compareARI {coseq} | R Documentation |
Provides the adjusted rand index (ARI) between pairs of clustering paritions.
compareARI(object, ...) ## S4 method for signature 'coseqResults' compareARI( object, K = NULL, parallel = FALSE, BPPARAM = bpparam(), plot = TRUE, ... ) ## S4 method for signature 'matrix' compareARI(object, parallel = FALSE, BPPARAM = bpparam(), plot = TRUE, ...) ## S4 method for signature 'data.frame' compareARI(object, parallel = FALSE, BPPARAM = bpparam(), plot = TRUE, ...)
object |
Object of class |
... |
Additional optional parameters for corrplot |
K |
If |
parallel |
If |
BPPARAM |
Optional parameter object passed internally to |
plot |
If |
Matrix of adjusted rand index values calculated between each pair of models.
Andrea Rau
## Simulate toy data, n = 300 observations set.seed(12345) countmat <- matrix(runif(300*4, min=0, max=500), nrow=300, ncol=4) countmat <- countmat[which(rowSums(countmat) > 0),] conds <- rep(c("A","B","C","D"), each=2) ## Run the Normal mixture model for K = 2,3,4 run_arcsin <- coseq(object=countmat, K=2:4, iter=5, transformation="arcsin", model="Normal", seed=12345) run_arcsin ## Plot and summarize results plot(run_arcsin) summary(run_arcsin) ## Compare ARI values for all models (no plot generated here) ARI <- compareARI(run_arcsin, plot=FALSE) ## Compare ICL values for models with arcsin and logit transformations run_logit <- coseq(object=countmat, K=2:4, iter=5, transformation="logit", model="Normal") compareICL(list(run_arcsin, run_logit)) ## Use accessor functions to explore results clusters(run_arcsin) likelihood(run_arcsin) nbCluster(run_arcsin) ICL(run_arcsin) ## Examine transformed counts and profiles used for graphing tcounts(run_arcsin) profiles(run_arcsin) ## Run the K-means algorithm for logclr profiles for K = 2,..., 20 run_kmeans <- coseq(object=countmat, K=2:20, transformation="logclr", model="kmeans") run_kmeans