| Title: | Specific Correspondence Analysis for the Social Sciences |
|---|---|
| Description: | 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 |
| Maintainer: | Anton Grau Larsen <[email protected]> |
| License: | GPL-3 |
| Version: | 0.8.1 |
| Built: | 2025-02-10 05:16:09 UTC |
| Source: | https://github.com/rsoc/soc.ca |
Add a layer of cases (individuals) to an mca map
add.cases( object, dim = c(1, 2), ind = extract_ind(object, dim), mapping = aes(), ... ) add.ind( object, dim = c(1, 2), ind = extract_ind(object, dim), mapping = aes(), ... )add.cases( object, dim = c(1, 2), ind = extract_ind(object, dim), mapping = aes(), ... ) add.ind( object, dim = c(1, 2), ind = extract_ind(object, dim), mapping = aes(), ... )
object |
a soc.mca result object |
dim |
a numeric vector with the plotted dimensions |
ind |
a data.frame with coordinates of cases as produced by extract_ind. This controls the plotted points. |
mapping |
a call to aes from the ggplot2 package. Here you can map aesthetics to variables such as color, fill, alpha, size and shape. |
... |
further arguments are passed on to geom_point() |
a ggplot2 object that can be added to an existing plot like those produced by map.ca.base
example(soc.mca) map.ca.base() + add.cases(result)example(soc.mca) map.ca.base() + add.cases(result)
Add a layer of categories (modalities) to an mca map
The presets adds, replaces or filters from the categories in cats. "ctr" returns all categories contributing above average to the plane defined in dim. "sup" returns the supplementary categories from object. "all" returns both supplementary and active categories.The presets adds, replaces or filters from the categories in cats. "ctr" returns all categories contributing above average to the plane defined in dim. "sup" returns the supplementary categories from object. "all" returns both supplementary and active categories.
object |
a soc.mca result object |
preset |
a character string selecting among presets. If "active" - no change is made to "cats". |
dim |
a numeric vector with the dimensions for the plane |
cats |
a data.frame with coordinates of categories as produced by extract_mod or extract_sup. This controls the plotted points. |
mapping |
a call to aes from the ggplot2 package. Here you can map aesthetics to variables such as color, alpha, size and family. |
repel |
if TRUE label position is adjusted to lower overlap |
check_overlap |
if TRUE overlapping categories are removed |
points |
if TRUE points are plotted |
... |
further arguments are passed onto geom_text or geom_text_repel |
a ggplot2 object that can be added to an existing plot like those produced by map.ca.base
example(soc.mca) map.ca.base() + add.categories(result, check_overlap = TRUE) map.ca.base() + add.categories(result, preset = "all", mapping = aes(color = type, label = Modality), repel = TRUE)example(soc.mca) map.ca.base() + add.categories(result, check_overlap = TRUE) map.ca.base() + add.categories(result, preset = "all", mapping = aes(color = type, label = Modality), repel = TRUE)
Add a new layer of points on top of an existing plot with output from the min_cut function
add.count(x, p, label = TRUE, ...)add.count(x, p, label = TRUE, ...)
x |
a matrix created by the min_cut function |
p |
is a ggplot object, preferably from one of the mapping functions in soc.ca |
label |
if TRUE the labels of points will be shown |
... |
further arguments are passed on to geom_path, geom_point and geom_text |
Add a layer with density curves to an mca map.
add.density( object, dim = c(1, 2), ind = extract_ind(object, dim), mapping = aes(), ... )add.density( object, dim = c(1, 2), ind = extract_ind(object, dim), mapping = aes(), ... )
object |
a soc.mca result object |
dim |
a numeric vector with the dimensions for the plane |
ind |
a data.frame with coordinates of cases as produced by extract_ind. This controls the points that are used for the density curves. |
mapping |
a call to aes from the ggplot2 package. Here you can map aesthetics to variables such as color, fill, alpha, size and linetype. |
... |
further arguments are passed onto geom_density_2d |
Add a layer with concentration ellipses to an mca map.
add.ellipse( object, var = NULL, draw = unique(var), dim = c(1, 2), el = ellipses(object, var = var, dim = dim), mapping = aes(color = Category), draw.axis = TRUE, ... )add.ellipse( object, var = NULL, draw = unique(var), dim = c(1, 2), el = ellipses(object, var = var, dim = dim), mapping = aes(color = Category), draw.axis = TRUE, ... )
object |
a soc.mca result object |
var |
a factor |
draw |
a character vector with the levels to draw ellipses for |
dim |
a numeric vector with the dimensions for the plane |
el |
a data.frame produced by the ellipses function. |
mapping |
a call to aes from the ggplot2 package. Here you can map aesthetics to variables such as color, fill, alpha, size and linetype. |
draw.axis |
if TRUE the axis within the concentration ellipse is drawn. |
... |
a ggplot2 object that can be added to an existing plot like those produced by map.ca.base
example(soc.mca) map.ca.base() + add.ind(result, mapping = aes(color = sup$Gender)) + add.ellipse(result, sup$Gender) map.ca.base() + add.ind(result, mapping = aes(color = sup$Age == "65+")) + add.ellipse(result, sup$Age == "65+", draw = "TRUE")example(soc.mca) map.ca.base() + add.ind(result, mapping = aes(color = sup$Gender)) + add.ellipse(result, sup$Gender) map.ca.base() + add.ind(result, mapping = aes(color = sup$Age == "65+")) + add.ellipse(result, sup$Age == "65+", draw = "TRUE")
This function is a convience function that uses annotate to easily create labels for the quadrants.
add.quadrant.labels( quadrant.labels = c("A", "B", "C", "D"), distance = "npc", geom = "label", color = "black", ... )add.quadrant.labels( quadrant.labels = c("A", "B", "C", "D"), distance = "npc", geom = "label", color = "black", ... )
quadrant.labels |
|
distance |
if equal to "npc" labels are positioned dynamically at the edges of the plot. see annotate. If a numeric vector it is interpreted as the distance to 0 on both X and Y. |
geom |
controls the annotation geom; usually you would use "text" or "label". |
color |
either a single value or 4 values that control the color of the labels |
... |
further arguments are passed onto annotate |
a ggplot2 layer that can be added to an existing ggplot object.
example(soc.mca) map.ind(result, point.size = 1) + add.quadrant.labels() labels <- c("Dominant:\nCultural fraction", "Dominant:\nEconomic fraction", "Dominated:\nEconomic fraction", "Dominated:\nCultural fraction") map.ca.base() + add.quadrant.labels(labels, geom = "text") map.ca.base() + add.ind(result, color = "grey80") + add.quadrant.labels(labels, geom = "text", distance = 1) map.ca.base() + add.categories(result, color = "grey50", check_overlap = TRUE) + add.quadrant.labels(labels, geom = "label", distance = 0.5, fill = c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3"), alpha = 0.3)example(soc.mca) map.ind(result, point.size = 1) + add.quadrant.labels() labels <- c("Dominant:\nCultural fraction", "Dominant:\nEconomic fraction", "Dominated:\nEconomic fraction", "Dominated:\nCultural fraction") map.ca.base() + add.quadrant.labels(labels, geom = "text") map.ca.base() + add.ind(result, color = "grey80") + add.quadrant.labels(labels, geom = "text", distance = 1) map.ca.base() + add.categories(result, color = "grey50", check_overlap = TRUE) + add.quadrant.labels(labels, geom = "label", distance = 0.5, fill = c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3"), alpha = 0.3)
Adds values to the end of the label of each modality.
add.to.label(object, value = "freq", prefix = "default", suffix = ")", dim = 1)add.to.label(object, value = "freq", prefix = "default", suffix = ")", dim = 1)
object |
is a soc.ca object |
value |
the type of values added to the labels. "freq" adds
frequencies, "mass" adds mass values to the active modalities, "ctr" adds contribution values to the active modalities, "cor" adds correlation values.
value also accepts any vector with the length of the number of active
modalities. "linebreak" adds a linebreak |
prefix |
if "default" an appropriate prefix is used |
suffix |
the suffix |
dim |
the dimension from which values are retrieved |
a soc.ca object with altered labels in names.mod and names.sup
example(soc.ca) result.label <- add.to.label(result) result.label$names.mod result.label <- add.to.label(result, value = "ctr", dim = 2) result.label$names.mod result.label <- add.to.label(result, value = result$variable, prefix = " - ", suffix = "") result.label$names.mod result.label <- add.to.label(result, value = "linebreak") result.label$names.mod map.ctr(result.label)example(soc.ca) result.label <- add.to.label(result) result.label$names.mod result.label <- add.to.label(result, value = "ctr", dim = 2) result.label$names.mod result.label <- add.to.label(result, value = result$variable, prefix = " - ", suffix = "") result.label$names.mod result.label <- add.to.label(result, value = "linebreak") result.label$names.mod map.ctr(result.label)
Assigns new labels to a soc.ca object. The input labels are defined in a .csv file created by the export.label function.
assign.label(object, file = FALSE, encoding = "UTF-8", sep = ",")assign.label(object, file = FALSE, encoding = "UTF-8", sep = ",")
object |
is a soc.ca object |
file |
is the path of the .csv file with the new labels. The file is preferably created by the export.label function |
encoding |
is the encoding of the imported file |
sep |
is the seperator used to create the imported .csv file |
To use this function first export the labels from your soc.mca analysis with the export.label function. Then open and edit the created file with your favorite spreadsheet editor, like LibreOffice Calc. Change labels in the "new.label" column to the desired values and save. Use the assign.label function but remember to assign the results into a new object or overwrite the existing object.
a soc.ca object with altered labels in object$names.mod, object$names.ind and
object$names.sup
Find the average coordinates for each category in a variable on two dimensions.
average.coord(object, x, dim = c(1, 2))average.coord(object, x, dim = c(1, 2))
object |
is soc.ca result object |
x |
is a variable of the same length and order as the active variables used to construct the soc.ca object |
dim |
is the two dimensions used |
a matrix with the mean points and frequencies of the given variable
example(soc.ca) average.coord(result, sup$Income)example(soc.ca) average.coord(result, sup$Income)
Calculates the balance of the contribution of each dimension. This measure indicates whether too much of a dimensions contribution is placed on either the + or - side of the dimension.
balance(object, act.dim = object$nd)balance(object, act.dim = object$nd)
object |
is a soc.ca class object |
act.dim |
is the number of active dimensions to be measured |
A matrix with the share of contribution on each side of 0 and their balance (+/-)
example(soc.ca) balance(result) balance(result, act.dim = 3)example(soc.ca) balance(result) balance(result, act.dim = 3)
Defining a partition of the cloud of individuals into groups, one can calculate the midpoints of the various groups. The total variance of the cloud of individuals can then be broken down to between–within variances, i.e. variance between the groups partitioning the cloud, and variance within the groups The ratio of the between-variance to the total variance is denoted by n2 (eta-square), and accounts for the percentage of variance 'explained' by the group-variable. (see Le Roux & Rouanet 2010, p. 20ff, 69, 114)
breakdown.variance(object, dim = 1:3, variable)breakdown.variance(object, dim = 1:3, variable)
object |
is a soc.ca class object |
dim |
the dimensions |
variable |
a factor in the same length and order as the active variables |
a matrix
Le Roux, Brigitte, and Henry Rouanet. 2010. Multiple Correspondence Analysis. Thousand Oaks, Calif.: Sage Publications.
example(soc.ca) breakdown.variance(result, dim = 1:3, variable = sup$Gender)example(soc.ca) breakdown.variance(result, dim = 1:3, variable = sup$Gender)
Different forms of contribution summaries for soc.ca objects. Results
are presented according to the specified mode
contribution( object, dim = 1, all = FALSE, indices = FALSE, mode = "sort", matrix.output = FALSE )contribution( object, dim = 1, all = FALSE, indices = FALSE, mode = "sort", matrix.output = FALSE )
object |
a soc.ca object |
dim |
the included dimensions |
all |
If TRUE returns all modalities instead of just those that contribute above average |
indices |
If TRUE; returns a vector with the row indices of the modalities or individuals |
mode |
indicates which form of output. Possible values: |
matrix.output |
if TRUE; returns output as a matrix instead of as printed output. |
Each mode prints different results:
"mod" |
Ranks all modalities according to their contribution |
"sort" |
Ranks all modalities according to their contribution and then sorts them according to their coordinates |
"ind" |
Ranks all individuals according to their contribution |
"variable" |
Sorts all modalities according to their variable and sums the contributions per variable |
The values reported:
Ctr |
Contribution values in percentage. Contribution values for individuals are reported in permille |
Coord |
Principal coordinates |
Cor |
The correlation with the dimension |
example(soc.ca) contribution(result) contribution(result, 2) contribution(result, dim = 3, all = TRUE) contribution(result, indices = TRUE) contribution(result, 1:2, mode = "variable")example(soc.ca) contribution(result) contribution(result, 2) contribution(result, dim = 3, all = TRUE) contribution(result, indices = TRUE) contribution(result, 1:2, mode = "variable")
If we are in a hurry and need to cut a lot of likert-scale or similar type of variables into MCA-friendly ordered factors this function comes in handy. cowboy_cut will try its best to create approx 3-5 categories, where the top and the bottom are smaller than the middle. Missing or other unwanted categories are recoded but still influence the categorization. So that when cowboy_cut tries to part the top of a variable with a threshold around 10 Make sure that levels are in the right order before cutting.
cowboy_cut(x, top.share = 0.1, bottom.share = 0.1, missing = "Missing")cowboy_cut(x, top.share = 0.1, bottom.share = 0.1, missing = "Missing")
x |
a factor |
top.share |
approximate share in top category |
bottom.share |
approximate share in bottom category |
missing |
a character vector with all the missing or unwanted categories. |
a recoded factor
Creates a vector from two dimensions from a soc.ca object. Labels are the
cardinal directions with the first designated dimension running East - West.
The center category is a circle defined by cut.radius.
create.quadrant( object, dim = c(1, 2), cut.min = -0.125, cut.max = 0.125, cut.radius = 0.25 )create.quadrant( object, dim = c(1, 2), cut.min = -0.125, cut.max = 0.125, cut.radius = 0.25 )
object |
a soc.ca class object |
dim |
the dimensions |
cut.min |
Minimum cut value |
cut.max |
Maximum cut value |
cut.radius |
Radius of the center category |
Returns a character vector with category memberships
example(soc.ca) create.quadrant(result, dim = c(2, 1)) table(create.quadrant(result, dim = c(1, 3), cut.radius = 0.5))example(soc.ca) create.quadrant(result, dim = c(2, 1)) table(create.quadrant(result, dim = c(1, 3), cut.radius = 0.5))
csa.all performs a class specific correspondence analysis for each
level in a factor variable. Returns a list with soc.csa objects and a list of
measures defined by csa.measures
csa.all(object, variable, dim = 1:5, ...)csa.all(object, variable, dim = 1:5, ...)
object |
is a soc.ca class object created with soc.mca |
variable |
a factor with the same length and order as the active variables that created the soc.ca object |
dim |
is the dimension analyzed |
... |
further arguments are directed to csa.measures |
results |
a list of soc.csa result objects |
cor |
a list of correlation matrixes |
cosines |
a list of matrixes with cosine values |
angles |
a list of matrixes with cosine angles between dimensions |
example(soc.ca) csa.all(result, taste$Age) csa.all(result, taste$Age)$measuresexample(soc.ca) csa.all(result, taste$Age) csa.all(result, taste$Age)$measures
Several measures for the evaluation of the relations between the dimensions of the CSA and the dimensions the of original MCA
csa.measures( csa.object, correlations = FALSE, cosines = TRUE, cosine.angles = TRUE, dim.mca = 1:5, dim.csa = 1:5, format = TRUE, ... )csa.measures( csa.object, correlations = FALSE, cosines = TRUE, cosine.angles = TRUE, dim.mca = 1:5, dim.csa = 1:5, format = TRUE, ... )
csa.object |
is a "soc.csa" class object created by the soc.csa function |
correlations |
if TRUE correlations calculated by the cor function is returned |
cosines |
if TRUE cosine similarities are returned |
cosine.angles |
if TRUE angles are calculated in the basis of the cosine values |
dim.mca |
the dimensions included from the original mca |
dim.csa |
the dimensions included from the csa |
format |
if TRUE results are formatted, rounded and printed for screen reading, if FALSE the raw numbers are returned |
... |
furhter arguments are send to the cor function |
A list of measures in either formatted or raw form.
example(soc.csa) csa.measures(res.csa) csa.measures(res.csa, correlations = FALSE, cosine.angles = FALSE, dim.mca = 1:5, format = FALSE)example(soc.csa) csa.measures(res.csa) csa.measures(res.csa, correlations = FALSE, cosine.angles = FALSE, dim.mca = 1:5, format = FALSE)
Prosopographical data on the top 100 CEO's from the 82 largest Danish corporations.
The directors dataset is prosopographical data collected from a wide array of sources on biographic and corporate information. Sources include the Danish variant of Who's Who (Blaa Bog), a private business information database (Greens Erhvervsinformation), journalistic portrait articles, article search engines, bibliographic databases and financial reports. CEOs from 82 corporations were selected according to their position as CEO in December 2007. 18 executives are included on other criteria, taking into account the magnitude of the corporations and issues regarding ownership and control, resulting in a final population of 100 CEOs. The 82 corporations have formal ownership and management located in Denmark and were selected through either financial capital, measured as having a turnover of over five billion DKK (650 million Eur.), or organizational capital, defined as having at least 5000 employees; 34 corporations were included on both criteria, 45 on financial capital and three on organizational capital alone. To avoid including investors, rather than executives, a minimum of 500 employees was also required, excluding 12 firms. Companies acting only as subsidiaries were also excluded. Data is for public use and no author permission is needed, but we would love to hear from you if you find the data useful. The following example is based on the analysis from the article: "A Very Economic Elite: The Case of the Danish Top CEOs".
Christoph Ellersgaard
Anton Grau Larsen
Ellersgaard, Christoph, Anton Grau Larsen, og Martin D. Munk. 2012. "A Very Economic Elite: The Case of the Danish Top CEOs". Sociology.
Ellersgaard, Christoph Houman, og Anton Grau Larsen. 2010. "Firmaets Maend". Master Thesis, Copenhagen: University of Copenhagen.
Ellersgaard, Christoph Houman, og Anton Grau Larsen. 2011. "Kulturel kapital blandt topdirektoerer i Danmark - En domineret kapitalform?" Dansk Sociologi 22(3):9-29.
Larsen, Anton Grau, og Christoph Houman Ellersgaard. 2012. "Status og integration paa magtens felt for danske topdirektoerer". Praktiske Grunde. Nordisk tidsskrift for kultur- og samfundsvidenskab 2012(2-3).
## Not run: data(directors) attach(directors) active <- data.frame(careerprofile_maclean_cat, careerfoundation_maclean_cat, years_between_edu_dir_cat, time_in_corp_before_ceo_cat, age_as_ceo_cat, career_changes_cat2, mba, abroad, hd, phd, education, author, placeofbirth, familyclass_bourdieu, partnersfamily_in_whoswho, family_in_whoswho) sup <- data.frame(size_prestige, ownership_cat_2, sector, location) id <- navn options(passive = c("MISSING", "Missing", "Irrelevant", "residence_value_cat2: Udlandet")) result <- soc.mca(active, sup, id) result # Contribution contribution(result, 1) contribution(result, 2) contribution(result, 3) contribution(result, 1, all = TRUE) contribution(result, 1, indices = TRUE) contribution(result, 1, mode = "mod") contribution(result, mode = "variable") # Individuals contribution(result, 1, mode = "ind") contribution(result, 2, mode = "ind") # Table of variance variance(result) # Invert result <- invert(result, c(1, 2, 3)) # Export and assign label # export.label(result) # result <- assign.label(result, # file = "https://raw.github.com/Rsoc/soc.ca/master/extra/director_labels.csv") # Add.n result <- add.to.label(result) contribution(result, 2) # The result object or "soc.ca" object str(result) dim1 <- result$coord.ind[, 1] qplot(dim1) # Quadrant quad <- create.quadrant(result) table(quad) quad <- create.quadrant(result, cut.min = 0, cut.max = 0) table(quad) # Map of individuals map.ind(result) map.ind(result, dim = c(2, 1), label = TRUE) map.ind(result, dim = c(2, 1), point.size = 3, point.shape = 2) map.ind(result, dim = c(2, 1), map.title = "The top 100 Danish CEO's", point.color = quad) # Map of the individuals colored by contribution map.ind(result, point.color = result$ctr.ind[, 1], point.shape = 18) + scale_color_continuous(low = "white", high = "red") # Map of contributing modalities map.ctr(result, dim = c(2, 1)) map.ctr(result, dim = c(2, 1), ctr.dim = 2) map.ctr(result, point.size = 3) map.active(result, dim = c(2, 1)) map.sup(result, dim = c(2, 1)) # Plot.list # Selecting specific active modalities select <- c("Career start: Corporation (n:57)", "No Phd (n:92)") boo.select <- match(select, result$names.mod) map.select(result, list.mod = boo.select) highcor <- which(result$cor.mod[, 1] >= 0.2) map.select(result, list.mod = highcor) # Selecting specific supplementary modalities highdim3 <- which(sqrt(result$coord.sup[, 3]^2) >= 0.5) map.select(result, list.sup = highdim3) # Selecting specific individuals based on a certain criteria forfatter <- author == "Forfatter" map.select(result, list.ind = forfatter) # Combining it all map.select(result, list.mod = highcor, list.sup = highdim3, list.ind = forfatter) # Add points to an existing plot ctrplot <- map.ctr(result, ctr.dim = 1, point.color = "red") map.add(result, ctrplot, data.type = "ctr", ctr.dim = 2, point.color = "blue") # Using the list option in add.points forfatter <- author == "Forfatter" map.add(result, ctrplot, data.type = "select", list.ind = forfatter, colour = "purple") # Using the list option in add.points to add labels to only a part of the cloud of individuals forfatter <- author == "Forfatter" notforfatter <- author != "Forfatter" map.forfatter <- map.select(result, list.ind = notforfatter, label = FALSE) map.forfatter map.forfatter <- map.add(result, map.forfatter, data.type = "select", list.ind = forfatter) map.forfatter # Plotting all the modalities of one individual result2 <- soc.ca(active, sup, id) individual <- which(id == "Lars Larsen") ind.mat <- indicator(active) modalities <- names(which(ind.mat[individual, ] == 1)) mod.ind <- match(modalities, result2$names.mod) lars <- map.select(result2, list.mod = mod.ind) map.add(result2, lars, data.type = "select", list.ind = individual, colour = "red") # Adding concentration ellipses to an existing plot el.forfatter <- map.ellipse(result, map.forfatter, author) el.forfatter ## End(Not run)## Not run: data(directors) attach(directors) active <- data.frame(careerprofile_maclean_cat, careerfoundation_maclean_cat, years_between_edu_dir_cat, time_in_corp_before_ceo_cat, age_as_ceo_cat, career_changes_cat2, mba, abroad, hd, phd, education, author, placeofbirth, familyclass_bourdieu, partnersfamily_in_whoswho, family_in_whoswho) sup <- data.frame(size_prestige, ownership_cat_2, sector, location) id <- navn options(passive = c("MISSING", "Missing", "Irrelevant", "residence_value_cat2: Udlandet")) result <- soc.mca(active, sup, id) result # Contribution contribution(result, 1) contribution(result, 2) contribution(result, 3) contribution(result, 1, all = TRUE) contribution(result, 1, indices = TRUE) contribution(result, 1, mode = "mod") contribution(result, mode = "variable") # Individuals contribution(result, 1, mode = "ind") contribution(result, 2, mode = "ind") # Table of variance variance(result) # Invert result <- invert(result, c(1, 2, 3)) # Export and assign label # export.label(result) # result <- assign.label(result, # file = "https://raw.github.com/Rsoc/soc.ca/master/extra/director_labels.csv") # Add.n result <- add.to.label(result) contribution(result, 2) # The result object or "soc.ca" object str(result) dim1 <- result$coord.ind[, 1] qplot(dim1) # Quadrant quad <- create.quadrant(result) table(quad) quad <- create.quadrant(result, cut.min = 0, cut.max = 0) table(quad) # Map of individuals map.ind(result) map.ind(result, dim = c(2, 1), label = TRUE) map.ind(result, dim = c(2, 1), point.size = 3, point.shape = 2) map.ind(result, dim = c(2, 1), map.title = "The top 100 Danish CEO's", point.color = quad) # Map of the individuals colored by contribution map.ind(result, point.color = result$ctr.ind[, 1], point.shape = 18) + scale_color_continuous(low = "white", high = "red") # Map of contributing modalities map.ctr(result, dim = c(2, 1)) map.ctr(result, dim = c(2, 1), ctr.dim = 2) map.ctr(result, point.size = 3) map.active(result, dim = c(2, 1)) map.sup(result, dim = c(2, 1)) # Plot.list # Selecting specific active modalities select <- c("Career start: Corporation (n:57)", "No Phd (n:92)") boo.select <- match(select, result$names.mod) map.select(result, list.mod = boo.select) highcor <- which(result$cor.mod[, 1] >= 0.2) map.select(result, list.mod = highcor) # Selecting specific supplementary modalities highdim3 <- which(sqrt(result$coord.sup[, 3]^2) >= 0.5) map.select(result, list.sup = highdim3) # Selecting specific individuals based on a certain criteria forfatter <- author == "Forfatter" map.select(result, list.ind = forfatter) # Combining it all map.select(result, list.mod = highcor, list.sup = highdim3, list.ind = forfatter) # Add points to an existing plot ctrplot <- map.ctr(result, ctr.dim = 1, point.color = "red") map.add(result, ctrplot, data.type = "ctr", ctr.dim = 2, point.color = "blue") # Using the list option in add.points forfatter <- author == "Forfatter" map.add(result, ctrplot, data.type = "select", list.ind = forfatter, colour = "purple") # Using the list option in add.points to add labels to only a part of the cloud of individuals forfatter <- author == "Forfatter" notforfatter <- author != "Forfatter" map.forfatter <- map.select(result, list.ind = notforfatter, label = FALSE) map.forfatter map.forfatter <- map.add(result, map.forfatter, data.type = "select", list.ind = forfatter) map.forfatter # Plotting all the modalities of one individual result2 <- soc.ca(active, sup, id) individual <- which(id == "Lars Larsen") ind.mat <- indicator(active) modalities <- names(which(ind.mat[individual, ] == 1)) mod.ind <- match(modalities, result2$names.mod) lars <- map.select(result2, list.mod = mod.ind) map.add(result2, lars, data.type = "select", list.ind = individual, colour = "red") # Adding concentration ellipses to an existing plot el.forfatter <- map.ellipse(result, map.forfatter, author) el.forfatter ## End(Not run)
Calculate concentraion ellipses
ellipses(object, var, dim = c(1, 2), kappa = 2, npoints = 1000)ellipses(object, var, dim = c(1, 2), kappa = 2, npoints = 1000)
object |
|
var |
|
dim |
|
kappa |
|
npoints |
example(soc.mca) ellipses(result, active[,1])example(soc.mca) ellipses(result, active[,1])
Export objects from the soc.ca package to csv files.
export(object, file = "export.csv", dim = 1:5)export(object, file = "export.csv", dim = 1:5)
object |
is a soc.ca class object |
file |
is the path and name of the .csv values are to be exported to |
dim |
is the dimensions to be exported |
A .csv file with various values in UTF-8 encoding
This function allows easy translation and renaming of modalities by exporting the labels into a .csv file that is easier to work with.
export.label(object, file = FALSE, encoding = "UTF-8", overwrite = FALSE)export.label(object, file = FALSE, encoding = "UTF-8", overwrite = FALSE)
object |
is a soc.ca object |
file |
is the name and path of the exported file |
encoding |
is the character encoding of the exported file |
overwrite |
decides whether to overwrite already existing files |
Two columns are created within the .csv: 'New label' and 'Old label'. In the 'New label' column you write the new labels. Remember to leave 'Old label' unchanged as this column is used for matching.
If you want to add frequencies to the labels with the add.to.label function you should do this after exporting and assigning labels with the assign.label function. Otherwise the matching of the labels is likely to fail.
A .csv with two columns and preferably UTF-8 encoding.
Extract individuals
extract_cases(result, dim = 1:3) extract_ind(result, dim = 1:3)extract_cases(result, dim = 1:3) extract_ind(result, dim = 1:3)
result |
a soc.ca object or a PCA |
dim |
the dimensions |
a data.frame with coordinates and frequences
example(soc.mca) extract_cases(result)example(soc.mca) extract_cases(result)
Extract coordinates for the categories from an soc.mca
extract_cats(result, dim = 1:3) extract_mod(result, dim = 1:3)extract_cats(result, dim = 1:3) extract_mod(result, dim = 1:3)
result |
a soc.mca object or a PCA |
dim |
the dimensions |
a data.frame with coordinates and frequences
example(soc.mca) extract_cats(result)example(soc.mca) extract_cats(result)
Extract supplementary categories from an soc.mca
extract_sup(result, dim = 1:3)extract_sup(result, dim = 1:3)
result |
a soc.mca object |
dim |
the dimensions |
a data.frame with coordinates and frequences
example(soc.mca) extract_sup(result)example(soc.mca) extract_sup(result)
Use this function to calculate PEM pem values, chisq, distance and coordinates for each pair of categories in either an indicator matrix or the categories from an soc.mca result object. These relationship are usefull for both diagnostics, analysis, interpretation and plotting. For plotting combine with add.category.relations to build your plot.
get.category.relations( r, ind = r$indicator.matrix.active, dim = c(1, 2), rel = t(combn(colnames(ind), 2)) )get.category.relations( r, ind = r$indicator.matrix.active, dim = c(1, 2), rel = t(combn(colnames(ind), 2)) )
r |
an soc.mca result object |
ind |
an indicator matrix, see indicator |
dim |
a numeric vector with the dimensions for the coordinates. This is only sent to extract_mod. |
rel |
a matrix with pairs of categories |
variable |
a character vector with the variable where each category in ind came from. If ind was created directly with indicator you can use names(colnames(ind)). |
coords |
a data.frame with coordinates - similar to those produced by extract_mod |
a tibble
example(soc.mca) get.category.relations(result)example(soc.mca) get.category.relations(result)
Calculate contributions per heading
headings(object, dim = 1:3)headings(object, dim = 1:3)
object |
a soc.ca object with headings |
dim |
a numeric vector with the dimensions |
a matrix
data(taste) active.headings <- list() active.headings$Consumption <- na.omit(taste)[, c("TV", "Film", "Art", "Eat")] active.headings$Background <- na.omit(taste)[, c("Gender", "Age", "Income")] result.headings <- soc.mca(active.headings) headings(result.headings)data(taste) active.headings <- list() active.headings$Consumption <- na.omit(taste)[, c("TV", "Film", "Art", "Eat")] active.headings$Background <- na.omit(taste)[, c("Gender", "Age", "Income")] result.headings <- soc.mca(active.headings) headings(result.headings)
Explore the cloud of individuals
ind.explorer(object, active, sup = NULL)ind.explorer(object, active, sup = NULL)
object |
|
active |
Defines the active modalities in a data.frame with rows of individuals and columns of factors, without NA's' |
sup |
Defines the supplementary modalities in a data.frame with rows of individuals and columns of factors, without NA's |
an html application
## Not run: example(soc.mca) ind.explorer(result, active, sup) ## End(Not run)## Not run: example(soc.mca) ind.explorer(result, active, sup) ## End(Not run)
Creates an indicator matrix from a data.frame with the categories of the questions as columns and individuals as rows.
indicator(x, id = NULL, ps = ": ")indicator(x, id = NULL, ps = ": ")
x |
a data.frame of factors |
id |
a vector defining the labels for the individuals. If id = NULL row number is used. |
ps |
the seperator used in the creation of the names of the columns. |
Returns a indicator matrix
a <- rep(c("A","B"), 5) b <- rep(c("C", "D"), 5) indicator(data.frame(a,b))a <- rep(c("A","B"), 5) b <- rep(c("C", "D"), 5) indicator(data.frame(a,b))
Sometimes we want the indicator matrix from an mca in long format and this function delivers just that. The results contain both active and passive categories.
indicator.to.long(r)indicator.to.long(r)
r |
an result object of class soc.mca |
a tibble in long format
example(soc.mca) result |> indicator.to.long()example(soc.mca) result |> indicator.to.long()
Invert one or more axes of a correspondence analysis. The principal coordinates of the analysis are multiplied by -1.
invert(x, dim = 1)invert(x, dim = 1)
x |
is a soc.ca object |
dim |
is the dimensions to be inverted |
This is a convieniency function as you would have to modify coord.mod, coord.ind and coord.sup in the soc.ca object.
a soc.ca object with inverted coordinates on the specified dimensions
example(soc.ca) inverted.result <- invert(result, 1:2) result$coord.ind[1, 1:2] inverted.result$coord.ind[1, 1:2]example(soc.ca) inverted.result <- invert(result, 1:2) result$coord.ind[1, 1:2] inverted.result$coord.ind[1, 1:2]
Creates a map of the active modalities on two selected dimensions.
map.active( object, dim = c(1, 2), point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = FALSE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "active", labelx = "default", labely = "default", legend = NULL )map.active( object, dim = c(1, 2), point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = FALSE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "active", labelx = "default", labely = "default", legend = NULL )
object |
|
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
point.shape |
a numerical value defining the shape of the points. If set to its default, the default scale is used. It may be mapped to a variable with a suitable length and order. |
point.alpha |
defines the alpha of the points. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
point.fill |
defines the fill color of the points. It may be mapped to a variable with a suitable length and order. |
point.color |
defines the color of the points. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
point.size |
a numerical value defining the size of the points. If set to its default, the size is determined by the frequency of each modality. It may be defined by a variable with a suitable length. |
label |
if TRUE each point is assigned its label, defined in the soc.ca object. See assign.label and add.to.label for ways to alter the labels. |
label.repel |
if TRUE overlapping labels are rearranged, see geom_text_repel or geom_label_repel. |
label.alpha |
defines the alpha of the labels. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
label.color |
defines the color of the labels. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
label.size |
defines the size of the labels. It may be mapped to a variable with a suitable length and order. |
label.fill |
defines the color of the box behind the labels. It may be mapped to a variable with a suitable length and order. This only works if label.repel is TRUE. See geom_label_repel. |
map.title |
the title of the map. If set to its default the standard title is used. |
labelx |
the label of the horizontal axis. If set to NULL a standard label is used. |
labely |
the label of the vertical axis. If set to NULL a standard label is used. |
legend |
if set to TRUE a legend is provided. Change the legend with the guides, theme and linkguide_legend functions from the ggplot2 package. |
example(soc.ca) map.active(result) map.active(result, dim = c(2, 1)) map.active(result, point.size = result$ctr.mod[, 1], map.title = "All active modalities with size according to contribution")example(soc.ca) map.active(result) map.active(result, dim = c(2, 1)) map.active(result, point.size = result$ctr.mod[, 1], map.title = "All active modalities with size according to contribution")
Add points to an existing map created by one of the soc.ca mapping functions.
map.add( object, ca.map, plot.type = NULL, ctr.dim = 1, list.mod = NULL, list.sup = NULL, list.ind = NULL, point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = TRUE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, labelx = "default", labely = "default", legend = NULL )map.add( object, ca.map, plot.type = NULL, ctr.dim = 1, list.mod = NULL, list.sup = NULL, list.ind = NULL, point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = TRUE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, labelx = "default", labely = "default", legend = NULL )
object |
|
ca.map |
a map created using one of the soc.ca map functions |
plot.type |
defines which type of points to add to the map. Accepted values are: "mod", "sup", "ind", "ctr". These values correspond to the different forms of |
ctr.dim |
the dimensions of the contribution values |
list.mod |
a numerical vector indicating which active modalities to plot. It may also be a logical vector of the same length and order as the modalities in object$names.mod. |
list.sup |
a numerical vector indicating which supplementary modalities to plot. It may also be a logical vector of the same length and order as the modalities in object$names.sup. |
list.ind |
a numerical vector indicating which individuals to plot. It may also be a logical vector of the same length and order as the modalities in object$names.ind. |
point.shape |
a numerical value defining the shape of the points. If set to its default, the default scale is used. It may be mapped to a variable with a suitable length and order. |
point.alpha |
defines the alpha of the points. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
point.fill |
defines the fill color of the points. It may be mapped to a variable with a suitable length and order. |
point.color |
defines the color of the points. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
point.size |
a numerical value defining the size of the points. If set to its default, the size is determined by the frequency of each modality. It may be defined by a variable with a suitable length. |
label |
if TRUE each point is assigned its label, defined in the soc.ca object. See assign.label and add.to.label for ways to alter the labels. |
label.repel |
if TRUE overlapping labels are rearranged, see geom_text_repel or geom_label_repel. |
label.alpha |
defines the alpha of the labels. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
label.color |
defines the color of the labels. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
label.size |
defines the size of the labels. It may be mapped to a variable with a suitable length and order. |
label.fill |
defines the color of the box behind the labels. It may be mapped to a variable with a suitable length and order. This only works if label.repel is TRUE. See geom_label_repel. |
labelx |
the label of the horizontal axis. If set to NULL a standard label is used. |
labely |
the label of the vertical axis. If set to NULL a standard label is used. |
legend |
if set to TRUE a legend is provided. Change the legend with the guides, theme and linkguide_legend functions from the ggplot2 package. |
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
example(soc.ca) original.map <- map.sup(result) map.add(result, original.map, plot.type = "ctr", ctr.dim = 2) map.add(result, map.ind(result), plot.type = "select",list.ind = 1:50, point.color = "red", label = FALSE, point.size = result$ctr.ind[1:50, 1]*2000)example(soc.ca) original.map <- map.sup(result) map.add(result, original.map, plot.type = "ctr", ctr.dim = 2) map.add(result, map.ind(result), plot.type = "select",list.ind = 1:50, point.color = "red", label = FALSE, point.size = result$ctr.ind[1:50, 1]*2000)
This function takes a list of map objects and arranges them into an array.
map.array(x, ncol = 1, title = "", fixed.coord = TRUE, padding = 0.15)map.array(x, ncol = 1, title = "", fixed.coord = TRUE, padding = 0.15)
x |
a list of objects created by one of the mapping functions in the soc.ca package or any other ggplot2 plot |
ncol |
the number of columns the plots are arranged into |
title |
the main title of the array |
fixed.coord |
if TRUE the limits of all plots are set to the same as the largest plot |
padding |
the distance between the most extreme position and the axis limit |
## Not run: example(soc.ca) map.array(list(map.ind(result), map.mod(result)), ncol = 2) ## End(Not run)## Not run: example(soc.ca) map.array(list(map.ind(result), map.mod(result)), ncol = 2) ## End(Not run)
Create the base of a soc.ca map
map.ca.base( up = NULL, down = NULL, right = NULL, left = NULL, base_size = 15, ... )map.ca.base( up = NULL, down = NULL, right = NULL, left = NULL, base_size = 15, ... )
up |
the name of + pole on the vertical axis - "North" |
down |
the name of the - pole on the vertical axis - "South" |
right |
the name of the + pole on horizontal axis - "East" |
left |
the name of the - pole on the horizontal axis - "West" |
base_size |
controls the text size of themed labels |
... |
further arguments are passed onto ggplot() |
a ggplot2 object
Creates an array of Class Specific Mulitple Correspondence analysises
map.csa.all( object, variable, dim = c(1, 2), ncol = 2, FUN = map.ind, fixed.coord = TRUE, main.title = "", titles = levels(variable), ... )map.csa.all( object, variable, dim = c(1, 2), ncol = 2, FUN = map.ind, fixed.coord = TRUE, main.title = "", titles = levels(variable), ... )
object |
a soc.ca result object |
variable |
a factor with the same order and length as those used for the active modalities in object |
dim |
indicates what dimensions to map and in which order to plot them |
ncol |
the number of columns the maps are arranged into |
FUN |
the mapping function used for the plots; map.active, map.ctr, map.ind, map.select or map.sup |
fixed.coord |
if TRUE the limits of all plots are set to the same as the largest plot |
main.title |
the main title for all the maps |
titles |
a vector of the same length as the number of levels in |
... |
sends any further arguments to the mapping functions |
## Not run: example(soc.csa) map.csa.all(result, active[, 1]) map.csa.all(result, active[, 1], FUN = map.ctr, ctr.dim = 1) ## End(Not run)## Not run: example(soc.csa) map.csa.all(result, active[, 1]) map.csa.all(result, active[, 1], FUN = map.ctr, ctr.dim = 1) ## End(Not run)
Map the coordinates of the individuals in a CSA and its MCA
map.csa.mca( csa.object, mca.dim = 1, csa.dim = 1, smooth = TRUE, method = "auto" )map.csa.mca( csa.object, mca.dim = 1, csa.dim = 1, smooth = TRUE, method = "auto" )
csa.object |
a result object created by the soc.csa function |
mca.dim |
the dimension from the original MCA |
csa.dim |
the dimension from the CSA |
smooth |
if TRUE a line is added to the plot |
method |
the method used by ggplot to set the line see geom_smooth |
soc.csa, map.csa.all, linkmap.csa.mca.array
example(soc.csa) csa.res <- soc.csa(result, class.age) map.csa.mca(csa.res, mca.dim = 2, csa.dim = 1)example(soc.csa) csa.res <- soc.csa(result, class.age) map.csa.mca(csa.res, mca.dim = 2, csa.dim = 1)
Create an array of map.csa.mca maps
map.csa.mca.array(csa.object, ndim = 3, fixed.coord = TRUE, ...)map.csa.mca.array(csa.object, ndim = 3, fixed.coord = TRUE, ...)
csa.object |
a result object created by the soc.csa function |
ndim |
the number of dimensions to include in the array, starting from 1 |
fixed.coord |
if TRUE the limits of all plots are set to the same as the largest plot |
... |
for further arguments see map.csa.mca |
example(soc.csa) csa.res <- soc.csa(result, class.age) map.csa.mca.array(csa.res, ndim = 3)example(soc.csa) csa.res <- soc.csa(result, class.age) map.csa.mca.array(csa.res, ndim = 3)
Creates a map of the modalities contributing above average to one or more dimensions on two selected dimension.
map.ctr( object, dim = c(1, 2), ctr.dim = 1, point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = TRUE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "ctr", labelx = "default", labely = "default", legend = NULL )map.ctr( object, dim = c(1, 2), ctr.dim = 1, point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = TRUE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "ctr", labelx = "default", labely = "default", legend = NULL )
object |
|
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
ctr.dim |
the dimensions of the contribution values |
point.shape |
a numerical value defining the shape of the points. If set to its default, the default scale is used. It may be mapped to a variable with a suitable length and order. |
point.alpha |
defines the alpha of the points. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
point.fill |
defines the fill color of the points. It may be mapped to a variable with a suitable length and order. |
point.color |
defines the color of the points. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
point.size |
a numerical value defining the size of the points. If set to its default, the size is determined by the frequency of each modality. It may be defined by a variable with a suitable length. |
label |
if TRUE each point is assigned its label, defined in the soc.ca object. See assign.label and add.to.label for ways to alter the labels. |
label.repel |
if TRUE overlapping labels are rearranged, see geom_text_repel or geom_label_repel. |
label.alpha |
defines the alpha of the labels. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
label.color |
defines the color of the labels. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
label.size |
defines the size of the labels. It may be mapped to a variable with a suitable length and order. |
label.fill |
defines the color of the box behind the labels. It may be mapped to a variable with a suitable length and order. This only works if label.repel is TRUE. See geom_label_repel. |
map.title |
the title of the map. If set to its default the standard title is used. |
labelx |
the label of the horizontal axis. If set to NULL a standard label is used. |
labely |
the label of the vertical axis. If set to NULL a standard label is used. |
legend |
if set to TRUE a legend is provided. Change the legend with the guides, theme and linkguide_legend functions from the ggplot2 package. |
example(soc.ca) map.ctr(result) map.ctr(result, ctr.dim = c(1, 2))example(soc.ca) map.ctr(result) map.ctr(result, ctr.dim = c(1, 2))
Draws a 2d density plot on top of an existing soc.ca map. The density is
calculated by the kde2d function from MASS and plotted by
geom_density2d from ggplot2 map.density uses the
coordinates of the individuals as a basis for the density calculation.
Borders are arbitrary.
map.density( object, map = map.ind(object), group = NULL, color = "red", alpha = 0.8, size = 0.5, linetype = "solid" )map.density( object, map = map.ind(object), group = NULL, color = "red", alpha = 0.8, size = 0.5, linetype = "solid" )
object |
a soc.ca class object |
map |
a soc.ca map object created by one of the soc.ca mapping functions |
group |
a factor determining group membership. Density is mapped for each group individually. |
color |
a single value or vector determining the color. See the scale
functions of |
alpha |
a single value or vector determining the alpha. |
size |
a single value or vector determining the size of the lines. |
linetype |
a single value or vector determining the linetype |
example(soc.ca) map.density(result, map.ind(result, dim = 2:3, point.alpha = 0.2)) map.density(result, map.ind(result, legend = TRUE, point.alpha = 0.2), group = duplicated(active), color = duplicated(active), linetype = duplicated(active)) map.density(result, map.ctr(result))example(soc.ca) map.density(result, map.ind(result, dim = 2:3, point.alpha = 0.2)) map.density(result, map.ind(result, legend = TRUE, point.alpha = 0.2), group = duplicated(active), color = duplicated(active), linetype = duplicated(active)) map.density(result, map.ctr(result))
Add ellipses for each level in a factor to a plot made from a soc.ca object.
map.ellipse( object, ca.plot = map.ind(object), variable, ellipse.label = TRUE, ellipse.color = "default", label.size = 4, draw.levels = 1:nlevels(variable), ellipse.line = "solid" )map.ellipse( object, ca.plot = map.ind(object), variable, ellipse.label = TRUE, ellipse.color = "default", label.size = 4, draw.levels = 1:nlevels(variable), ellipse.line = "solid" )
object |
is a soc.ca class object. |
ca.plot |
is a plot made from a soc.ca object. |
variable |
is a factor of the same length and in the same order as the active varibles used for the soc.ca object. |
ellipse.label |
if TRUE the labels are included in the map. |
ellipse.color |
defines the color of the ellipses. If "default" the globally defined default colors are used. Ellipse.color can be either length of 1 or equal to the number of drawn levels. |
label.size |
defines the size of the labels. |
draw.levels |
indicates the levels in the variable for which a ellipse is drawn. |
ellipse.line |
defines the type of line used for the ellipses. |
a plot with a concentration ellipse containing 80% of the individuals for each modality.
example(soc.ca) map <- map.ind(result) map.ellipse(result, map, active[,2])example(soc.ca) map <- map.ind(result) map.ellipse(result, map, active[,2])
Create seperate maps with ellipses for each level in a factor arranged in an array.
map.ellipse.array( object, variable, dim = c(1, 2), draw.ellipses = TRUE, ncol = 2, titles = levels(variable), main.title = "", ... )map.ellipse.array( object, variable, dim = c(1, 2), draw.ellipses = TRUE, ncol = 2, titles = levels(variable), main.title = "", ... )
object |
a soc.ca class object |
variable |
a factor of the same length as the data.frame used to create object |
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
draw.ellipses |
if TRUE ellipses are drawn |
ncol |
the number of columns the plots are arranged into |
titles |
a vector of the same length as the number of levels in variable. These are the titles given to each subplot |
main.title |
the main title for all the plots |
... |
sends any further arguments to map.select and map.ellipse. |
## Not run: example(soc.ca) map.ellipse.array(result, active[, 1]) ## End(Not run)## Not run: example(soc.ca) map.ellipse.array(result, active[, 1]) ## End(Not run)
Creates a map of the individuals on two selected dimension.
map.ind( object, dim = c(1, 2), point.shape = 21, point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = 3, label = FALSE, label.repel = FALSE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "ind", labelx = "default", labely = "default", legend = NULL )map.ind( object, dim = c(1, 2), point.shape = 21, point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = 3, label = FALSE, label.repel = FALSE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "ind", labelx = "default", labely = "default", legend = NULL )
object |
|
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
point.shape |
a numerical value defining the shape of the points. It may be mapped to a variable with a suitable length and order. |
point.alpha |
defines the alpha of the points. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
point.fill |
defines the fill color of the points. It may be mapped to a variable with a suitable length and order. |
point.color |
defines the color of the points. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
point.size |
a numerical value defining the size of the points. It may be defined by a variable with a suitable length. |
label |
if TRUE each point is assigned its label, defined in the soc.ca object. See assign.label and add.to.label for ways to alter the labels. |
label.repel |
if TRUE overlapping labels are rearranged, see geom_text_repel or geom_label_repel. |
label.alpha |
defines the alpha of the labels. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
label.color |
defines the color of the labels. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
label.size |
defines the size of the labels. It may be mapped to a variable with a suitable length and order. |
label.fill |
defines the color of the box behind the labels. It may be mapped to a variable with a suitable length and order. This only works if label.repel is TRUE. See geom_label_repel. |
map.title |
the title of the map. If set to its default the standard title is used. |
labelx |
the label of the horizontal axis. If set to NULL a standard label is used. |
labely |
the label of the vertical axis. If set to NULL a standard label is used. |
legend |
if set to TRUE a legend is provided. Change the legend with the guides, theme and linkguide_legend functions from the ggplot2 package. |
example(soc.ca) map.ind(result) map.ind(result, map.title = "Each individual is given its shape according to a value in a factor", point.shape = active[, 1], legend = TRUE) map <- map.ind(result, map.title = "The contribution of the individuals with new scale", point.color = result$ctr.ind[, 1], point.shape = 18) map + scale_color_continuous(low = "white", high = "red") quad <- create.quadrant(result) map.ind(result, map.title = "Individuals in the space given shape and color by their quadrant", point.shape = quad, point.color = quad)example(soc.ca) map.ind(result) map.ind(result, map.title = "Each individual is given its shape according to a value in a factor", point.shape = active[, 1], legend = TRUE) map <- map.ind(result, map.title = "The contribution of the individuals with new scale", point.color = result$ctr.ind[, 1], point.shape = 18) map + scale_color_continuous(low = "white", high = "red") quad <- create.quadrant(result) map.ind(result, map.title = "Individuals in the space given shape and color by their quadrant", point.shape = quad, point.color = quad)
Creates a map of all active and supplementary modalities on two selected dimension.
map.mod( object, dim = c(1, 2), point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = FALSE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "mod", labelx = "default", labely = "default", legend = NULL )map.mod( object, dim = c(1, 2), point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = FALSE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "mod", labelx = "default", labely = "default", legend = NULL )
object |
|
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
point.shape |
a numerical value defining the shape of the points. If set to its default, the default scale is used. It may be mapped to a variable with a suitable length and order. |
point.alpha |
defines the alpha of the points. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
point.fill |
defines the fill color of the points. It may be mapped to a variable with a suitable length and order. |
point.color |
defines the color of the points. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
point.size |
a numerical value defining the size of the points. If set to its default, the size is determined by the frequency of each modality. It may be defined by a variable with a suitable length. |
label |
if TRUE each point is assigned its label, defined in the soc.ca object. See assign.label and add.to.label for ways to alter the labels. |
label.repel |
if TRUE overlapping labels are rearranged, see geom_text_repel or geom_label_repel. |
label.alpha |
defines the alpha of the labels. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
label.color |
defines the color of the labels. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
label.size |
defines the size of the labels. It may be mapped to a variable with a suitable length and order. |
label.fill |
defines the color of the box behind the labels. It may be mapped to a variable with a suitable length and order. This only works if label.repel is TRUE. See geom_label_repel. |
map.title |
the title of the map. If set to its default the standard title is used. |
labelx |
the label of the horizontal axis. If set to NULL a standard label is used. |
labely |
the label of the vertical axis. If set to NULL a standard label is used. |
legend |
if set to TRUE a legend is provided. Change the legend with the guides, theme and linkguide_legend functions from the ggplot2 package. |
example(soc.ca) map.mod(result) map.mod(result, dim = c(3, 2), point.size = 2)example(soc.ca) map.mod(result) map.mod(result, dim = c(3, 2), point.size = 2)
Plot a path along an ordered variable. If the variable is numerical it is cut into groups by the min_cut function.
map.path( object, x, map = map.ind(object, dim), dim = c(1, 2), label = TRUE, min.size = length(x)/10, ... )map.path( object, x, map = map.ind(object, dim), dim = c(1, 2), label = TRUE, min.size = length(x)/10, ... )
object |
is a soc.ca result object |
x |
is an ordered vector, either numerical or factor |
map |
is a plot object created with one of the mapping functions in the soc.ca package |
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
label |
if TRUE the label of the points are shown |
min.size |
is the minimum size given to the groups of a numerical variable, see min_cut. |
... |
further arguments are passed onto geom_path, geom_point and geom_text from the ggplot2 package |
example(soc.ca) map <- map.ind(result, point.color = as.numeric(sup$Age)) map <- map + scale_color_continuous(high = "red", low = "yellow") map.path(result, sup$Age, map)example(soc.ca) map <- map.ind(result, point.color = as.numeric(sup$Age)) map <- map + scale_color_continuous(high = "red", low = "yellow") map.path(result, sup$Age, map)
Creates a map of selected modalities or individuals
map.select( object, dim = c(1, 2), ctr.dim = 1, list.mod = NULL, list.sup = NULL, list.ind = NULL, point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = FALSE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "select", labelx = "default", labely = "default", legend = NULL, ... )map.select( object, dim = c(1, 2), ctr.dim = 1, list.mod = NULL, list.sup = NULL, list.ind = NULL, point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = FALSE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "select", labelx = "default", labely = "default", legend = NULL, ... )
object |
|
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
ctr.dim |
the dimensions of the contribution values |
list.mod |
a numerical vector indicating which active modalities to plot. It may also be a logical vector of the same length and order as the modalities in object$names.mod. |
list.sup |
a numerical vector indicating which supplementary modalities to plot. It may also be a logical vector of the same length and order as the modalities in object$names.sup. |
list.ind |
a numerical vector indicating which individuals to plot. It may also be a logical vector of the same length and order as the modalities in object$names.ind. |
point.shape |
a numerical value defining the shape of the points. If set to its default, the default scale is used. It may be mapped to a variable with a suitable length and order. |
point.alpha |
defines the alpha of the points. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
point.fill |
defines the fill color of the points. It may be mapped to a variable with a suitable length and order. |
point.color |
defines the color of the points. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
point.size |
a numerical value defining the size of the points. If set to its default, the size is determined by the frequency of each modality. It may be defined by a variable with a suitable length. |
label |
if TRUE each point is assigned its label, defined in the soc.ca object. See assign.label and add.to.label for ways to alter the labels. |
label.repel |
if TRUE overlapping labels are rearranged, see geom_text_repel or geom_label_repel. |
label.alpha |
defines the alpha of the labels. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
label.color |
defines the color of the labels. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
label.size |
defines the size of the labels. It may be mapped to a variable with a suitable length and order. |
label.fill |
defines the color of the box behind the labels. It may be mapped to a variable with a suitable length and order. This only works if label.repel is TRUE. See geom_label_repel. |
map.title |
the title of the map. If set to its default the standard title is used. |
labelx |
the label of the horizontal axis. If set to NULL a standard label is used. |
labely |
the label of the vertical axis. If set to NULL a standard label is used. |
legend |
if set to TRUE a legend is provided. Change the legend with the guides, theme and guide_legend functions from the ggplot2 package. |
... |
further arguments are currently ignored. |
example(soc.ca) map.select(result, map.title = "Map of the first ten modalities", list.mod = 1:10) select <- active[, 3] select <- select == levels(select)[2] map.select(result, map.title = "Map of all individuals sharing a particular value", list.ind = select, point.size = 3) map.select(result, map.title = "Map of both select individuals and modalities", list.ind = select, list.mod = 1:10)example(soc.ca) map.select(result, map.title = "Map of the first ten modalities", list.mod = 1:10) select <- active[, 3] select <- select == levels(select)[2] map.select(result, map.title = "Map of all individuals sharing a particular value", list.ind = select, point.size = 3) map.select(result, map.title = "Map of both select individuals and modalities", list.ind = select, list.mod = 1:10)
Creates a map of the supplementary modalities on two selected dimension.
map.sup( object, dim = c(1, 2), point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = TRUE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "sup", labelx = "default", labely = "default", legend = NULL )map.sup( object, dim = c(1, 2), point.shape = "variable", point.alpha = 0.8, point.fill = "whitesmoke", point.color = "black", point.size = "freq", label = TRUE, label.repel = TRUE, label.alpha = 0.8, label.color = "black", label.size = 4, label.fill = NULL, map.title = "sup", labelx = "default", labely = "default", legend = NULL )
object |
|
dim |
the dimensions in the order they are to be plotted. The first number defines the horizontal axis and the second number defines the vertical axis. |
point.shape |
a numerical value defining the shape of the points. If set to its default, the default scale is used. It may be mapped to a variable with a suitable length and order. |
point.alpha |
defines the alpha of the points. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
point.fill |
defines the fill color of the points. It may be mapped to a variable with a suitable length and order. |
point.color |
defines the color of the points. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
point.size |
a numerical value defining the size of the points. If set to its default, the size is determined by the frequency of each modality. It may be defined by a variable with a suitable length. |
label |
if TRUE each point is assigned its label, defined in the soc.ca object. See assign.label and add.to.label for ways to alter the labels. |
label.repel |
if TRUE overlapping labels are rearranged, see geom_text_repel or geom_label_repel. |
label.alpha |
defines the alpha of the labels. Values range from 0 to 1. It may be mapped to a variable with a suitable length and order. |
label.color |
defines the color of the labels. It may be mapped to a variable with a suitable length and order. See colors for some of the valid values. |
label.size |
defines the size of the labels. It may be mapped to a variable with a suitable length and order. |
label.fill |
defines the color of the box behind the labels. It may be mapped to a variable with a suitable length and order. This only works if label.repel is TRUE. See geom_label_repel. |
map.title |
the title of the map. If set to its default the standard title is used. |
labelx |
the label of the horizontal axis. If set to NULL a standard label is used. |
labely |
the label of the vertical axis. If set to NULL a standard label is used. |
legend |
if set to TRUE a legend is provided. Change the legend with the guides, theme and linkguide_legend functions from the ggplot2 package. |
example(soc.ca) map.sup(result) map.sup(result, dim = c(2, 1)) map.sup(result, point.size = result$coord.sup[, 4], map.title = "All supplementary modalities with size according to coordinate on the 4th dimension")example(soc.ca) map.sup(result) map.sup(result, dim = c(2, 1)) map.sup(result, point.size = result$coord.sup[, 4], map.title = "All supplementary modalities with size according to coordinate on the 4th dimension")
Two variables that have perfectly or almost perfectly overlapping sets of categories will skew an mca analysis. This function tries to find the variables that do that so that we may remove them from the analysis or set some of the categories as passive. An MCA is run on all pairs of variables in the active dataset and we take first and strongest eigenvalue for each pair. Values range from 0.5 to 1, where 1 signifies a perfect or near perfect overlap between sets of categories while 0.5 is the opposite - a near orthogonal relationship between the two variables. While a eigenvalue of 1 is a strong candidate for intervention, probably exclusion of one of the variables, it is less clear what the lower bound is. But values around 0.8 are also strong candidates for further inspection.
mca.eigen.check(x, passive = "Missing")mca.eigen.check(x, passive = "Missing")
x |
a data.frame of factors or a result object from soc.mca |
passive |
a character vector with the full or partial names of categories to be set as passive. Each element in passive is passed to a grep function. |
a tibble
example(soc.mca) mca.eigen.check(active) mca.eigen.check(result)example(soc.mca) mca.eigen.check(active) mca.eigen.check(result)
Compare MCA's with triads
mca.triads(l.mca, l.triads, dim = c(1, 2), fix.mca = 1)mca.triads(l.mca, l.triads, dim = c(1, 2), fix.mca = 1)
l.mca |
a list of soc.mca objects |
l.triads |
a list of triads |
dim |
the dimensions of the plane |
fix.mca |
the indice of the mca that is used as a fixpoint for the axis across mca's |
a triad object
Many continuous variables are very unequally distributed, often with many individuals in the lower categories and fewer in the top. As a result it is often difficult to create groups of equal size, with unique cut-points. By defining the wanted minimum of individuals in each category, but still allowing this minimum to be surpassed, it is easy to create ordinal variables from continuous variables. The last category will not neccessarily have the minimum number of individuals.
min_cut(x, min.size = length(x)/10)min_cut(x, min.size = length(x)/10)
x |
is a continuous numerical variable |
min.size |
is the minimum number of individuals in each category |
a numerical vector with the number of each category
a <- 1:1000 table(min_cut(a)) b <- c(rep(0, 50), 1:500) table(min_cut(b, min.size = 20))a <- 1:1000 table(min_cut(a)) b <- c(rep(0, 50), 1:500) table(min_cut(b, min.size = 20))
The example dataset used by Odysseas E. Moschidis (2009):
Odysseas E. Moschidis
Moschidis, Odysseas E. “A Different Approach to Multiple Correspondence Analysis (MCA) than That of Specific MCA.” Mathématiques et Sciences Humaines / Mathematics and Social Sciences 47, no. 186 (October 15, 2009): 77–88. https://doi.org/10.4000/msh.11091.
# The moschidis example #data(moschidis) #active <- moschidis[, c("E1","E2", "E3")] #id <- moschidis[, c("ID")] # result <- soc.mca(active, identifier = id, Moschidis = FALSE) # Compare output to Moschidis (2009, p. 85) #result$inertia_full # In the analysis of the 'real' data the modality #'E1: 1' with a low mass (fr/Q) has a very high contribution to the fourth axis #result$ctr.mod[, 4] # Using the transformed model suggested by Moschidis (2009) that takes into # account the number of modalities per question in order to balance the # contribution of the modalities #result_trans <- soc.mca(active, identifier = id, Moschidis = TRUE) #result_trans$inertia_full #result_trans$ctr.mod[, 4]# The moschidis example #data(moschidis) #active <- moschidis[, c("E1","E2", "E3")] #id <- moschidis[, c("ID")] # result <- soc.mca(active, identifier = id, Moschidis = FALSE) # Compare output to Moschidis (2009, p. 85) #result$inertia_full # In the analysis of the 'real' data the modality #'E1: 1' with a low mass (fr/Q) has a very high contribution to the fourth axis #result$ctr.mod[, 4] # Using the transformed model suggested by Moschidis (2009) that takes into # account the number of modalities per question in order to balance the # contribution of the modalities #result_trans <- soc.mca(active, identifier = id, Moschidis = TRUE) #result_trans$inertia_full #result_trans$ctr.mod[, 4]
This dataset was used to construct a field of the Danish Power Elite from 2013
Jacob Lunding, Anton Grau Larsen and Christoph Ellersgaard
The example dataset used by Brigitte Le Roux & Henry Rouanet (2004):
Brigitte Le Roux
Perrineau, Pascal, Jean Chiche, Brigitte Le Roux, and Henry Rouanet. “L’espace politique des électeurs français à la fin des années 1990: nouveaux et anciens clivages, hétérogénéité des électorats.” Revue Francaise de Science Politique, no. 3 (June 2000): 463–88.
Le Roux, Brigitte, and Henry Rouanet. Multiple Correspondence Analysis. Thousand Oaks, Calif.: Sage Publications, 2010.
# French Political Space example data(political_space97) #Recoding political_space97$Democracy <- ifelse(political_space97$Democracy %in% 1:2, "1_2", political_space97$Democracy) political_space97$Politicians <- ifelse(political_space97$Politicians %in% 1:2, "1_2", political_space97$Politicians) #Assigning questions to themes ethno <- data.frame(Immigrants = political_space97$Immigrants, "North-Africans" = political_space97$NorthAfricans, Races = political_space97$Races, "At home" = political_space97$AtHome, check.names = FALSE) autho <- data.frame("Death Penalty" = political_space97$DeathPenalty, School = political_space97$School, check.names = FALSE) social <- data.frame("Strike Effectiveness" = political_space97$StrikeEffectivness, "Strike 95" = political_space97$Strike95, "Unions" = political_space97$Unions, "Public services" = political_space97$PublicServices, check.names = FALSE) economy <- data.frame(Liberalism = political_space97$Liberalism, Profit = political_space97$Profit, Privatization = political_space97$Privatization, Globalization = political_space97$Globalization, check.names = FALSE) politics <- data.frame(Democracy = political_space97$Democracy, Politicians = political_space97$Politicians, check.names = FALSE) supranat <- data.frame(Euro = political_space97$Euro, "EU Power" = political_space97$EUpower, "End EU" = political_space97$EndEU, "EU protection" = political_space97$EUprotection, check.names = FALSE) # Creating and naming list of headings active <- list(ethno, autho, social, economy, politics, supranat) names(active) <- c("Ethnocentrism", "Authoritarianism", "Social", "Economy", "Politics", "Supranationality") sup <- data.frame(political_space97$Vote) result <- soc.mca(active, sup = sup, passive = ": 5") headings(result) map.active(result, point.color = result$headings, point.shape = result$headings, label.color = result$headings)# French Political Space example data(political_space97) #Recoding political_space97$Democracy <- ifelse(political_space97$Democracy %in% 1:2, "1_2", political_space97$Democracy) political_space97$Politicians <- ifelse(political_space97$Politicians %in% 1:2, "1_2", political_space97$Politicians) #Assigning questions to themes ethno <- data.frame(Immigrants = political_space97$Immigrants, "North-Africans" = political_space97$NorthAfricans, Races = political_space97$Races, "At home" = political_space97$AtHome, check.names = FALSE) autho <- data.frame("Death Penalty" = political_space97$DeathPenalty, School = political_space97$School, check.names = FALSE) social <- data.frame("Strike Effectiveness" = political_space97$StrikeEffectivness, "Strike 95" = political_space97$Strike95, "Unions" = political_space97$Unions, "Public services" = political_space97$PublicServices, check.names = FALSE) economy <- data.frame(Liberalism = political_space97$Liberalism, Profit = political_space97$Profit, Privatization = political_space97$Privatization, Globalization = political_space97$Globalization, check.names = FALSE) politics <- data.frame(Democracy = political_space97$Democracy, Politicians = political_space97$Politicians, check.names = FALSE) supranat <- data.frame(Euro = political_space97$Euro, "EU Power" = political_space97$EUpower, "End EU" = political_space97$EndEU, "EU protection" = political_space97$EUprotection, check.names = FALSE) # Creating and naming list of headings active <- list(ethno, autho, social, economy, politics, supranat) names(active) <- c("Ethnocentrism", "Authoritarianism", "Social", "Economy", "Politics", "Supranationality") sup <- data.frame(political_space97$Vote) result <- soc.mca(active, sup = sup, passive = ": 5") headings(result) map.active(result, point.color = result$headings, point.shape = result$headings, label.color = result$headings)
Prints commonly used measures used in the analysis of multiple correspondence analysis
## S3 method for class 'soc.mca' print(x, ...)## S3 method for class 'soc.mca' print(x, ...)
x |
is a soc.ca class object |
... |
further arguments are ignored |
Active dimensions is the number of dimensions remaining after the reduction of the dimensionality of the analysis.
Active modalities is the number of modalities that are not set as passive.
Share of passive mass is the percentage of the total mass that is represented by the passive modalities.
The values represented in the scree plot are the adjusted inertias, see variance
The active variables are represented with their number of active modalities and their share of the total variance/inertia.
example(soc.ca) print(result)example(soc.ca) print(result)
This function tests and removes variables that have no or too few relations with other variables. In other words variables that only contribute with random noise to the analysis. Removing these variables will tend to increase the strength of the first dimensions and give a wider dispersion of the cloud of cases on the first dimensions. Removing these variables can also give a simpler analysis that is easier to interpret and communicate. The core of the pruning procedure uses the mca.eigen.check to construct a weighted network of relations between variables. Tie strength is measured by the first eigenvalue of an MCA between the two variables. Ties between variables with a weak relationship are removed and variables with few connections to other variables are discarded. With the default values a analysis without irrelevant variables is unchanged. Note that passive categories are inherited from the original analysis and are not included in the mca.eigen.check. This procedure does not help with variables that are too strongly related.
prune.mca( r, eigen.cut.off = 0.55, network.pruning = TRUE, average.pruning = FALSE, min.degree = 1 )prune.mca( r, eigen.cut.off = 0.55, network.pruning = TRUE, average.pruning = FALSE, min.degree = 1 )
r |
a result object from soc.mca |
eigen.cut.off |
the cut.off for the first eigen value from mca.eigen.check |
network.pruning |
If TRUE variables are pruned on the basis their degree |
average.pruning |
If TRUE variables with a sum of ties below average are discarded. This |
min.degree |
the minimum number of ties a variable has to have to remain in the analysis |
A list containing:
var |
a tibble with the weighted degree of the variables |
mca.eigen.check |
The results from mca.eigen.check |
g |
a network graph - see igraph |
remaining.var |
a character vector with the names of the remaining variables |
removed |
a character vector with the names of the removed variables |
pruned.r |
A pruned version of the original soc.mca object |
Inspired by: Durand, Jean-Luc, and Brigitte Le Roux. 2018. “Linkage Index of Variables and its Relationship with Variance of Eigenvalues in PCA and MCA.” Statistica Applicata 29(2):123–35. doi: 10.26398/ijas.0029-006.
example(soc.mca) pr <- prune.mca(result) pr$removed # This example has no irrelevant variables so nothing is removedexample(soc.mca) pr <- prune.mca(result) pr$removed # This example has no irrelevant variables so nothing is removed
We sample from each of the active variables independently removing the original correlations but retaining the frequencies of the categories. This function is useful to see the extent to which the mca solution reflects the correlations between variables or the frequency distribution between the active categories. Passive categories are inherited from the original analysis.
randomize.mca(r, replace = FALSE)randomize.mca(r, replace = FALSE)
r |
a result object from soc.mca |
replace |
a soc.mca object
example(soc.mca) randomize.mca(result)example(soc.mca) randomize.mca(result)
This package is optimized to the needs of scientists within the social sciences. The soc.ca package produces specific and class specific multiple correspondence analysis on survey-like data. Soc.ca is optimized to only give the most essential statistical output sorted so as to help in analysis. Seperate functions exists for near publication-ready plots and tables.
We are in debt to the work of others, especially Brigitte Le Roux and Henry Rouanet for the mathematical definitions of the method and their examples. Furthermore this package was initially based on code from the ca package written by Michael Greenacre and Oleg Nenadic.
If you are looking for features that are absent in soc.ca, it may be available in some of these packages for correspondence analysis: ca, anacor and FactoMineR.
Le Roux, Brigitte, and Henry Rouanet. 2010. Multiple correspondence analysis. Thousand Oaks: Sage.
Le Roux, Brigitte, and Henry Rouanet. 2004. Geometric Data Analysis from Correspondence Analysis to Structured Data Analysis. Dordrecht: Kluwer Academic Publishers.
data(taste) # Create a data frame of factors containing all the active variables taste <- taste[which(taste$Isup == 'Active'), ] attach(taste) active <- data.frame(TV, Film, Art, Eat) sup <- data.frame(Gender, Age, Income) detach(taste) # Runs the analysis result <- soc.mca(active, sup)data(taste) # Create a data frame of factors containing all the active variables taste <- taste[which(taste$Isup == 'Active'), ] attach(taste) active <- data.frame(TV, Film, Art, Eat) sup <- data.frame(Gender, Age, Income) detach(taste) # Runs the analysis result <- soc.mca(active, sup)
soc.csa performs a class specific multiple correspondence analysis on a data.frame of factors, where cases are rows and columns are variables. Most descriptive and analytical functions that work for soc.mca, also work for soc.csa
soc.csa(object, class.indicator, sup = NULL)soc.csa(object, class.indicator, sup = NULL)
object |
is a soc.ca class object created with soc.mca |
class.indicator |
the row indices of the class specific individuals |
sup |
Defines the supplementary modalities in a data.frame with rows of individuals and columns of factors, without NA's |
nd |
Number of active dimensions |
n.ind |
The number of active individuals |
n.mod |
The number of active modalities |
eigen |
Eigenvectors |
total.inertia |
The sum of inertia |
adj.inertia |
A matrix with all active dimensions, adjusted and unadjusted inertias. See variance |
freq.mod |
Frequencies for the active modalities. See add.to.label |
freq.sup |
Frequencies for the supplementary modalities. See add.to.label |
ctr.mod |
A matrix with the contribution values of the active modalities per dimension. See contribution |
ctr.ind |
A matrix with the contribution values of the individuals per dimension. |
cor.mod |
The correlation or quality of each modality per dimension. |
cor.ind |
The correlation or quality of each individual per dimension. |
mass.mod |
The mass of each modality |
coord.mod |
A matrix with the principal coordinates of each active modality per dimension. |
coord.ind |
A matrix with the principal coordinates of each individual per dimension. |
coord.sup |
A matrix with the principal coordinates of each supplementary modality per dimension. Notice that the position of the supplementary modalities in class specific analysis is the mean point of the individuals, which is not directly comparable with the cloud of the active modalities. |
indicator.matrix |
A indicator matrix. See indicator |
names.mod |
The names of the active modalities |
labels.mod |
The shorter labels of the active modalities |
names.ind |
The names of the individuals |
names.sup |
The names of the supplementary modalities |
names.passive |
The names of the passive modalities |
modal |
A matrix with the number of modalities per variable and their location |
variable |
A vector with the name of the variable for each of the active modalities |
variable.sup |
A vector with the name of the variable for each of the supplementary modalities |
original.class.indicator |
The class indicator |
original.result |
The original soc.ca object used for the CSA |
Anton Grau Larsen, University of Copenhagen
Stefan Bastholm Andrade, University of Copenhagen
Christoph Ellersgaard, University of Copenhagen
Le Roux, B., og H. Rouanet. 2010. Multiple correspondence analysis. Thousand Oaks: Sage.
example(soc.ca) class.age <- which(taste$Age == '55-64') res.csa <- soc.csa(result, class.age) res.csaexample(soc.ca) class.age <- which(taste$Age == '55-64') res.csa <- soc.csa(result, class.age) res.csa
soc.mca performs a specific multiple correspondence analysis on a data.frame of factors, where cases are rows and columns are variables.Specific Multiple Correspondence Analysis
soc.mca( active, sup = NULL, identifier = NULL, passive = getOption("passive", default = "Missing"), weight = NULL, Moschidis = FALSE, detailed.results = FALSE )soc.mca( active, sup = NULL, identifier = NULL, passive = getOption("passive", default = "Missing"), weight = NULL, Moschidis = FALSE, detailed.results = FALSE )
active |
Defines the active modalities in a data.frame with rows of individuals and columns of factors, without NA's'. Active can also be a named list of data.frames. The data.frames will correspond to the analytical headings. |
sup |
Defines the supplementary modalities in a data.frame with rows of individuals and columns of factors, without NA's |
identifier |
A single vector containing a single value for each row/individual in x and sup. Typically a name or an id.number. |
passive |
A single character vector with the full or partial names of the passive modalities. All names that have a full or partial match will be set as passive. |
weight |
a numeric vector with the weights for the individual rows. The weight is normalized afterwards. |
Moschidis |
If TRUE adjusts contribution values for rare modalities. see moschidis. |
detailed.results |
If FALSE the result object is trimmed to reduce its memory footprint. |
nd |
Number of active dimensions |
n.ind |
The number of active individuals |
n.mod |
The number of active modalities |
eigen |
Eigenvectors |
total.inertia |
The sum of inertia |
adj.inertia |
A matrix with all active dimensions, adjusted and unadjusted inertias. See variance |
freq.mod |
Frequencies for the active modalities. See add.to.label |
freq.sup |
Frequencies for the supplementary modalities. See add.to.label |
ctr.mod |
A matrix with the contribution values of the active modalities per dimension. See contribution |
ctr.ind |
A matrix with the contribution values of the individuals per dimension. |
cor.mod |
The correlation or quality of each modality per dimension. |
cor.ind |
The correlation or quality of each individual per dimension. |
mass.mod |
The mass of each modality |
coord.mod |
A matrix with the principal coordinates of each active modality per dimension. |
coord.ind |
A matrix with the principal coordinates of each individual per dimension. |
coord.sup |
A matrix with the principal coordinates of each supplementary modality per dimension. |
names.mod |
The names of the active modalities |
labels.mod |
The shorter labels of the active modalities |
names.ind |
The names of the individuals |
names.sup |
The names of the supplementary modalities |
names.passive |
The names of the passive modalities |
modal |
A matrix with the number of modalities per variable and their location |
variable |
A character vector with the name of the variable of the active modalities |
Rosenlund.tresh |
A numeric vector with the contribution values adjusted with the Rosenlund threshold, see: see p 92 in: Rosenlund, Lennart. Exploring the City with Bourdieu: Applying Pierre Bourdieu’s Theories and Methods to Study the Community. Saarbrücken: VDM Verlag Dr. Müller, 2009. |
t.test.sup |
A matrix with a the student t-test of the coordinates of the supplementary variables |
Share.of.var |
A matrix the share of variance for each variable |
Anton Grau Larsen
Jacob Lunding
Stefan Bastholm Andrade
Christoph Ellersgaard
Le Roux, B., og H. Rouanet. 2010. Multiple correspondence analysis. Thousand Oaks: Sage.
# Loads the "taste" dataset included in this package data(taste) # Create a data frame of factors containing all the active variables taste <- taste[which(taste$Isup == 'Active'), ] attach(taste) active <- data.frame(TV, Film, Art, Eat) sup <- data.frame(Gender, Age, Income) detach(taste) # Runs the analysis result <- soc.mca(active, sup) # Prints the results result # A specific multiple correspondence analysis # options defines what words or phrases that are looked for in the labels of the active modalities. options(passive = c("Film: CostumeDrama", "TV: Tv-Sport")) soc.mca(active, sup) options(passive = NULL)# Loads the "taste" dataset included in this package data(taste) # Create a data frame of factors containing all the active variables taste <- taste[which(taste$Isup == 'Active'), ] attach(taste) active <- data.frame(TV, Film, Art, Eat) sup <- data.frame(Gender, Age, Income) detach(taste) # Runs the analysis result <- soc.mca(active, sup) # Prints the results result # A specific multiple correspondence analysis # options defines what words or phrases that are looked for in the labels of the active modalities. options(passive = c("Film: CostumeDrama", "TV: Tv-Sport")) soc.mca(active, sup) options(passive = NULL)
Calculate the average coordinates in the category cloud of a soc.mca analysis.
supplementary.categories(object, sup, dim = 1:2)supplementary.categories(object, sup, dim = 1:2)
object |
a soc.mca result object |
sup |
a data.frame of factors or an indicator matrix |
dim |
a numeric vector with the two dimensions calculated |
a data.frame with coordinates and labels
example(soc.mca) supplementary.categories(result, sup)example(soc.mca) supplementary.categories(result, sup)
Add supplementary individuals to a result object
supplementary.individuals(object, sup.indicator, replace = FALSE)supplementary.individuals(object, sup.indicator, replace = FALSE)
object |
is a soc.ca class object created with soc.mca |
sup.indicator |
is a indicator matrix for the supplementary individuals with the same columns as the active variables in object. |
replace |
if TRUE the coordinates of the active individuals are discarded. If FALSE the coordinates of the supplementary and active individuals are combined. The factor |
a soc.ca class object created with soc.mca
example(soc.mca) res.pas <- soc.mca(active, passive = "Costume") res.sup <- supplementary.individuals(res.pas, sup.indicator = indicator(active)) a <- res.sup$coord.ind[res.sup$supplementary.individuals == "Supplementary",] b <- res.pas$coord.ind all.equal(as.vector(a), as.vector(b)) map.ind(res.sup)example(soc.mca) res.pas <- soc.mca(active, passive = "Costume") res.sup <- supplementary.individuals(res.pas, sup.indicator = indicator(active)) a <- res.sup$coord.ind[res.sup$supplementary.individuals == "Supplementary",] b <- res.pas$coord.ind all.equal(as.vector(a), as.vector(b)) map.ind(res.sup)
The taste example dataset used by Le Roux & Rouanet(2010):
The variables included in the dataset:
Preferred TV program |
(8 categories): news, comedy, police, nature, sport, films, drama, soap operas |
Preferred Film |
(8 categories): action, comedy, costume drama, documentary, horror, musical, romance, SciFi |
Preferred type of Art |
(7 categories): performance, landscape, renaissance, still life, portrait, modern, impressionsism |
Preferred place to Eat out |
(6 categories): fish & chips, pub, Indian restuarant, Italian restaurant, French restaurant, steak house |
Brigitte Le Roux
Le Roux, Brigitte, Henry Rouanet, Mike Savage, og Alan Warde. 2008. "Class and Cultural Division in the UK". Sociology 42(6):1049-1071.
Le Roux, B., og H. Rouanet. 2010. Multiple correspondence analysis. Thousand Oaks: Sage.
## Not run: # The taste example data(taste) data_taste <- taste[which(taste$Isup == 'Active'), ] active <- data.frame(data_taste$TV, data_taste$Film, data_taste$Art, data_taste$Eat) sup <- data.frame(data_taste$Gender, data_taste$Age, data_taste$Income) # Multiple Correspondence Analysis result.mca <- soc.mca(active, sup) str(result.mca) result.mca variance(result.mca) # See p.46 in Le Roux(2010) contribution(result.mca, 1) contribution(result.mca, 2) contribution(result.mca, 1:3, mode = "variable") map.active(result.mca, point.fill = result.mca$variable) map.active(result.mca, map.title="Map of active modalities with size of contribution to 1. dimension", point.size=result.mca$ctr.mod[, 1]) map.active(result.mca, map.title="Map of active modalities with size of contribution to 2. dimension", point.size=result.mca$ctr.mod[, 2]) map.ind(result.mca) map.ind(result.mca, dim=c(1, 2), point.color=result.mca$ctr.ind[, 1], point.shape=18) + scale_color_continuous(low="white", high="black") # Plot of all dublets map.ind(result.mca, map.title="Map of all unique individuals", point.color=duplicated(active)) map.ind(result.mca, map.title="Map with individuals colored by the TV variable", point.color=active$TV) # Ellipse map <- map.ind(result.mca) map.ellipse(result.mca, map, as.factor(data_taste$Age == '55-64')) ##### Specific Multiple Correspondence Analysis options(passive= c("Film: CostumeDrama", "TV: Tv-Sport")) result.smca <- soc.mca(active, sup) result.smca result.smca$names.passive ##### Class Specific Correspondence Analysis options(passive=NULL) class.age <- which(data_taste$Age == '55-64') result.csca <- soc.csa(result.mca, class.age, sup) str(result.csca) # Correlations csa.measures(result.csca) variance(result.csca) contribution(result.csca, 1) contribution(result.csca, 2) contribution(result.csca, 1:3, mode = "variable") # Plots map.ind(result.csca) map.csa.mca(result.csca) map.csa.mca.array(result.csca) ## End(Not run)## Not run: # The taste example data(taste) data_taste <- taste[which(taste$Isup == 'Active'), ] active <- data.frame(data_taste$TV, data_taste$Film, data_taste$Art, data_taste$Eat) sup <- data.frame(data_taste$Gender, data_taste$Age, data_taste$Income) # Multiple Correspondence Analysis result.mca <- soc.mca(active, sup) str(result.mca) result.mca variance(result.mca) # See p.46 in Le Roux(2010) contribution(result.mca, 1) contribution(result.mca, 2) contribution(result.mca, 1:3, mode = "variable") map.active(result.mca, point.fill = result.mca$variable) map.active(result.mca, map.title="Map of active modalities with size of contribution to 1. dimension", point.size=result.mca$ctr.mod[, 1]) map.active(result.mca, map.title="Map of active modalities with size of contribution to 2. dimension", point.size=result.mca$ctr.mod[, 2]) map.ind(result.mca) map.ind(result.mca, dim=c(1, 2), point.color=result.mca$ctr.ind[, 1], point.shape=18) + scale_color_continuous(low="white", high="black") # Plot of all dublets map.ind(result.mca, map.title="Map of all unique individuals", point.color=duplicated(active)) map.ind(result.mca, map.title="Map with individuals colored by the TV variable", point.color=active$TV) # Ellipse map <- map.ind(result.mca) map.ellipse(result.mca, map, as.factor(data_taste$Age == '55-64')) ##### Specific Multiple Correspondence Analysis options(passive= c("Film: CostumeDrama", "TV: Tv-Sport")) result.smca <- soc.mca(active, sup) result.smca result.smca$names.passive ##### Class Specific Correspondence Analysis options(passive=NULL) class.age <- which(data_taste$Age == '55-64') result.csca <- soc.csa(result.mca, class.age, sup) str(result.csca) # Correlations csa.measures(result.csca) variance(result.csca) contribution(result.csca, 1) contribution(result.csca, 2) contribution(result.csca, 1:3, mode = "variable") # Plots map.ind(result.csca) map.csa.mca(result.csca) map.csa.mca.array(result.csca) ## End(Not run)
Convert to MCA class from FactoMineR
to.MCA(object, active, dim = 1:5)to.MCA(object, active, dim = 1:5)
object |
is a soc.ca object |
active |
the active variables |
dim |
a numeric vector |
an FactoMineR class object
variance returns a table of variance for the selected dimensions.
variance(object, dim = NULL)variance(object, dim = NULL)
object |
is a soc.ca object |
dim |
is the included dimensions, if set to NULL, then only the dimensions explaining approx. more than 0.90 of the adjusted variance are included |
If assigned variance returns a matrix version of the table of variance.
example(soc.ca) variance(result) variance(result, dim = 1:4)example(soc.ca) variance(result) variance(result, dim = 1:4)
Performs tests on what has been passed on to soc.mca by the user.
what.is.x(x)what.is.x(x)
x |
the active variables sent to soc.mca |
a character vector with an evaluation of whether x is data.frame, a list of data.frames, an indicator or a list of indicators.
## Not run: # Valid scenarios ---- # X is a valid data.frame x <- taste[, 2:7] what.is.x(x) # X is a valid indicator x <- indicator(taste[, 2:7]) what.is.x(x) # X is a valid list of data.frames with names x <- list(nif = taste[, 2:3], hurma = taste[, 4:5]) what.is.x(x) # X is a valid list of indicators x <- list(nif = indicator(taste[, 2:3]), hurma = indicator(taste[, 4:5])) what.is.x(x) # Invalid scenarios ---- # X is a matrix - but not numeric x <- as.matrix(taste[, 2:7]) what.is.x(x) # X is a of data.frames list but does not have names x <- list(taste[, 1:3], taste[, 4:5]) what.is.x(x) # X is a list of indicators but does not have names x <- list(indicator(taste[, 2:3]), indicator(taste[, 4:5])) what.is.x(x) # X is a data.frame and contains NA x <- taste[, 2:7] x[1,1] <- NA what.is.x(x) # X is a list of indicators and contains NA x <- list(nif = indicator(taste[, 2:3]), hurma = indicator(taste[, 4:5])) x[[1]][1,1] <- NA what.is.x(x) # X contains elements that are neither a matrix nor a data.frame x <- list(nif = 1:10, taste[, 1:3], taste[, 4:7]) what.is.x(x) # X contains both indicators and matrixes x <- list(nif = taste[, 2:3], hurma = indicator(taste[, 5:6])) what.is.x(x) ## End(Not run)## Not run: # Valid scenarios ---- # X is a valid data.frame x <- taste[, 2:7] what.is.x(x) # X is a valid indicator x <- indicator(taste[, 2:7]) what.is.x(x) # X is a valid list of data.frames with names x <- list(nif = taste[, 2:3], hurma = taste[, 4:5]) what.is.x(x) # X is a valid list of indicators x <- list(nif = indicator(taste[, 2:3]), hurma = indicator(taste[, 4:5])) what.is.x(x) # Invalid scenarios ---- # X is a matrix - but not numeric x <- as.matrix(taste[, 2:7]) what.is.x(x) # X is a of data.frames list but does not have names x <- list(taste[, 1:3], taste[, 4:5]) what.is.x(x) # X is a list of indicators but does not have names x <- list(indicator(taste[, 2:3]), indicator(taste[, 4:5])) what.is.x(x) # X is a data.frame and contains NA x <- taste[, 2:7] x[1,1] <- NA what.is.x(x) # X is a list of indicators and contains NA x <- list(nif = indicator(taste[, 2:3]), hurma = indicator(taste[, 4:5])) x[[1]][1,1] <- NA what.is.x(x) # X contains elements that are neither a matrix nor a data.frame x <- list(nif = 1:10, taste[, 1:3], taste[, 4:7]) what.is.x(x) # X contains both indicators and matrixes x <- list(nif = taste[, 2:3], hurma = indicator(taste[, 5:6])) what.is.x(x) ## End(Not run)