SM.dlm.fitted-class: Class SM.dlm.fitted - An S4 class to represent the fitted...

In 4301350/sm4sd: Statistical Modeling for Sensor Data

Description

The SM.dlm.fitted class store information about fitted dynamic linear model and its pre-definition.

Objects from the Class

SM.dlm.fitted is created by applying method fit to the SM.dlm class.

Slots

parameters

A list containing the best best parameters which are used to build the corresponded model by getMod.

filtered

A list contaning the filtered value. For more information, check the Value section of dlmFilter.

smoothed

A list contaning the smoothed value. See also dlmSmooth.

lags

A list contaning the best lag for each outlier indicator. For more detail, see the section Details of SM.dlm.

seasonalSign

A character indicating the significance of sesonal part in the model:

• null: if the data doesn't have sesonal part (freq == 1)

• TRUE: the seasonality is significant in the model

• FALSE: the seasonality is not significant

. Check details for class building.

tracking

A data frame contaning the tracking of the gridsearch history ordered by the negative log likelihood.

Methods

Available methods for this class are:

• show

• print

• get ('['): giving the value of a slot

• getMod: returning the dlm model

• residualDiag: residual diagnostic for model validation

• extractMeasures: measurement extraction and visualization.

Extends

From SM.dlm, directly.

Examples

n <- 50
rs.class <- RS(sigb = rnorm(n, mean = 0, sd = 1),
               epsb = rnorm(n, mean = 0, sd = 2),
               epsp = rnorm(n, mean = 1, sd = 1))
# The inputs we created are random variable,
# then they have non-stationality structure.
freq <- 1
# Suppose there is a rain at index 1 and 20, and irrigation at 5.
index <- list(rain = c(1, 20), watering = c(5))
lagMax <- 10
verify <- parallel <- TRUE
lagMax <- 1

 dlm.class <- buildClass(object = rs.class, method = "dlm",
                        freq = freq, ind = index, lagMax = lagMax,
                        verify = verify, parallel = parallel)
 dlm.fitted.class <- fit(dlm.class)
 dlm_model <- getMod(dlm.fitted.class)

4301350/sm4sd documentation built on June 14, 2020, 4:22 p.m.