 7 <-> 8       pair 02 0.6880491
#> 8 <-> 9       pair 02 0.7182612
#> 9 <-> 10      pair 02 0.7030160
#> 10 <-> 11     pair 02 0.6786581
#> 12 <-> 13     pair 03 0.8350373
#> 13 <-> 14     pair 03 0.7173501
#> 14 <-> 15     pair 03 0.6857867

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, ]
#>             group  canberra
#> 1 <-> 2   pair 01 0.6506300
#> 4 <-> 5   pair 01 0.8491029
#> 6 <-> 7   pair 02 0.7346971
#> 7 <-> 8   pair 02 0.6880491
#> 8 <-> 9   pair 02 0.7182612
#> 9 <-> 10  pair 02 0.7030160
#> 10 <-> 11 pair 02 0.6786581
#> 12 <-> 13 pair 03 0.8350373
#> 13 <-> 14 pair 03 0.7173501
#> 14 <-> 15 pair 03 0.6857867

Brought to you by the Language Use and Social Interaction lab at Texas Tech University
Brought to you by the Language Use and Social Interaction lab at Texas Tech University