# Do tuning parameter K and R selection for sparse biclustering via cross-validation. Perform cross-validation to select K (number of row clusters) and R (number of column clusters) for sparse biclustering. We assume that lambda is known. ```r sparseBC.choosekr(x, k, r, lambda, percent = 0.1, trace=FALSE) ``` ## Arguments - `x`: Data matrix; samples are rows and columns are features. Cannot contain missing values. - `k`: A range of values of K to be considered. Values considered must be an increasing sequence. - `r`: A range of values of R to be considered. Values considered must be an increasing sequence. - `lambda`: Non-negative regularization parameter for lasso. lambda=0 means no regularization. - `percent`: Percentage of elements of x to be left out for cross-validation. 1 must be divisible by the specified percentage. The default value is 0.1. - `trace`: Print out progress as iterations are performed. Default is FALSE. ## Details The function performs cross-validation as described in Algorithm (2) in Tan and Witten (2014) 'Sparse biclustering of transposable data'. Briefly, it works as follows: (1) some percent of the elements of x is removed at random from the data matrix - call those elements missing elements, (2) the missing elements are imputed using the mean of the other elements of the matrix, (3) sparse biclustering is performed with various values of K and R and the mean matrix is estimated, (4) calculate the sum of squared error of the missing values between x and the estimated mean matrix. This procedure is repeated 1/percent times. Finally, we select K and R based on the criterion described in Algorithm (2) in Tan and Witten (2014). A similar procedure is used in Witten et al (2009) 'A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis'. Note that sparseBC is run with center=TRUE in this function. ## Returns - **estimated_kr**: The chosen values of K and R based on cross-validation. - **results.mean**: Mean squared error for all values of K and R considered. - **results.se**: Standard error of the sum of squared error for all values of K and R considered. ## References KM Tan and D Witten (2014) Sparse biclustering of transposable data. **Journal of Computational and Graphical Statistics** 23(4):985-1008. D Witten, R Tibshirani, and T Hastie (2009) A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis, **Biostatistics** 10(3), 515--534. ## Author(s) Kean Ming Tan and Daniela Witten ## See Also `sparseBC` `sparseBC.BIC` ## Examples ```r ########### Create data matrix with K=2 R=4 row and column clusters #k <- 2 #r <- 4 #n <- 200 #p <- 200 # mus<-runif(k*r,-3,3) # mus<-matrix(c(mus),nrow=k,ncol=r,byrow=FALSE) # truthCs<-sample(1:k,n,rep=TRUE) # truthDs<-sample(1:r,p,rep=TRUE) # x<-matrix(rnorm(n*p,mean=0,sd=2),nrow=n,ncol=p) # for(i in 1:max(truthCs)){ # for(j in 1:max(truthDs)){ # x[truthCs==i, truthDs==j] <- x[truthCs==i, truthDs==j] + mus[i,j] # } # } # x<-x-mean(x) # Example is commented out for short run-time ############ Perform sparseBC.choosekr to choose the number of row and column clusters #sparseBC.choosekr(x,1:5,1:5,0,0.2)$estimated_kr ```

sparseBC.choosekr function