View source: R/bootvalid_variables.R
bootvalid_variables | R Documentation |
Bootstrap validation of MCA, through the computation of the coordinates of active variables for bootstrap replications of the data.
bootvalid_variables(resmca, axes = c(1,2), type = "partial", K = 30)
resmca |
object of class |
axes |
numeric vector of length 2, specifying the components (axes) to plot. Default is c(1,2). |
type |
character string. Can be "partial", "total1", "total2" or "total3" (see details). Default is "partial". |
K |
integer. Number of bootstrap replications (default is 30). |
The bootstrap technique is used here as an internal and non-parametric validation procedure of the results of a multiple correspondence analysis. Following the work of Ludovic Lebart, several methods are proposed. The "total bootstrap" uses new MCAs computed from bootstrap replications of the initial data. In the type 1 total bootstrap (type
= "total1"), the sign of the coordinates is corrected if necessary (the direction of the axes of an ACM being arbitrary). In type 2 (type
= "total2"), the order of the axes and the sign of the coordinates are corrected if necessary. In type 3 (type
= "total3"), a procrustean rotation is used to find the best superposition between the initial axes and the replicated axes.
The "partial bootstrap"" (type
= "partial") does not compute new MCAs: it projects bootstrap replications of the initial data as supplementary elements of the MCA. It gives a more optimistic view of the stability of the results than the total bootstrap. It also runs faster. See references for more details, pros and cons of the various types, etc.
A data frame with the following elements :
varcat |
Names of the active categories |
K |
Indexes of the bootstrap replications |
dim.x |
Bootstrap coordinates on the first selected axis |
dim.y |
Bootstrap coordinates on the second selected axis |
Nicolas Robette
Lebart L. (2006). "Validation Techniques in Multiple Correspondence Analysis". In M. Greenacre et J. Blasius (eds), Multiple Correspondence Analysis and related techniques, Chapman and Hall/CRC, p.179-196.
Lebart L. (2007). "Which bootstrap for principal axes methods?". In P. Brito et al. (eds), Selected Contributions in Data Analysis and Classification, Springer, p.581-588.
ggbootvalid_variables
, bootvalid_supvars
data(Taste)
junk <- c("FrenchPop.NA", "Rap.NA", "Rock.NA", "Jazz.NA", "Classical.NA",
"Comedy.NA", "Crime.NA", "Animation.NA", "SciFi.NA", "Love.NA",
"Musical.NA")
resmca <- speMCA(Taste[,1:11], excl = junk)
bv <- bootvalid_variables(resmca, type = "partial", K = 5)
str(bv)
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