Example | R Documentation |
Simulated data, used to demonstrate the features of datacggm class. It contains two simulated datasets that mimic the structural characteristics of the two real datasets MKMEP and MM.
MKMEP.Sim
: is a reponse matrix containing 50 samples of 10 genes;
MM.Sim
: is a list containing two components;
Y
: is the response matrix containing 50 measurements of 10 miRNAs
X
: is the predictor matrix containing 50 measurements of 5 variables (four numerical and one categorical)
In both datasets, the response matrices are right-censored with an upper limit of detection fixed to 40.
data("Example")
cglasso
, to_graph
, and the method functions summary
, coef
, plot
, AIC.cglasso
, BIC.cglasso
, MKMEP
and MM
.
data("Example") MM.Sim <- datacggm(Y = MM.Sim$Y, up = 40, X = MM.Sim$X) ColMeans(MM.Sim) ColVars(MM.Sim) summary(MM.Sim) MKMEP.Sim <- datacggm(Y = MKMEP.Sim, up = 40) ColMeans(MKMEP.Sim) ColVars(MKMEP.Sim) summary(MKMEP.Sim)
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