ccrs (version 0.1.0)

ccrs: Correcting and Clustering response style biased data

Description

Applies CCRS to ccrsdata.list.

Usage

ccrs(ccrsdata.list,K=K,lam=lam, tandem.initial=FALSE,
            tol = 1e-5, maxit = 50, trace = 1, nstart = 3, parallel=F,verbose=T)

Arguments

ccrsdata.list

A list generated by create.ccrsdata.

K

An integer indicating the number of content-based clusters used for CCRS estimation.

lam

A numeric value indicating lambda used for CCRS estimation.

tandem.initial

A logical value indicating whether the 1st initial value is generated by CCRS tandem initialization. See Section 3.3 in the paper for the detail.

tol

A numeric value indicating the absolute convergence tolerance

maxit

An integer indicating the maximum number of iterations

trace

An non-negative integer. If positive, tracing information on the progress of the optimization is produced. Higher values produce more tracing information.

nstart

An integer indicating the number of random initial values.

parallel

A logical value indicating parallelization over starts is used.

verbose

A logical value indicaitng if the progress is printed during the iteration (only when parallel==FALSE).

Value

Returns a list with the following elements.

G

A K by m matrix of content-based cluster centroid.

cls.cont.vec

A vector of integers (from 1:K) indicating the content-based cluster to which each respondent is allocated.

opt.obval

An optimal value of objective function.

crs.list

A list of class crs, same as the one generated by correct.rs.

References

Takagishi, M., Velden, M. van de & Yadohisa, H. (2019). Clustering preference data in the presence of response style bias, to appear in British Journal of Mathematical and Statistical Psychology.

See Also

correct.rs

Examples

# NOT RUN {
###data setting
n <- 30 ; m <- 10 ; H.true <- 2 ; K.true <- 2 ; q <- 5
datagene <- generate.rsdata(n=n,m=m,K.true=K.true,H.true=H.true,q=q,clustered.rs = TRUE)
###obtain n x m data matrix
X <- datagene$X
ccrsdata.list <- create.ccrsdata(X,q=q)
###CCRS
lam <- 0.8 ; K <- 2
ccrs.list <- ccrs(ccrsdata.list,K=K,lam=lam)
###check content-based clustering result
ccrs.list$cls.cont.vec
###check correction result
plot(ccrs.list$crs.list)
# }