Observation: R6 class for HMM observation model
In TheoMichelot/hmmTMB: Fit Hidden Markov Models using Template Model Builder
Observation R Documentation
R6 class for HMM observation model
Description
Contains the data, distributions, parameters, and formulas for
the observation model from a hidden Markov model.
Methods
Public methods
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Method new()
Create new Observation object
Usage
Observation$new(
data = NULL,
dists,
formulas = NULL,
n_states = NULL,
par,
fixpar = NULL
)
Arguments
dataData frame containing response variables (named in dists
and par) and covariates (named in formulas)
distsNamed list of distribution names for each data stream,
with the following options: beta, binom, cat, dir, exp, foldednorm,
gamma, gamma2, lnorm, mvnorm, nbinom, norm, pois, t, truncnorm, tweedie,
vm, weibull, wrpcauchy, zibinom, zigamma, zigamma2, zinbinom, zipois,
zoibeta, ztnbinom, ztpois. See vignette about list of distributions for
more details, e.g., list of parameters for each distribution.
formulasList of formulas for observation parameters. This should
be a nested list, where the outer list has one element for each
observed variable, and the inner lists have one element for each
parameter. Any parameter that is not included is assumed to have the
formula ~1. By default, all parameters have the formula ~1 (i.e., no
covariate effects).
n_statesNumber of states (optional). If not provided, the number
of states is derived from the length of entries of par.
parList of initial observation parameters. This should
be a nested list, where the outer list has one element for each
observed variable, and the inner lists have one element for each
parameter. The choice of good initial values can be important, especially
for complex models; the package vignettes discuss approaches to selecting
them (e.g., see Observation$suggest_initial()).
fixparList with optional elements "obs" (fixed coefficients for
observation parameters), and "lambda_obs" (fixed smoothness parameters),
Each element is a named vector of coefficients that should either be
fixed (if the corresponding element is set to NA) or estimated to a
common value (using integers or factor levels).
Returns
A new Observation object
Examples
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model "energy" with normal distributions
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0)
# Model "energy" with gamma distributions
obs <- Observation$new(data = energy,
dists = list(Price = "gamma2"),
par = par0)
# Model with non-linear effect of EurDol on mean price
f <- list(Price = list(mean = ~ s(EurDol, k = 5, bs = "cs")))
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
formula = f)
Method data()
Data frame
Usage
Observation$data()
Method dists()
List of distributions
Usage
Observation$dists()
Method nstates()
Number of states
Usage
Observation$nstates()
Method par()
Parameters on natural scale
Usage
Observation$par(t = 1, full_names = TRUE, linpred = NULL, as_list = FALSE)
Arguments
tTime index or vector of time indices; default t = 1. If
t = "all", then return observation parameters for all time points.
full_namesLogical. If TRUE, the rows of the output
are named in the format "variable.parameter" (default). If
FALSE, the rows are names in the format "parameter". The
latter is used in various internal functions, when the parameters
need to be passed on to an R function.
linpredOptional custom linear predictor.
as_listLogical. If TRUE, the output is a nested list with three levels:
(1) time step, (2) observed variable, (3) observation parameter. If FALSE (default),
the output is an array with one row for each observation parameter, one column for
each state, and one slice for each time step.
Returns
Array of parameters with one row for each observation parameter,
one column for each state, and one slice for each time step. (See as_list
argument for alternative output format.)
Examples
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model with linear effect of EurDol on mean price
f <- list(Price = list(mean = ~ EurDol))
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
n_states = 2,
formula = f)
# Set slope coefficients
obs$update_coeff_fe(coeff_fe = c(3, 2, 6, -2, log(2), log(2)))
# Observation parameter values for given data rows
obs$par(t = c(1, 10, 20))
Method par_alt()
Alternative parameter output
This function is only useful for the categorical and multivariate
normal distributions, and it formats the parameters in a slightly nicer
way.
Usage
Observation$par_alt(var = NULL, t = 1)
Arguments
varName of observation variable for which parameters are
required. By default, the first variable in 'dists' is used.
tTime index for covariate values. Only one value should be
provided.
Returns
List of distribution parameters, with one element for each state
Method inipar()
Return initial parameter values supplied
Usage
Observation$inipar()
Method coeff_fe()
Fixed effect parameters on working scale
Usage
Observation$coeff_fe()
Method coeff_re()
Random effect parameters
Usage
Observation$coeff_re()
Method X_fe()
Fixed effect design matrix
Usage
Observation$X_fe()
Method X_re()
Random effect design matrix
Usage
Observation$X_re()
Method lambda()
Smoothness parameters
Usage
Observation$lambda()
Method sd_re()
Standard deviation of smooth terms
This function transforms the smoothness parameter of
each smooth term into a standard deviation, given by
SD = 1/sqrt(lambda). It is particularly helpful to get the
standard deviations of independent normal random effects.
Usage
Observation$sd_re()
Method formulas()
List of model formulas for observation model
Usage
Observation$formulas(raw = FALSE)
Arguments
rawLogical. If FALSE, returns the nested list created by
make_formulas (default). If TRUE, returns formulas passed as input.
Method terms()
Terms of model formulas
Usage
Observation$terms()
Method obs_var()
Data frame of response variables
Usage
Observation$obs_var(expand = FALSE)
Arguments
expandIf TRUE, then multivariate variables in observations are
expanded to be univariate, creating extra columns.
Returns
Data frame of observation variables
Method known_states()
Vector of known states
Usage
Observation$known_states(mat = TRUE)
Arguments
matLogical.
Method fixpar()
Fixed parameters
Usage
Observation$fixpar(all = FALSE)
Arguments
allLogical. If FALSE, only user-specified fixed
parameters are returned, but not parameters that are fixed
for some other reason (e.g., size of binomial distribution)
Method empty()
Empty model? (for simulation only)
Usage
Observation$empty()
Method update_par()
Update parameters
Updates the 'par' attribute to the list passed as input,
and updates the intercept elements of 'coeff_fe' using
the list passed as input
Usage
Observation$update_par(par)
Arguments
parNew list of parameters
Method update_coeff_fe()
Update coefficients for fixed effect parameters
Usage
Observation$update_coeff_fe(coeff_fe)
Arguments
coeff_feNew vector of coefficients for fixed effect
parameters
Method update_coeff_re()
Update random effect parameters
Usage
Observation$update_coeff_re(coeff_re)
Arguments
coeff_reNew vector of coefficients for random effect
parameters
Method update_X_fe()
Update fixed effect design matrix
Usage
Observation$update_X_fe(X_fe)
Arguments
X_feNew fixed effect design matrix
Method update_X_re()
Update random effect design matrix
Usage
Observation$update_X_re(X_re)
Arguments
X_reNew random effect design matrix
Method update_lambda()
Update smoothness parameters
Usage
Observation$update_lambda(lambda)
Arguments
lambdaNew smoothness parameter vector
Method update_data()
Update data
Usage
Observation$update_data(data)
Arguments
dataNew data frame
Method update_fixpar()
Update information about fixed parameters
Usage
Observation$update_fixpar(fixpar)
Arguments
fixparNew list of fixed parameters, in the same format
expected by Observation$new()
Method make_mat()
Make model matrices
Usage
Observation$make_mat(new_data = NULL)
Arguments
new_dataOptional new data set, including covariates for which
the design matrices should be created. If this argument is not specified,
the design matrices are based on the original data frame.
Returns
A list with elements:
- X_fe
Design matrix for fixed effects
- X_re
Design matrix for random effects
- S
Smoothness matrix for random effects
- ncol_fe
Number of columns of X_fe for each parameter
- ncol_re
Number of columns of X_re and S for each random effect
Method make_newdata_grid()
Design matrices for grid of covariates
Usage
Observation$make_newdata_grid(var, covs = NULL, n_grid = 1000)
Arguments
varName of variable
covsOptional named list for values of covariates (other than 'var')
that should be used in the plot (or dataframe with single row). If this is
not specified, the mean value is used for numeric variables, and the
first level for factor variables.
n_gridGrid size (number of points). Default: 1000.
Returns
A list with the same elements as the output of make_mat,
plus a data frame of covariates values.
Method n2w()
Natural to working parameter transformation
This function applies the link functions of the distribution
parameters, to transform parameters from their natural scale
to the working scale (i.e., linear predictor scale)
Usage
Observation$n2w(par)
Arguments
parList of parameters on natural scale
Returns
Vector of parameters on working scale
Method w2n()
Working to natural parameter transformation
This function applies the inverse link functions of the
distribution parameters, to transform parameters from the working
scale (i.e., linear predictor scale) to their natural scale.
Usage
Observation$w2n(wpar)
Arguments
wparVector of parameters on working scale
Returns
List of parameters on natural scale
Method linpred()
Compute linear predictor
Usage
Observation$linpred()
Method obs_probs()
Observation likelihoods
Usage
Observation$obs_probs(data = NULL)
Arguments
dataOptional dataframe to include in form of obs_var() output
Returns
Matrix of likelihoods of observations, with one row for each
time step, and one column for each state.
Method cdf()
Cumulative probabilities of observations
Usage
Observation$cdf()
Returns
List of cumulative probabilities, with one element for each
observed variable. Matrix rows correspond to time steps, and columns
correspond to states.
Method suggest_initial()
Suggest initial observation parameters
The K-means algorithm is used to define clusters of observations
(supposed to approximate the HMM states). Then, for each cluster,
the parapprox function of the relevant Dist object
is used to obtain parameter values.
Usage
Observation$suggest_initial()
Returns
List of initial parameters for each observation variable
Examples
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model "energy" with normal distributions
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
n_states = 2)
# Print observation parameters
obs$par()
# Suggest initial parameters
par0_new <- obs$suggest_initial()
par0_new
# Update model parameters to suggested
obs$update_par(par = par0_new)
obs$par()
Method plot_dist()
Plot histogram of data and pdfs
Plot histogram of observations for the variable specified by the argument name,
overlaid with the pdf of the specified distribution for that data stream.
Helpful to select initial parameter values for model fitting, or to visualise
fitted state-dependent distributions.
Usage
Observation$plot_dist(var = NULL, weights = NULL, t = 1)
Arguments
varName of response variable for which the histogram
and pdfs should be plotted.
weightsOptional vector of length the number of pdfs that are
plotted. Useful to visualise a mixture of distributions weighted by the
proportion of time spent in the different states.
tIndex of time step to use for covariates (default: 1).
Returns
A ggplot object
Method formulation()
Print model formulation
Usage
Observation$formulation()
Method print()
Print Observation object
Check constructor arguments
Usage
Observation$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
Observation$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `Observation$new`
## ------------------------------------------------
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model "energy" with normal distributions
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0)
# Model "energy" with gamma distributions
obs <- Observation$new(data = energy,
dists = list(Price = "gamma2"),
par = par0)
# Model with non-linear effect of EurDol on mean price
f <- list(Price = list(mean = ~ s(EurDol, k = 5, bs = "cs")))
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
formula = f)
## ------------------------------------------------
## Method `Observation$par`
## ------------------------------------------------
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model with linear effect of EurDol on mean price
f <- list(Price = list(mean = ~ EurDol))
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
n_states = 2,
formula = f)
# Set slope coefficients
obs$update_coeff_fe(coeff_fe = c(3, 2, 6, -2, log(2), log(2)))
# Observation parameter values for given data rows
obs$par(t = c(1, 10, 20))
## ------------------------------------------------
## Method `Observation$suggest_initial`
## ------------------------------------------------
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model "energy" with normal distributions
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
n_states = 2)
# Print observation parameters
obs$par()
# Suggest initial parameters
par0_new <- obs$suggest_initial()
par0_new
# Update model parameters to suggested
obs$update_par(par = par0_new)
obs$par()
TheoMichelot/hmmTMB documentation built on Dec. 13, 2024, 11:52 a.m.
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| Observation | R Documentation |
R6 class for HMM observation model
Description
Contains the data, distributions, parameters, and formulas for the observation model from a hidden Markov model.
Methods
Public methods
Method new()
Create new Observation object
Usage
Observation$new( data = NULL, dists, formulas = NULL, n_states = NULL, par, fixpar = NULL )
Arguments
dataData frame containing response variables (named in dists and par) and covariates (named in formulas)
distsNamed list of distribution names for each data stream, with the following options: beta, binom, cat, dir, exp, foldednorm, gamma, gamma2, lnorm, mvnorm, nbinom, norm, pois, t, truncnorm, tweedie, vm, weibull, wrpcauchy, zibinom, zigamma, zigamma2, zinbinom, zipois, zoibeta, ztnbinom, ztpois. See vignette about list of distributions for more details, e.g., list of parameters for each distribution.
formulasList of formulas for observation parameters. This should be a nested list, where the outer list has one element for each observed variable, and the inner lists have one element for each parameter. Any parameter that is not included is assumed to have the formula ~1. By default, all parameters have the formula ~1 (i.e., no covariate effects).
n_statesNumber of states (optional). If not provided, the number of states is derived from the length of entries of
par.parList of initial observation parameters. This should be a nested list, where the outer list has one element for each observed variable, and the inner lists have one element for each parameter. The choice of good initial values can be important, especially for complex models; the package vignettes discuss approaches to selecting them (e.g., see
Observation$suggest_initial()).fixparList with optional elements "obs" (fixed coefficients for observation parameters), and "lambda_obs" (fixed smoothness parameters), Each element is a named vector of coefficients that should either be fixed (if the corresponding element is set to NA) or estimated to a common value (using integers or factor levels).
Returns
A new Observation object
Examples
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model "energy" with normal distributions
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0)
# Model "energy" with gamma distributions
obs <- Observation$new(data = energy,
dists = list(Price = "gamma2"),
par = par0)
# Model with non-linear effect of EurDol on mean price
f <- list(Price = list(mean = ~ s(EurDol, k = 5, bs = "cs")))
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
formula = f)
Method data()
Data frame
Usage
Observation$data()
Method dists()
List of distributions
Usage
Observation$dists()
Method nstates()
Number of states
Usage
Observation$nstates()
Method par()
Parameters on natural scale
Usage
Observation$par(t = 1, full_names = TRUE, linpred = NULL, as_list = FALSE)
Arguments
tTime index or vector of time indices; default t = 1. If t = "all", then return observation parameters for all time points.
full_namesLogical. If TRUE, the rows of the output are named in the format "variable.parameter" (default). If FALSE, the rows are names in the format "parameter". The latter is used in various internal functions, when the parameters need to be passed on to an R function.
linpredOptional custom linear predictor.
as_listLogical. If TRUE, the output is a nested list with three levels: (1) time step, (2) observed variable, (3) observation parameter. If FALSE (default), the output is an array with one row for each observation parameter, one column for each state, and one slice for each time step.
Returns
Array of parameters with one row for each observation parameter, one column for each state, and one slice for each time step. (See as_list argument for alternative output format.)
Examples
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model with linear effect of EurDol on mean price
f <- list(Price = list(mean = ~ EurDol))
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
n_states = 2,
formula = f)
# Set slope coefficients
obs$update_coeff_fe(coeff_fe = c(3, 2, 6, -2, log(2), log(2)))
# Observation parameter values for given data rows
obs$par(t = c(1, 10, 20))
Method par_alt()
Alternative parameter output
This function is only useful for the categorical and multivariate normal distributions, and it formats the parameters in a slightly nicer way.
Usage
Observation$par_alt(var = NULL, t = 1)
Arguments
varName of observation variable for which parameters are required. By default, the first variable in 'dists' is used.
tTime index for covariate values. Only one value should be provided.
Returns
List of distribution parameters, with one element for each state
Method inipar()
Return initial parameter values supplied
Usage
Observation$inipar()
Method coeff_fe()
Fixed effect parameters on working scale
Usage
Observation$coeff_fe()
Method coeff_re()
Random effect parameters
Usage
Observation$coeff_re()
Method X_fe()
Fixed effect design matrix
Usage
Observation$X_fe()
Method X_re()
Random effect design matrix
Usage
Observation$X_re()
Method lambda()
Smoothness parameters
Usage
Observation$lambda()
Method sd_re()
Standard deviation of smooth terms
This function transforms the smoothness parameter of each smooth term into a standard deviation, given by SD = 1/sqrt(lambda). It is particularly helpful to get the standard deviations of independent normal random effects.
Usage
Observation$sd_re()
Method formulas()
List of model formulas for observation model
Usage
Observation$formulas(raw = FALSE)
Arguments
rawLogical. If FALSE, returns the nested list created by make_formulas (default). If TRUE, returns formulas passed as input.
Method terms()
Terms of model formulas
Usage
Observation$terms()
Method obs_var()
Data frame of response variables
Usage
Observation$obs_var(expand = FALSE)
Arguments
expandIf TRUE, then multivariate variables in observations are expanded to be univariate, creating extra columns.
Returns
Data frame of observation variables
Method known_states()
Vector of known states
Usage
Observation$known_states(mat = TRUE)
Arguments
matLogical.
Method fixpar()
Fixed parameters
Usage
Observation$fixpar(all = FALSE)
Arguments
allLogical. If FALSE, only user-specified fixed parameters are returned, but not parameters that are fixed for some other reason (e.g., size of binomial distribution)
Method empty()
Empty model? (for simulation only)
Usage
Observation$empty()
Method update_par()
Update parameters
Updates the 'par' attribute to the list passed as input, and updates the intercept elements of 'coeff_fe' using the list passed as input
Usage
Observation$update_par(par)
Arguments
parNew list of parameters
Method update_coeff_fe()
Update coefficients for fixed effect parameters
Usage
Observation$update_coeff_fe(coeff_fe)
Arguments
coeff_feNew vector of coefficients for fixed effect parameters
Method update_coeff_re()
Update random effect parameters
Usage
Observation$update_coeff_re(coeff_re)
Arguments
coeff_reNew vector of coefficients for random effect parameters
Method update_X_fe()
Update fixed effect design matrix
Usage
Observation$update_X_fe(X_fe)
Arguments
X_feNew fixed effect design matrix
Method update_X_re()
Update random effect design matrix
Usage
Observation$update_X_re(X_re)
Arguments
X_reNew random effect design matrix
Method update_lambda()
Update smoothness parameters
Usage
Observation$update_lambda(lambda)
Arguments
lambdaNew smoothness parameter vector
Method update_data()
Update data
Usage
Observation$update_data(data)
Arguments
dataNew data frame
Method update_fixpar()
Update information about fixed parameters
Usage
Observation$update_fixpar(fixpar)
Arguments
fixparNew list of fixed parameters, in the same format expected by Observation$new()
Method make_mat()
Make model matrices
Usage
Observation$make_mat(new_data = NULL)
Arguments
new_dataOptional new data set, including covariates for which the design matrices should be created. If this argument is not specified, the design matrices are based on the original data frame.
Returns
A list with elements:
- X_fe
Design matrix for fixed effects
- X_re
Design matrix for random effects
- S
Smoothness matrix for random effects
- ncol_fe
Number of columns of X_fe for each parameter
- ncol_re
Number of columns of X_re and S for each random effect
Method make_newdata_grid()
Design matrices for grid of covariates
Usage
Observation$make_newdata_grid(var, covs = NULL, n_grid = 1000)
Arguments
varName of variable
covsOptional named list for values of covariates (other than 'var') that should be used in the plot (or dataframe with single row). If this is not specified, the mean value is used for numeric variables, and the first level for factor variables.
n_gridGrid size (number of points). Default: 1000.
Returns
A list with the same elements as the output of make_mat, plus a data frame of covariates values.
Method n2w()
Natural to working parameter transformation
This function applies the link functions of the distribution parameters, to transform parameters from their natural scale to the working scale (i.e., linear predictor scale)
Usage
Observation$n2w(par)
Arguments
parList of parameters on natural scale
Returns
Vector of parameters on working scale
Method w2n()
Working to natural parameter transformation
This function applies the inverse link functions of the distribution parameters, to transform parameters from the working scale (i.e., linear predictor scale) to their natural scale.
Usage
Observation$w2n(wpar)
Arguments
wparVector of parameters on working scale
Returns
List of parameters on natural scale
Method linpred()
Compute linear predictor
Usage
Observation$linpred()
Method obs_probs()
Observation likelihoods
Usage
Observation$obs_probs(data = NULL)
Arguments
dataOptional dataframe to include in form of obs_var() output
Returns
Matrix of likelihoods of observations, with one row for each time step, and one column for each state.
Method cdf()
Cumulative probabilities of observations
Usage
Observation$cdf()
Returns
List of cumulative probabilities, with one element for each observed variable. Matrix rows correspond to time steps, and columns correspond to states.
Method suggest_initial()
Suggest initial observation parameters
The K-means algorithm is used to define clusters of observations
(supposed to approximate the HMM states). Then, for each cluster,
the parapprox function of the relevant Dist object
is used to obtain parameter values.
Usage
Observation$suggest_initial()
Returns
List of initial parameters for each observation variable
Examples
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model "energy" with normal distributions
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
n_states = 2)
# Print observation parameters
obs$par()
# Suggest initial parameters
par0_new <- obs$suggest_initial()
par0_new
# Update model parameters to suggested
obs$update_par(par = par0_new)
obs$par()
Method plot_dist()
Plot histogram of data and pdfs
Plot histogram of observations for the variable specified by the argument name, overlaid with the pdf of the specified distribution for that data stream. Helpful to select initial parameter values for model fitting, or to visualise fitted state-dependent distributions.
Usage
Observation$plot_dist(var = NULL, weights = NULL, t = 1)
Arguments
varName of response variable for which the histogram and pdfs should be plotted.
weightsOptional vector of length the number of pdfs that are plotted. Useful to visualise a mixture of distributions weighted by the proportion of time spent in the different states.
tIndex of time step to use for covariates (default: 1).
Returns
A ggplot object
Method formulation()
Print model formulation
Usage
Observation$formulation()
Method print()
Print Observation object Check constructor arguments
Usage
Observation$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
Observation$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
## ------------------------------------------------
## Method `Observation$new`
## ------------------------------------------------
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model "energy" with normal distributions
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0)
# Model "energy" with gamma distributions
obs <- Observation$new(data = energy,
dists = list(Price = "gamma2"),
par = par0)
# Model with non-linear effect of EurDol on mean price
f <- list(Price = list(mean = ~ s(EurDol, k = 5, bs = "cs")))
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
formula = f)
## ------------------------------------------------
## Method `Observation$par`
## ------------------------------------------------
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model with linear effect of EurDol on mean price
f <- list(Price = list(mean = ~ EurDol))
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
n_states = 2,
formula = f)
# Set slope coefficients
obs$update_coeff_fe(coeff_fe = c(3, 2, 6, -2, log(2), log(2)))
# Observation parameter values for given data rows
obs$par(t = c(1, 10, 20))
## ------------------------------------------------
## Method `Observation$suggest_initial`
## ------------------------------------------------
# Load data set from MSwM package
data(energy, package = "MSwM")
# Initial observation parameters
par0 <- list(Price = list(mean = c(3, 6), sd = c(2, 2)))
# Model "energy" with normal distributions
obs <- Observation$new(data = energy,
dists = list(Price = "norm"),
par = par0,
n_states = 2)
# Print observation parameters
obs$par()
# Suggest initial parameters
par0_new <- obs$suggest_initial()
par0_new
# Update model parameters to suggested
obs$update_par(par = par0_new)
obs$par()
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