soc.mca: soc.mca 'soc.mca' performs a specific multiple correspondence...
In soc.ca: Specific Correspondence Analysis for the Social Sciences

Description Usage Arguments Value Author(s) References See Also Examples

Specific Multiple Correspondence Analysis

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 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.

`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
`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.

soc.csa, contribution

# 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)

Loading required package: ggplot2
                        Specific Multiple Correspondence Analysis:                         
 
                    Statistics                                   Scree plot               
	Active dimensions:                            12  |  1.     47.6%   ************************
	Dimensions explaining 80% of inertia:          3  |  2.     21.5%   ***********
	Active modalities:                            29  |  3.     11.8%   ******
	Supplementary modalities:                     14  |  4.      7.1%   ****
	Individuals:                                1215  |  5.      5.0%   **
	Share of passive mass:                         0  |  6.      3.0%   **
	Number of passive modalities:                  0  |  7.      1.7%   *

                    The 4 active variables: [No. modalities - share of variance]                    

             TV [8 - 28%]            Film [8 - 28%]             Art [7 - 24%] 
            Eat [6 - 20%]
                        Specific Multiple Correspondence Analysis:                         
 
                    Statistics                                   Scree plot               
	Active dimensions:                            11  |  1.     45.8%   ***********************
	Dimensions explaining 80% of inertia:          3  |  2.     27.9%   **************
	Active modalities:                            27  |  3.      8.8%   ****
	Supplementary modalities:                     14  |  4.      6.5%   ***
	Individuals:                                1215  |  5.      4.2%   **
	Share of passive mass:                      0.06  |  6.      3.4%   **
	Number of passive modalities:                  2  |  7.      1.5%   *

                    The 4 active variables: [No. modalities - share of variance]                    

             TV [7 - 24%]            Film [7 - 24%]             Art [7 - 24%] 
            Eat [6 - 20%]