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
)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.
Defines the supplementary modalities in a data.frame with rows of individuals and columns of factors, without NA's
A single vector containing a single value for each row/individual in x and sup. Typically a name or an id.number.
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.
a numeric vector with the weights for the individual rows. The weight is normalized afterwardsds.
If TRUE adjusts contribution values for rare modalities. see moschidis.
If FALSE the result object is trimmed to reduce its memory footprint.
Number of active dimensions
The number of active individuals
The number of active modalities
Eigenvectors
The sum of inertia
A matrix with all active dimensions, adjusted and unadjusted inertias. See variance
Frequencies for the active modalities. See add.to.label
Frequencies for the supplementary modalities. See add.to.label
A matrix with the contribution values of the active modalities per dimension. See contribution
A matrix with the contribution values of the individuals per dimension.
The correlation or quality of each modality per dimension.
The correlation or quality of each individual per dimension.
The mass of each modality
A matrix with the principal coordinates of each active modality per dimension.
A matrix with the principal coordinates of each individual per dimension.
A matrix with the principal coordinates of each supplementary modality per dimension.
The names of the active modalities
The names of the individuals
The names of the supplementary modalities
The names of the passive modalities
A matrix with the number of modalities per variable and their location
A character vector with the name of the variable of the active modalities
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.
A matrix with a the student t-test of the coordinates of the supplementary variables
A matrix the share of variance for each variable
Le Roux, B., og H. Rouanet. 2010. Multiple correspondence analysis. Thousand Oaks: Sage.
# 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)
# }
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