ModelMixRHLP-class: A Reference Class which represents a fitted mixture of RHLP...

In fchamroukhi/mixRHLP: Mixture of Regression Models with Hidden Logistic Processes ('mixRHLP') for Simultaneous Curve Clustering and Segmentation

Description

ModelMixRHLP represents an estimated mixture of RHLP model.

Fields

param

A ParamMixRHLP object. It contains the estimated values of the parameters.

stat

A StatMixRHLP object. It contains all the statistics associated to the MixRHLP model.

Methods

plot(what = c("estimatedsignal", "regressors", "loglikelihood"), ...)

Plot method.

what

The type of graph requested:

• "estimatedsignal" = Estimated signal (field Ey of class StatMixRHLP).

• "regressors" = Polynomial regression components (fields polynomials and pi_jkr of class StatMixRHLP).

• "loglikelihood" = Value of the log-likelihood for each iteration (field stored_loglik of class StatMixRHLP).

...

Other graphics parameters.

By default, all the above graphs are produced.

summary(digits = getOption("digits"))

Summary method.

digits

The number of significant digits to use when printing.

See Also

ParamMixRHLP, StatMixRHLP

Examples

data(toydataset)

# Let's fit a mixRHLP model on a dataset containing 2 clusters:
data <- toydataset[1:190,1:21]
x <- data$x
Y <- t(data[,2:ncol(data)])

mixrhlp <- cemMixRHLP(X = x, Y = Y, K = 2, R = 2, p = 1, verbose = TRUE)

# mixrhlp is a ModelMixRHLP object. It contains some methods such as 'summary' and 'plot'
mixrhlp$summary()
mixrhlp$plot()

# mixrhlp has also two fields, stat and param which are reference classes as well

# Log-likelihood:
mixrhlp$stat$loglik

# Parameters of the polynomial regressions:
mixrhlp$param$beta

fchamroukhi/mixRHLP documentation built on Sept. 23, 2019, 4:19 a.m.