Package: soc.ca 0.8.1

Anton Grau Larsen

soc.ca: Specific Correspondence Analysis for the Social Sciences

Specific and class specific multiple correspondence analysis on survey-like data. Soc.ca is optimized to the needs of the social scientist and presents easily interpretable results in near publication ready quality.

Authors:Anton Grau Larsen and Jacob Lunding with contributions from Christoph Ellersgaard and Stefan Andrade

soc.ca_0.8.1.tar.gz
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soc.ca_0.8.1.tgz(r-4.4-any)soc.ca_0.8.1.tgz(r-4.3-any)
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soc.ca.pdf |soc.ca.html✨
soc.ca/json (API)
NEWS

# Install 'soc.ca' in R:

install.packages('soc.ca', repos = c('https://rsoc.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/rsoc/soc.ca/issues

Datasets:

directors - Directors dataset
moschidis - Moschidis example
pe13 - The Field of the Danish Power Elite
political_space97 - French Political Space example
taste - Taste dataset

On CRAN:

4.44 score 14 stars 49 scripts 284 downloads 57 exports 136 dependencies

Last updated 8 months agofrom:753cee0368. Checks:OK: 1 ERROR: 6. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Oct 13 2024
R-4.5-win	ERROR	Oct 13 2024
R-4.5-linux	ERROR	Oct 13 2024
R-4.4-win	ERROR	Oct 13 2024
R-4.4-mac	ERROR	Oct 13 2024
R-4.3-win	ERROR	Oct 13 2024
R-4.3-mac	ERROR	Oct 13 2024

Exports:%>%add.categories add.density add.ellipse add.ind add.quadrant.labels add.to.label assign.label average.coord balance breakdown.variance contribution cowboy_cut create.quadrant csa.all csa.measures ellipses export export.label extract_cases extract_cats extract_ind extract_mod extract_sup get.category.relations headings ind.explorer indicator indicator.to.long invert map.active map.add map.array map.ca.base map.csa.all map.csa.mca map.csa.mca.array map.ctr map.density map.ellipse map.ellipse.array map.ind map.mod map.path map.select map.sup mca.eigen.check mca.triads min_cut print.soc.mca prune.mca randomize.mca soc.csa soc.mca supplementary.categories supplementary.individuals variance

Dependencies:abind askpass backports base64enc boot broom bslib cachem car carData checkmate cli cluster colorspace commonmark cowplot cpp11 crayon crosstalk data.table Deriv descriptio digest doBy dplyr DT ellipse emmeans estimability evaluate FactoMineR fansi farver fastmap flashClust flextable fontawesome fontBitstreamVera fontLiberation fontquiver forcats Formula fs GDAtools gdtools generics ggplot2 ggpp ggrepel glue gridExtra gtable highr htmlTable htmltools htmlwidgets httpuv igraph isoband jquerylib jsonlite knitr labeling later lattice lazyeval leaps lifecycle lme4 lubridate magrittr MASS Matrix MatrixModels memoise mgcv microbenchmark mime minqa modelr multcompView munsell mvtnorm nlme nloptr nnet numDeriv officer openssl pbkrtest pillar pkgconfig plyr polynom promises purrr quantreg R6 ragg rappdirs RColorBrewer Rcpp RcppEigen reshape2 rlang rmarkdown rstudioapi sass scales scatterplot3d shiny sourcetools SparseM stringi stringr survival sys systemfonts textshaping tibble tidyr tidyselect timechange tinytex utf8 uuid vctrs viridisLite withr xfun xml2 xtable xts yaml zip zoo

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Add a layer of cases (individuals) to an mca map	add.cases add.ind
Add a layer of categories (modalities) to an mca map	add.categories
Add a new layer of points on top of an existing plot with output from the min_cut function	add.count
Add a layer with density curves to an mca map.	add.density
Add a layer with concentration ellipses to an mca map.	add.ellipse
Annotate labels to the quadrants of an MCA or any ggplot2 based quadrant plot.	add.quadrant.labels
Add values to label	add.to.label
Assign new labels	assign.label
Average coordinates	average.coord
Contribution balance	balance
Breakdown of variance by group	breakdown.variance
Summaries of contribution values	contribution
Cut ordinal variables	cowboy_cut
Create categories according to the quadrant position of each individual	create.quadrant
Multiple Class Specific Correspondence Analysis on all values in a factor	csa.all
CSA measures	csa.measures
Directors dataset	directors
Calculate concentraion ellipses	ellipses
Export results from soc.ca	export
Exports the labels of a soc.ca object into a csv file.	export.label
Extract individuals	extract_cases extract_ind
Extract coordinates for the categories from an soc.mca	extract_cats extract_mod
Extract supplementary categories from an soc.mca	extract_sup
Get and calculate the relationships and oppositions between each pair of categories	get.category.relations
Calculate contributions per heading	headings
Explore the cloud of individuals	ind.explorer
Indicator matrix	indicator
Pivot the indicator matrix from an MCA to long format	indicator.to.long
Invert the direction of coordinates	invert
Map the active modalities	map.active
Add points to an existing map created by one of the soc.ca mapping functions.	map.add
Array of maps	map.array
Create the base of a soc.ca map	map.ca.base
Array of several CSA maps	map.csa.all
Map the coordinates of the individuals in a CSA and its MCA	map.csa.mca
CSA-MCA array	map.csa.mca.array
Map the most contributing modalities	map.ctr
Density plot for the cloud of individuals	map.density
Concentration ellipses	map.ellipse
Ellipse array	map.ellipse.array
Map the individuals of a soc.ca analysis	map.ind
Map all modalities	map.mod
Map path along an ordered variable	map.path
Map select modalities and individuals	map.select
Map the supplementary modalities	map.sup
MCA Eigenvalue check	mca.eigen.check
Compare MCA's with triads	mca.triads
Cut a continuous variable into categories with a specified minimum	min_cut
Moschidis example	moschidis
The Field of the Danish Power Elite	pe13
French Political Space example	political_space97
Print soc.ca objects	print.soc.mca
Remove unnecessary variables from an MCA	prune.mca
Create a randomized mca on the basis of an existing mca	randomize.mca
Soc.ca a package for specific correspondence analysis	soc.ca
Class Specific Multiple Correspondence Analysis	soc.csa
soc.mca 'soc.mca' performs a specific multiple correspondence analysis on a data.frame of factors, where cases are rows and columns are variables.	soc.mca
Supplementary coordinates for a data.frame of factors	supplementary.categories
Add supplementary individuals to a result object	supplementary.individuals
Taste dataset	taste
Convert to MCA class from FactoMineR	to.MCA
Variance table	variance
Check if data is valid for soc.mca	what.is.x