# relevel.predkmeans: Re-order cluster labels In predkmeans: Covariate Adaptive Clustering

## Description

Function for re-ordering the order of clusters in a predkmeans object.

## Usage

 ```1 2``` ```## S3 method for class 'predkmeans' relevel(x, ref = NULL, order = NULL, ...) ```

## Arguments

 `x` object of class `predkmeans` `ref` New reference group ("Cluster 1"). Only used if `order` is NULL. `order` New order of clusters. `...` Ignored additional arguments.

## Details

The elements of the `order` argument should refer to the current position of clusters, with the position giving the new order. So `c(3, 1, 2)` moves 1 to 2, 2 to 3, and 3 to 1.

## Author(s)

Joshua Keller

Other methods for predkmeans objects: `predictML.predkmeans()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```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) pkm <- predkmeans(X=cbind(x1, x2), R=R, K=4) table(pkm\$cluster) # Move cluster '4' to be first pkm2 <- relevel(pkm, ref=4) table(pkm2\$cluster) # Re-order based upon number of observations in each cluster pkm3 <- relevel(pkm, order=order(table(pkm\$cluster), decreasing=TRUE)) table(pkm3\$cluster) ```