fims_distributions::LogNormalLPDF< Type > Struct Template Reference

Implements the LogNormalLPDF distribution functor used by FIMS to evaluate observation-level and total log-likelihood contributions under a lognormal error model for data, priors, and random effects. More...

#include <lognormal_lpdf.hpp>

Inheritance diagram for fims_distributions::LogNormalLPDF< Type >:

Public Member Functions
	LogNormalLPDF ()
	Constructor.
virtual	~LogNormalLPDF ()
	Destructor.
virtual const Type	evaluate ()
	Evaluates the lognormal log probability density function.
Public Member Functions inherited from fims_distributions::DensityComponentBase< Type >
Type &	get_observed (size_t i)
	Retrieve one observed value based on input_type.
Type &	get_observed (size_t i, size_t j)
	Retrieve one observed matrix-like value based on input_type.
Type &	get_expected (size_t i)
	Retrieve one expected value based on input_type and use_mean.
size_t	get_n_x ()
	Get length of the active observed input vector.
size_t	get_n_expected ()
	Get length of the active expected input vector.
	DensityComponentBase ()
	Constructor, which initializes default prior pointer state and ID.
Public Member Functions inherited from fims_model_object::FIMSObject< Type >
uint32_t	GetId () const
	Getter that returns the unique id for parameters in the model.
bool	CheckDimensions (size_t actual, size_t expected)
	Check the dimensions of an object.

Public Attributes
fims::Vector< Type >	log_sd
	Natural log of the standard deviation of the distribution on the log scale. The argument can be a vector or scalar, where the latter is referenced for each instance through the use of get_force_scalar().
Public Attributes inherited from fims_distributions::DensityComponentBase< Type >
std::string	input_type
	Classification of the input pathway for this distribution object. Options used by accessor methods are, "prior", "random_effects", and "data".
std::shared_ptr< fims_data_object::DataObject< Type > >	data_observed_values
	Observed data.
fims::Vector< Type >	expected_values
	Expected value vector for prior-based pathways.
fims::Vector< Type > *	re = NULL
	Pointer to random effects vector.
fims::Vector< Type > *	re_expected_values = NULL
	Expected value vector for random-effects pathways.
fims::Vector< Type > *	data_expected_values = NULL
	Expected value vector for data pathways.
std::vector< fims::Vector< Type > * >	priors
	Vector of pointers where each entry points to a prior parameter.
fims::Vector< Type >	observed_values
	Input value of distribution function for priors or random effects.
fims::Vector< Type >	expected_mean
	The expected mean of the distribution; overrides expected values.
std::string	use_mean = fims::to_string("no")
	If "yes", expected_mean is used instead of expected_values. The default is "no" leading to the use of expected_values.
Type	lpdf
	Total log probability density contribution of the distribution.
int	observed_data_id_m = -999
	ID of observed data component.
fims::Vector< Type >	lpdf_vec
	Vector storing observation-level log-likelihood contributions.
bool	osa_flag = false
	Boolean; if true, one-step-ahead (OSA) residuals are calculated.
bool	simulate_flag = false
	Boolean; if true, data are simulated from the distribution.
std::vector< uint32_t >	key
	Unique ID for variable map that points to a fims::Vector.
Public Attributes inherited from fims_model_object::FIMSObject< Type >
uint32_t	id
std::vector< Type * >	parameters
std::vector< Type * >	random_effects_parameters
std::vector< Type * >	fixed_effects_parameters

Additional Inherited Members
Static Public Attributes inherited from fims_distributions::DensityComponentBase< Type >
static uint32_t	id_g = 0
	Global unique identifier for distribution modules.

Detailed Description

template<typename Type>
struct fims_distributions::LogNormalLPDF< Type >

Implements the LogNormalLPDF distribution functor used by FIMS to evaluate observation-level and total log-likelihood contributions under a lognormal error model for data, priors, and random effects.

This implementation relies on TMB's R-style dnorm() utility for normal log-density calculations on log-transformed values. Specifically, when evaluating the lognormal likelihood, observations are transformed with log(x) and passed to dnorm(..., give_log = true) to obtain log-density values. For data inputs, the Jacobian adjustment -log(x) is applied where appropriate to convert from normal density on the log scale to the lognormal density on the original scale.

For data input, values equal to na_value are skipped and contribute zero to the objective. Per-observation contributions are stored in lpdf_vec; the summed total is returned by evaluate() and stored in lpdf.

Member Function Documentation

evaluate()

template<typename Type >

virtual const Type fims_distributions::LogNormalLPDF< Type >::evaluate ( )

inlinevirtual

Evaluates the lognormal log probability density function.

The following equation is the lognormal probability density function, and thus, the log of it is evaluated:

\[ f(x) = \frac{1.0}{ x\sigma\sqrt{2\pi} }\mathrm{exp}\Bigg(-\frac{(\mathrm{ln}(x) - \mu)^{2}}{2\sigma^{2}}\Bigg), \]

where \(\mu\) is the mean of the distribution of \(\mathrm{ln(x)}\) and \(\sigma^2\) is the variance of \(\mathrm{ln}(x)\).

Implements fims_distributions::DensityComponentBase< Type >.

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The documentation for this struct was generated from the following file:

• inst/include/distributions/functors/lognormal_lpdf.hpp