# Diagnostic plot for the number of iterations used in the varselbest function `chooseB` plots the proportion of times an explanatory variable is selected according to the number of iterations (B). ```r chooseB( res.varselbest, plotvar = NULL, linewidth = 1, linetype = "dotdash", xlab = "B", ylab = "Proportion", nrow = 2, ncol = 2, graph = TRUE ) ``` ## Arguments - `res.varselbest`: an output from the varselbest function - `plotvar`: index of variables for which a curve is ploted - `linewidth`: a numerical value setting the widths of lines - `linetype`: what type of plot should be drawn - `xlab`: a title for the x axis - `ylab`: a title for the y axis - `nrow`: argument of gtable. Default value is 2. - `ncol`: argument of gtable. Default value is 2. - `graph`: a boolean. If FALSE, no graphics are ploted. Default value is TRUE ## Returns a list of matrices where each row corresponds to the vector of proportions (for all explanatory variables) obtained for a given value of B ## Details `varselbest` performs variable selection on random subsets of variables and, then, combines them to recover which explanatory variables are related to the response, following Bar-Hen and Audigier (2022) . More precisely, the outline of the algorithm are as follows: let consider a random subset of `sizeblock` among p variables. Then, any selection variables scheme can be applied. By resampling `B` times, a sample of size `sizeblock` among the p variables, we may count how many times a variable is considered as significantly related to the response and how many times it is not. The number of iterations `B` should be large so that the proportion of times a variable is selected becomes stable. `chooseB` plots the values of proportion according to the number of iterations. ## Examples ```r data(wine, package = "clusterMI") require(parallel) ref <- wine$cult nb.clust <- 3 wine.na<-wine wine.na$cult <- NULL wine.na <- prodna(wine.na) nnodes <- 2 # Number of CPU cores for parallel computing B <- 80 # Number of iterations for variable selection # variable selection res.varsel <- varselbest(data.na = wine.na, listvar = "alco", B = B, nnodes = nnodes, nb.clust = nb.clust, graph = FALSE) # convergence res.chooseB <- chooseB(res.varsel) ``` ## References Bar-Hen, A. and Audigier, V., An ensemble learning method for variable selection: application to high dimensional data and missing values, Journal of Statistical Computation and Simulation, , 2022. ## See Also `varselbest`

chooseB function