Visualize results from coseq clustering
Description
Plot a coseq object.
Usage
1 2 3 4 5 6  ## S3 method for class 'coseq'
plot(x, y_profiles = NULL, K = NULL, threshold = 0.8,
conds = NULL, average_over_conds = FALSE, graphs = c("logLike", "ICL",
"profiles", "boxplots", "probapost_boxplots", "probapost_barplots",
"probapost_histogram", "lambda_barplots"), order = FALSE,
profiles_order = NULL, n_row = NULL, n_col = NULL, ...)

Arguments
x 
An object of class 
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. If 
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 
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 
... 
Additional optional plotting arguments 
Author(s)
Andrea Rau, Cathy MaugisRabusseau
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  ## 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(y=countmat, K=2:4, iter=5, transformation="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(y=countmat, K=2:4, iter=5, transformation="logit")
compareICL(list(run_arcsin, run_logit))
