| Title: | Functions for Multi-Dimensional Analysis |
| Version: | 1.0.1 |
| Date: | 2025-09-22 |
| Description: | Multi-Dimensional Analysis (MDA) is an adaptation of factor analysis developed by Douglas Biber (1992) <doi:10.1007/BF00136979>. Its most common use is to describe language as it varies by genre, register, and use. This package contains functions for carrying out the calculations needed to describe and plot MDA results: dimension scores, dimension means, and factor loadings. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.3.2 |
| Imports: | dplyr, ggplot2, ggpubr, ggrepel, nFactors, stats, tidyr, viridis |
| Depends: | R (≥ 2.10) |
| Suggests: | rmarkdown, knitr, corrplot, kableExtra, tidyverse, testthat (≥ 3.0.0) |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-09-30 19:51:17 UTC; runner |
| Author: | David Brown  [aut,
cre],
Alex Reinhart
[aut] |
| Maintainer: | David Brown <dwb2@andrew.cmu.edu> |
| Repository: | CRAN |
| Date/Publication: | 2025-10-07 18:00:02 UTC |
Create boxplot for multi-dimensional analysis
Description
Combine scaled vectors of the relevant factor loadings and boxplots of dimension scores.
Usage
boxplot_mda(mda_data, n_factor = 1)
Arguments
mda_data |
An mda data.frame produced by the |
n_factor |
The factor to be plotted. |
Value
A combined plot of scaled vectors and boxplots.
See Also
Conduct multi-dimensional analysis
Description
Multi-Dimensional Analysis is a statistical procedure developed by Biber and is commonly used in descriptions of language as it varies by genre, register, and task. The procedure is a specific application of factor analysis, which is used as the basis for calculating a 'dimension score' for each text.
Usage
mda_loadings(obs_by_group, n_factors, cor_min = 0.2, threshold = 0.35)
Arguments
obs_by_group |
A data frame containing exactly 1 categorical (factor) variable and multiple continuous (numeric) variables. Each row represents one document/observation. |
n_factors |
The number of factors to be calculated in the factor analysis. |
cor_min |
The correlation threshold for including variables in the factor analysis. Variables whose (absolute) Pearson correlation with any other variable is greater than this threshold will be included in the factor analysis. Set to 0 to disable thresholding. |
threshold |
The loading threshold above which variables should be included in factor score calculations. Set to 0 to include all variables. |
Details
MDA is fundamentally factor analysis using the promax rotation, applied to
the numeric variables in obs_by_group. However, MDA adds two screening steps:
Only variables with a nontrivial correlation with any other variable are included; the correlation threshold is configurable with the
cor_minargument.The factor scores are based only on variables whose loadings are greater (in absolute value) than the
thresholdargument. (Variables are standardized to ensure loadings are comparable.)
These two choices eliminate variables that are uncorrelated with others, and essentially enforce sparsity in each factor, ensuring it is loaded only on a smaller set of variables.
Value
An mda data frame containing one row per document, containing
factor scores for each document. Attributes include the number of factors
(n_factors), the correlation threshold (threshold), the factor loadings
(loadings), and the mean factor score for each group (group_means).
References
Biber (1988). Variation across Speech and Writing. Cambridge University Press.
Biber (1992). "The multi-dimensional approach to linguistic analyses of genre variation: An overview of methodology and findings." Computers and the Humanities 26 (5/6), 331-345. doi:10.1007/BF00136979
See Also
screeplot_mda(), stickplot_mda(), boxplot_mda()
Examples
# Extract the subject area from each document ID and use it as the grouping
# variable
micusp_biber$doc_id <- factor(substr(micusp_biber$doc_id, 1, 3))
m <- mda_loadings(micusp_biber, n_factors = 2)
attr(m, "group_means")
heatmap_mda(m)
MICUSP corpus tagged with pseudobibeR features
Description
The Michigan Corpus of Upper-Level Student Papers (MICUSP) contains 828 student papers. Here each document is tagged with Biber features using the pseudobibeR package. Type-to-token ratio is calculated using the moving average type-to-token ratio (MATTR).
Usage
micusp_biber
Format
A data frame with 828 rows and 68 columns:
- doc_id
Document ID (from MICUSP)
- f_01_past_tense
Rate of past tense per 1,000 tokens
- f_02_perfect_aspect
Rate of perfect aspect per 1,000 tokens
- f_03_present_tense
Rate of present tense per 1,000 tokens
- f_04_place_adverbials
Rate of place adverbials per 1,000 tokens
- f_05_time_adverbials
Rate of time adverbials per 1,000 tokens
- f_06_first_person_pronouns
Rate of first person pronouns per 1,000 tokens
- f_07_second_person_pronouns
Rate of second person pronouns per 1,000 tokens
- f_08_third_person_pronouns
Rate of third person pronouns per 1,000 tokens
- f_09_pronoun_it
Rate of pronoun 'it' per 1,000 tokens
- f_10_demonstrative_pronoun
Rate of demonstrative pronouns per 1,000 tokens
- f_11_indefinite_pronouns
Rate of indefinite pronouns per 1,000 tokens
- f_12_proverb_do
Rate of proverb 'do' per 1,000 tokens
- f_13_wh_question
Rate of wh-questions per 1,000 tokens
- f_14_nominalizations
Rate of nominalizations per 1,000 tokens
- f_15_gerunds
Rate of gerunds per 1,000 tokens
- f_16_other_nouns
Rate of other nouns per 1,000 tokens
- f_17_agentless_passives
Rate of agentless passives per 1,000 tokens
- f_18_by_passives
Rate of by-passives per 1,000 tokens
- f_19_be_main_verb
Rate of 'be' as main verb per 1,000 tokens
- f_20_existential_there
Rate of existential 'there' per 1,000 tokens
- f_21_that_verb_comp
Rate of that-verb complements per 1,000 tokens
- f_22_that_adj_comp
Rate of that-adjective complements per 1,000 tokens
- f_23_wh_clause
Rate of wh-clauses per 1,000 tokens
- f_24_infinitives
Rate of infinitives per 1,000 tokens
- f_25_present_participle
Rate of present participles per 1,000 tokens
- f_26_past_participle
Rate of past participles per 1,000 tokens
- f_27_past_participle_whiz
Rate of past participle whiz-deletions per 1,000 tokens
- f_28_present_participle_whiz
Rate of present participle whiz-deletions per 1,000 tokens
- f_29_that_subj
Rate of that-subject clauses per 1,000 tokens
- f_30_that_obj
Rate of that-object clauses per 1,000 tokens
- f_31_wh_subj
Rate of wh-subject clauses per 1,000 tokens
- f_32_wh_obj
Rate of wh-object clauses per 1,000 tokens
- f_33_pied_piping
Rate of pied-piping per 1,000 tokens
- f_34_sentence_relatives
Rate of sentence relatives per 1,000 tokens
- f_35_because
Rate of 'because' per 1,000 tokens
- f_36_though
Rate of 'though' per 1,000 tokens
- f_37_if
Rate of 'if' per 1,000 tokens
- f_38_other_adv_sub
Rate of other adverbial subordinators per 1,000 tokens
- f_39_prepositions
Rate of prepositions per 1,000 tokens
- f_40_adj_attr
Rate of attributive adjectives per 1,000 tokens
- f_41_adj_pred
Rate of predicative adjectives per 1,000 tokens
- f_42_adverbs
Rate of adverbs per 1,000 tokens
- f_43_type_token
Type-token ratio (MATTR)
- f_44_mean_word_length
Mean word length
- f_45_conjuncts
Rate of conjuncts per 1,000 tokens
- f_46_downtoners
Rate of downtoners per 1,000 tokens
- f_47_hedges
Rate of hedges per 1,000 tokens
- f_48_amplifiers
Rate of amplifiers per 1,000 tokens
- f_49_emphatics
Rate of emphatics per 1,000 tokens
- f_50_discourse_particles
Rate of discourse particles per 1,000 tokens
- f_51_demonstratives
Rate of demonstratives per 1,000 tokens
- f_52_modal_possibility
Rate of possibility modals per 1,000 tokens
- f_53_modal_necessity
Rate of necessity modals per 1,000 tokens
- f_54_modal_predictive
Rate of predictive modals per 1,000 tokens
- f_55_verb_public
Rate of public verbs per 1,000 tokens
- f_56_verb_private
Rate of private verbs per 1,000 tokens
- f_57_verb_suasive
Rate of suasive verbs per 1,000 tokens
- f_58_verb_seem
Rate of 'seem' verbs per 1,000 tokens
- f_59_contractions
Rate of contractions per 1,000 tokens
- f_60_that_deletion
Rate of that-deletions per 1,000 tokens
- f_61_stranded_preposition
Rate of stranded prepositions per 1,000 tokens
- f_62_split_infinitve
Rate of split infinitives per 1,000 tokens
- f_63_split_auxiliary
Rate of split auxiliaries per 1,000 tokens
- f_64_phrasal_coordination
Rate of phrasal coordination per 1,000 tokens
- f_65_clausal_coordination
Rate of clausal coordination per 1,000 tokens
- f_66_neg_synthetic
Rate of synthetic negation per 1,000 tokens
- f_67_neg_analytic
Rate of analytic negation per 1,000 tokens
Source
Michigan Corpus of Upper-Level Student Papers, https://elicorpora.info/main, tagged with the pseudobibeR package.
Scree plot for multi-dimensional analysis
Description
The scree plot shows each factor along the X axis, and the proportion of common variance explained by that factor on the Y axis. The proportion of common variance explained is given by the factor eigenvalue.
Usage
screeplot_mda(obs_by_group, cor_min = 0.2)
Arguments
obs_by_group |
A data frame containing 1 categorical (factor) variable and continuous (numeric) variables. |
cor_min |
The correlation threshold for including variables in the factor analysis. |
Details
A wrapper for the nFactors:nScree() and nFactors::plotnScree() functions.
Value
Nothing returned
See Also
Plots of MDA factor group means and loadings
Description
Stick plots show each group's mean loading along a factor, plotted along a
positive/negative cline. Heatmaps show each variable's loading on a factor.
stickplot_mda() produces just a stick plot, while heatmap_mda() places a
heatmap alongside the stick plot.
Usage
stickplot_mda(mda_data, n_factor = 1)
heatmap_mda(mda_data, n_factor = 1)
Arguments
mda_data |
An mda data frame produced by the |
n_factor |
Index of the factor to be plotted. |
Value
ggplot object