1d:T7d2,# Plot `crs` objects Plots results of correction (1st plot: estimated response functions, 2nd plot: coefficient plot. See Appendix A of the reference paper for the 2nd plot). ```r ## S3 method for class 'crs' plot(x, H = NULL, cls.rs.vec = NULL, ...) ``` ## Arguments - `x`: An object of class `crs`. - `H`: An integer indicating the number of response-style-based clusters to display the correction result. If `H=NULL` and `cls.rs.vec=NULL`, `H` is set as `H=n`. If `H=NULL` but `cls.rs.vec!=NULL`, `H` is set as `H=max(cls.rs.vec)`. The default is `H=NULL`. - `cls.rs.vec`: An integer vector of length n indicating response-style-based clusters for n respondents. If `cls.rs.vec=NULL` and `H!=NULL`, clusters are determined by k-means clustering of Beta. The default is `cls.rs.vec=NULL`. - ``...``: Additional arguments passed to `plot`. ## Details Correction results for each respondent are displayed. If either response-style-based clusters or the number of response-style-based clusters are specified, the correction results of response-style-based clusters are displayed. ## Examples ```r ###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$X ccrsdata.list <- create.ccrsdata(X,q=q) crs.list <- correct.rs(ccrsdata.list) ###You can check correction result using this \code{crs.plot} function. plot(crs.list) #####You can also check correction result obtained #####by a simultaneous analysis of correction and content-based clustering. ###CCRS lam <- 0.8 ; K <- 2 ccrs.list <- ccrs(ccrsdata.list,K=K,lam=lam) ###check correction result using this \code{crs.plot} function. plot(ccrs.list$crs.list) ``` ## 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 `ccrs`

plot.crs function