This example demonstrates a few ways to specify comparisons and groups in lingmatch.
Built with R 4.2.2 on January 22 2023
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 6.864407 13.859885 6.938533 9.984956 12.977499 11.781433
#> 2 study 1 prompt 2 12.113190 8.793740 9.987835 3.679203 6.488306 9.610828
#> 3 study 1 prompt 3 4.906687 12.534891 7.852603 8.653199 13.482843 7.836163
#> 4 study 2 prompt 1 7.747281 3.858712 10.086379 10.800065 9.719417 11.361967
#> 5 study 2 prompt 2 5.976320 10.213058 4.272967 13.107793 5.597550 9.089050
# 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 1 b 0.7020615 10.491647 3.779056 -2.757235
#> 2 study 1 pair 01 prompt 2 a 11.3947600 8.548593 10.834191 7.965312
#> 3 study 1 pair 01 prompt 1 a 10.9304932 7.945934 9.669309 5.248526
#> 4 study 1 pair 01 prompt 3 a 10.7997755 17.222643 10.715310 8.092856
#> 5 study 1 pair 01 prompt 1 a 12.4622082 7.465270 14.939144 8.013883Matching 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"
#>
#> $comp
#> ppron ipron article adverb conj prep auxverb
#> 10.079219 10.416393 9.645455 8.946775 10.435997 10.173685 9.635552
# and maybe the average similarity score
mean(lsm_mean$sim)
#> [1] 0.8312954This 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] 0.7237678
# 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: novels"
#>
#> $comp
#> ppron ipron article adverb conj prep auxverb
#> novels 10.35 4.79 8.35 4.17 6.28 14.27 7.77
mean(lsm_auto$sim)
#> [1] 0.7470567External 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)])"
#>
#> $comp
#> ppron ipron article adverb conj prep auxverb
#> 7.642559 8.651467 7.688486 9.920160 8.655075 9.818750 9.434635
mean(lsm_prmed$sim)
#> [1] 0.8166922Group 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"
#>
#> $comp
#> ppron ipron article adverb conj prep auxverb
#> study 1 9.970332 10.67027 9.406745 8.793292 9.726499 9.769428 9.298925
#> study 2 10.183835 10.17247 9.874803 9.094240 11.117671 10.562090 9.958977
tapply(lsm_topics$sim[, 2], lsm_topics$sim[, 1], mean)
#> study 1 study 2
#> 0.8177467 0.8449237This type of group variable is just splitting the data, and performing the same comparisons within splits.
Matching with other texts
The previous comparisons were all with standards, where the LSM score could be interpreted as indicating a more or less generic language style (as defined by the comparison and grouping).
Condition ID
Here, prompts constitute our experimental conditions. We have 3 unique prompt IDs, but 6 unique prompts, since each study had its own set, so we need the study and prompt ID to appropriately match prompts:
lsm <- lingmatch(data, prompts, group = c("study", "prompt"), type = "lsm")
lsm$comp.type
#> [1] "prompts"
lsm$comp[, 1:6]
#> ppron ipron article adverb conj prep
#> study 1 prompt 1 6.864407 13.859885 6.938533 9.984956 12.977499 11.781433
#> study 1 prompt 2 12.113190 8.793740 9.987835 3.679203 6.488306 9.610828
#> study 1 prompt 3 4.906687 12.534891 7.852603 8.653199 13.482843 7.836163
#> study 2 prompt 1 7.747281 3.858712 10.086379 10.800065 9.719417 11.361967
#> study 2 prompt 2 5.976320 10.213058 4.272967 13.107793 5.597550 9.089050
#> study 2 prompt 3 8.247466 2.648515 6.992599 13.295743 3.664837 9.233055
lsm$sim[1:10, ]
#> g1 canberra
#> 1 study 1 prompt 1 0.4196806
#> 2 study 1 prompt 2 0.7421996
#> 3 study 1 prompt 1 0.7849499
#> 4 study 1 prompt 3 0.7358929
#> 5 study 1 prompt 1 0.7092547
#> 6 study 1 prompt 3 0.7825726
#> 7 study 1 prompt 2 0.7275835
#> 8 study 1 prompt 2 0.7250848
#> 9 study 1 prompt 3 0.6666420
#> 10 study 1 prompt 1 0.7949822Here, 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$comp[1:10, ]
#> ppron ipron article adverb conj prep
#> pair 01 b 0.7020615 10.491647 3.779056 -2.757235 4.742780 5.672667
#> pair 01 a 11.3968092 10.295610 11.539489 7.330144 6.592874 10.141503
#> pair 02 b 9.6657169 9.840502 10.462939 7.642948 11.012827 9.891918
#> pair 03 a 11.9535745 12.435491 6.445858 12.740861 12.616633 6.268792
#> pair 03 b 10.2310641 10.529372 10.019987 8.087845 8.769773 10.779999
#> pair 04 b 11.3083514 5.863618 11.835957 7.323427 13.502936 13.973873
#> pair 05 a 9.6600465 11.391218 9.662540 8.387847 9.966338 9.799975
#> pair 05 b 10.4133382 9.882321 12.424708 13.537444 12.087927 10.672846
#> pair 06 a 17.1951134 9.296324 4.053924 13.062216 14.760465 5.377913
#> pair 06 b 10.1448893 7.680004 7.372897 8.449567 5.986017 7.971165
#> auxverb
#> pair 01 b -0.2898078
#> pair 01 a 15.0987578
#> pair 02 b 9.7237697
#> pair 03 a 11.5777332
#> pair 03 b 9.5540010
#> pair 04 b 10.5615227
#> pair 05 a 6.7583120
#> pair 05 b 11.2632321
#> pair 06 a 12.5754275
#> pair 06 b 17.8446326
interlsm$sim[1:10, ]
#> g1 canberra
#> 1 pair 01 b 1.0000000
#> 2 pair 01 a 0.8424788
#> 3 pair 01 a 0.8776799
#> 4 pair 01 a 0.8940391
#> 5 pair 01 a 0.8750118
#> 6 pair 02 b 0.7509222
#> 7 pair 02 b 0.7398585
#> 8 pair 02 b 0.8375352
#> 9 pair 02 b 0.8351512
#> 10 pair 03 a 0.8812831Matching 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$sim[1:10, ]
#> group canberra
#> 1 <-> 2, 3, 4, 5 pair 01 0.4506036
#> 6, 7, 8, 9 pair 02 1.0000000
#> 10 <-> 11 pair 03 0.7689010
#> 11 <-> 12 pair 03 0.7746189
#> 12 <-> 13, 14, 15, 16 pair 03 0.8756456
#> 17, 18 pair 04 1.0000000
#> 19, 20, 21, 22 <-> 23, 24 pair 05 0.8885610
#> 23, 24 <-> 25 pair 05 0.8024998
#> 26, 27 <-> 28 pair 06 0.7645176
#> 29, 30 <-> 31 pair 07 0.8678496The rownames of sim show the row numbers that are being
compared, with some being aggregated if the same speaker takes multiple
turns in a row. You could also just compare edges by adding
agg = FALSE:
lingmatch(
data, "seq",
group = c("pair", "speaker"), type = "lsm", agg = FALSE
)$sim[1:10, ]
#> group canberra
#> 1 <-> 2 pair 01 0.4455924
#> 6, 7, 8, 9 pair 02 1.0000000
#> 10 <-> 11 pair 03 0.7689010
#> 11 <-> 12 pair 03 0.7746189
#> 12 <-> 13 pair 03 0.8514062
#> 17, 18 pair 04 1.0000000
#> 22 <-> 23 pair 05 0.8166640
#> 24 <-> 25 pair 05 0.7569052
#> 27 <-> 28 pair 06 0.6722367
#> 30 <-> 31 pair 07 0.7898381
Brought to you by the Language Use and Social Interaction lab at Texas Tech University