This function sets and checks model parameters. Unspecified parameters are sampled.
Usage
fHMM_parameters(
controls = list(),
hierarchy = FALSE,
states = if (!hierarchy) 2 else c(2, 2),
sdds = if (!hierarchy) "normal" else c("normal", "normal"),
Gamma = NULL,
mu = NULL,
sigma = NULL,
df = NULL,
Gamma_star = NULL,
mu_star = NULL,
sigma_star = NULL,
df_star = NULL,
scale_par = c(1, 1),
seed = NULL,
check_controls = TRUE
)
# S3 method for fHMM_parameters
print(x, ...)
Arguments
- controls
Either a
list
or an object of classfHMM_controls
.The
list
can contain the following elements, which are described in more detail below:hierarchy
, defines an hierarchical HMM,states
, defines the number of states,sdds
, defines the state-dependent distributions,horizon
, defines the time horizon,period
, defines a flexible, periodic fine-scale time horizon,data
, alist
of controls that define the data,fit
, alist
of controls that define the model fitting
Either none, all, or selected elements can be specified.
Unspecified parameters are set to their default values, see below.
Specifications in
controls
override individual specifications.- hierarchy
A
logical
, set toTRUE
for an hierarchical HMM.If
hierarchy = TRUE
, some of the other controls must be specified for the coarse-scale and the fine-scale layer.By default,
hierarchy = FALSE
.- states
An
integer
, the number of states of the underlying Markov chain.If
hierarchy = TRUE
,states
must be avector
of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer.By default,
states = 2
ifhierarchy = FALSE
andstates = c(2, 2)
ifhierarchy = TRUE
.- sdds
A
character
, specifying the state-dependent distribution. One of"normal"
(the normal distribution),"lognormal"
(the log-normal distribution),"t"
(the t-distribution),"gamma"
(the gamma distribution),"poisson"
(the Poisson distribution).
The distribution parameters, i.e. the
mean
mu
,standard deviation
sigma
(not for the Poisson distribution),degrees of freedom
df
(only for the t-distribution),
can be fixed via, e.g.,
"t(df = 1)"
or"gamma(mu = 0, sigma = 1)"
. To fix different values of a parameter for different states, separate by "|", e.g."poisson(mu = 1|2|3)"
.If
hierarchy = TRUE
,sdds
must be avector
of length 2. The first entry corresponds to the coarse-scale layer, while the second entry corresponds to the fine-scale layer.By default,
sdds = "normal"
ifhierarchy = FALSE
andsdds = c("normal", "normal")
ifhierarchy = TRUE
.- Gamma, Gamma_star
A transition probability
matrix
.It should have dimension
states[1]
.Gamma_star
is alist
of fine-scale transition probability matrices. Thelist
must be of lengthstates[1]
. Each transition probability matrix must be of dimensionstates[2]
.- mu, mu_star
A
numeric
vector of expected values for the state-dependent distribution in the different states.For the gamma- or Poisson-distribution,
mu
must be positive.It should have length
states[1]
.mu_star
is alist
ofvectors
with fine-scale expectations. Thelist
must be of lengthstates[1]
. Eachvector
must be of lengthstates[2]
.- sigma, sigma_star
A positive
numeric
vector of standard deviations for the state-dependent distribution in the different states.It should have length
states[1]
.sigma_star
is alist
ofvectors
with fine-scale standard deviations. Thelist
must be of lengthstates[1]
. Each vector must be of lengthstates[2]
.- df, df_star
A positive
numeric
vector of degrees of freedom for the state-dependent distribution in the different states.It should have length
states[1]
.Only relevant in case of a state-dependent t-distribution.
df_star
is alist
ofvectors
with fine-scale degrees of freedom. Thelist
must be of lengthstates[1]
. Each vector must be of lengthstates[2]
. Only relevant in case of a fine-scale state-dependent t-distribution.- scale_par
A positive
numeric
vector of length two, containing scales for sampled expectations and standard deviations.The first entry is the scale for
mu
andsigma
, the second entry is the scale formu_star
andsigma_star
(if any).- seed
Sets a seed for the sampling of parameters.
- check_controls
Either
TRUE
to check the defined controls orFALSE
to not check them (which saves computation time), else.- x
An object of class
fHMM_parameters
.- ...
Currently not used.
Details
See the vignette on the model definition for more details.