soc.mca: soc.mca
`soc.mca` performs a specific multiple correspondence analysis on a data.frame of factors, where cases are rows and columns are variables.

Description

Specific Multiple Correspondence Analysis

Usage

soc.mca(
  active,
  sup = NULL,
  identifier = NULL,
  passive = getOption("passive", default = "Missing"),
  weight = NULL,
  Moschidis = FALSE,
  detailed.results = FALSE
)

Arguments

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 afterwardsds.

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.

Value

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

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<U+2019>s Theories and Methods to Study the Community. Saarbr<U+00FC>cken: VDM Verlag Dr. M<U+00FC>ller, 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

References

Le Roux, B., og H. Rouanet. 2010. Multiple correspondence analysis. Thousand Oaks: Sage.

Examples

Run this code

# NOT RUN {
# 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)
# }