View source: R/stationary_moments.R
stationary_moments | R Documentation |
hhh4
-modelReturns the mean vector and covariance matrix of the periodically stationary distribution implied by an hhh4 object.
stationary_moments(
hhh4Obj,
start = 1,
n_seasons = 1,
return_Sigma = FALSE,
return_cov_array = FALSE,
return_mu_decomposed = FALSE,
return_M = FALSE,
max.iter = 10,
tolerance = 1e-05
)
hhh4Obj |
|
start |
start of the season |
n_seasons |
number of |
return_Sigma |
logical: return entire variance/covariance matrix of the prediction; can take a lot of storage |
return_cov_array |
logical: return an array containing week-wise covariance matrices |
return_mu_decomposed |
logical: return an array containing a decomposition of
stationary means into the three
components |
return_M |
logical: return the array M containing un-centered second moments (used internally for calculations) |
max.iter |
maximum number of iterations before iterative algorithm stops |
tolerance |
element-wise maximum tolerance (entering into termination criterion for the iterative calculation) |
An object of class stationary_moments_hhh4
containing the following components:
mu_matrix
A matrix containing the stationary means. Each row corresponds
to a time period and each column to a unit.
var_matrix
A matrix containing the stationary variances.
cov_array
An array containing time period-wise variance-covariance matrices.
mu_vector
as mu_matrix
, but flattened into a vector.
Sigma
a large covariance matrix for all elements of the prediction
(corresponding to mu_vector
)
M
a matrix containing stationary means and (un-centered) second moments,
specifically E(c(1, X)
Important in the internal calculation, accessible mainly for de-bugging purposes.
mu_decomposed
an array with the same number of rows and columns as
mu_matrix
, but three layers corresponding to the contributions of the three components
to the means
start
the position (within a cycle) of the time period to which the first elements of
mu_matrix
etc. correspond (i.e. the start
argument from the call of
stationary_moments
)
freq
the length of a cycle
n_seasons
the number of seasons covered in mu_matrix
etc.
n_units
the number of units covered in the prediction
timepoints
the positions within a cycle of the timepoints covered by mu_matrix
etc.
condition
NULL
. Only relevant in predictive moments, just a place holder here.
type
"stationary"
; to distinguish from predictive moments.
has_temporal_structure
does the object still have the original temporal structure? can
be set to FALSE
when aggregated using aggregate_prediction
.
data("salmonella.agona")
## convert old "disProg" to new "sts" data class
salmonella <- disProg2sts(salmonella.agona)
# specify and fit model
control_salmonella <- list(end = list(f = addSeason2formula(~ 1), lag = 1),
ar = list(f = addSeason2formula(~ 1), lag = 1),
family = "NegBinM")
fit_salmonella <- hhh4(salmonella, control_salmonella)
# obtain periodically stationary moments:
stat_mom <- stationary_moments(fit_salmonella)
# plot periodically stationary means:
fanplot_stationary(stat_mom)
# add paths of the six seasons in the data set:
for(i in 0:5){
lines(1:52/52, salmonella@observed[(i*52 + 1):((i + 1)*52)], col = "blue")
}
legend("topleft", col = "blue", lty = 1, legend = "observed seasons")
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