plot {coseq} | R Documentation |
Plot a coseqResults object.
plot(x, ...) ## S4 method for signature 'coseqResults' plot( x, y_profiles = NULL, K = NULL, threshold = 0.8, conds = NULL, average_over_conds = FALSE, collapse_reps = "none", graphs = c("logLike", "ICL", "profiles", "boxplots", "probapost_boxplots", "probapost_barplots", "probapost_histogram"), order = FALSE, profiles_order = NULL, n_row = NULL, n_col = NULL, add_lines = TRUE, ... ) coseqGlobalPlots(object, graphs = c("logLike", "ICL"), ...) coseqModelPlots( probaPost, y_profiles, K = NULL, threshold = 0.8, conds = NULL, collapse_reps = "none", graphs = c("profiles", "boxplots", "probapost_boxplots", "probapost_barplots", "probapost_histogram"), order = FALSE, profiles_order = NULL, n_row = NULL, n_col = NULL, add_lines = TRUE, ... )
x |
An object of class |
... |
Additional optional plotting arguments (e.g., xlab, ylab, use_sample_names, facet_labels) |
y_profiles |
y (n x q) matrix of observed profiles for n
observations and q variables to be used for graphing results (optional for
|
K |
If desired, the specific model to use for plotting (or the specific cluster number(s)
to use for plotting in the case of |
threshold |
Threshold used for maximum conditional probability; only observations with maximum conditional probability greater than this threshold are visualized |
conds |
Condition labels, if desired |
average_over_conds |
If |
collapse_reps |
If |
graphs |
Graphs to be produced, one (or more) of the following:
|
order |
If |
profiles_order |
If |
n_row |
Number of rows for plotting layout of line plots and boxplots of profiles.
Note that if |
n_col |
Number of columns for plotting layout of line plots and boxplots of profiles.
Note that if |
add_lines |
If |
object |
An object of class |
probaPost |
Matrix or data.frame of dimension (n x K) containing the conditional probilities of cluster membership for n genes in K clusters arising from a mixture model |
Named list of plots of the coseqResults
object.
Andrea Rau, Cathy Maugis-Rabusseau
## 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