dGLM: Class for NHPP-based software reliability model with...

Description Usage Format Value Fields Methods See Also

Description

Class for NHPP-based software reliability model with d-metrics

Usage

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Format

R6Class object.

Value

Object of R6Class with methods for NHPP-based software reliability model with d-metrics.

Fields

name

A character string for the name of model.

params

A numeric vector for the model parameters.

df

An integer for the degrees of freedom of the model.

data

Data to esimate parameters.

Methods

print()

This method prints model parameters.

omega()

This method returns the number of total faults.

coefficients()

This method returns a vector for the coefficients.

mvf(t, data = NULL)

This method returns the mean value function at time t. The d-metrics is given from data. If data is NULL, the d-metrics for the estimation is used.

dmvf(t, data = NULL)

This method returns the intensity function at time t. The d-metrics is given from data. If data is NULL, the d-metrics for the estimation is used.

residual(t, data = NULL)

This method returns the expected residual number of faults at time t. The d-metrics is given from data. If data is NULL, the d-metrics for the estimation is used.

ffp(t, data = NULL)

This method returns the fault-free probability at time t. The d-metrics is given from data. If data is NULL, the d-metrics for the estimation is used.

init_params(data)

This method changes the model parameters based on a given data. This is used to set the initial value for the fitting algorithm.

set_params(params)

This method sets the model parameters.

set_data(data)

This method sets data.

em(params, data)

This method returns a list with an updated parameter vector (param), absolute difference of parameter vector (pdiff), log-likelihood function for a given parameter vector (llf), the number of total faults (total) via EM algorithm for a given data.

llf(data)

This method returns the log-likelihood function for a given data.

See Also

fit.srm.logit


okamumu/Rsrat documentation built on Oct. 3, 2018, 2:36 a.m.