# Cross-validation of Predictive K-means Clustering Performs cross-validation of predictive k-means clustering and cluster prediction. ```r predkmeansCVest( X, R, K, cv.groups = 10, sigma2 = 0, sigma2fixed = FALSE, scale = TRUE, covarnames = colnames(R), PCA = FALSE, PCAcontrol = list(covarnames = colnames(R), ncomps = 5), TPRS = FALSE, TPRScontrol = list(df = 5, xname = "x", yname = "y"), returnAll = FALSE, ... ) predkmeansCVpred( object, X = object$X, R = object$R, method = c("ML", "MixExp", "SVM"), ... ) ``` ## Arguments - `X`: Outcome data - `R`: Covariates. Coerced to data frame. - `K`: Number of clusters - `cv.groups`: A list providing the cross-validation groups for splitting the data. groups for splitting the data. Alternatively, a single number giving the number of groups into which the data are randomly split. A value of '0' implies leave-one-out. Defaults to 10. - `sigma2`: starting value of sigma2. Setting `sigma2=0` and `sigma2fixed=TRUE` results in regular k-means clustering. - `sigma2fixed`: Logical indicating whether sigma2 should be held fixed. If FALSE, then sigma2 is estimated using Maximum Likelihood. - `scale`: Should the outcomes be re-scaled within each training group? - `covarnames`: Names of covariates to be included directly. - `PCA`: Logical indicator for whether PCA components should be computed from R. - `PCAcontrol`: Arguments passed to `createPCAmodelmatrix`. This includes `ncomps`. - `TPRS`: Logical indicator for whether thin-plate regression splines should be created and added to covariates. - `TPRScontrol`: Arguments passed to `createTPRSmodelmatrix`. This includes `df`. - `returnAll`: A list containing all `nStarts` solutions is included in the output. - `...`: Additional arguments passed to either `predkmeans` or the prediction method. - `object`: A `predkmeansCVest` object. - `method`: Character string indicating which prediciton method should be used. Optins are `ML`, `MixExp`, and `SVM`. See `predictML` for more information. ## Details These wrappers are designed to simplify cross-validation of a dataset. For models including thin-plate regression splines (TPRS) or principal component analysis (PCA) scores, these functions will re-evaluate the TPRS basis or PCA decomposition on each training set. ## Examples ```r n <- 200 r1 <- rnorm(n) r2 <- rnorm(n) u1 <- rbinom(n, size=1,prob=0) cluster <- ifelse(r1<0, ifelse(u1, "A", "B"), ifelse(r2<0, "C", "D")) mu1 <- c(A=2, B=2, C=-2, D=-2) mu2 <- c(A=1, B=-1, C=-1, D=-1) x1 <- rnorm(n, mu1[cluster], 4) x2 <- rnorm(n, mu2[cluster], 4) R <- model.matrix(~r1 + r2) X <- cbind(x1, x2) pkmcv <- predkmeansCVest(X=cbind(x1, x2), R=R, K=4, nStarts=4, cv.groups= 5, TPRS=FALSE, PCA=FALSE, covarnames=colnames(R)) pkmcv ``` ## See Also `predkmeans`, `createPCAmodelmatrix`, `createTPRSmodelmatrix` ## Author(s) Joshua Keller

predkmeansCVest function