The class estimate.mpin.ecm is the blueprint of
S4 objects that store the results of the estimation of the MPIN
model using the Expectation-Conditional Maximization method, as
implemented in the function mpin_ecm().
# S4 method for estimate.mpin.ecm
show(object)selectModel(object, criterion)
# S4 method for estimate.mpin.ecm
selectModel(object, criterion)
getSummary(object)
# S4 method for estimate.mpin.ecm
getSummary(object)
an object of class estimate.mpin.ecm.
a character string specifying the model selection criterion.
criterion should take one of these values {"BIC", "AIC", "AWE"}.
They stand for Bayesian Information Criterion, Akaike Information Criterion,
and Approximate Weight of Evidence, respectively.
selectModel(estimate.mpin.ecm): returns the optimal model among
the estimated models, i.e., the model having the lowest information
criterion, provided by the user.
getSummary(estimate.mpin.ecm): returns a summary of
the estimation of the MPIN model using the ECM algorithm for different
values of the argument layers. For each estimation, the number of layers,
the MPIN value, the log-likelihood value, as well as the values of the
different information criteria, namely AIC, BIC and AWE are displayed.
success(logical) returns the value TRUE when the
estimation has succeeded, FALSE otherwise.
errorMessage(character) returns an error message if the MPIN
estimation has failed, and is empty otherwise.
convergent.sets(numeric) returns the number of initial parameter
sets at which the likelihood maximization converged.
method(character) returns the method of estimation, and is equal
to 'Expectation-Conditional Maximization Algorithm'.
layers(numeric) returns the number of layers estimated by the
Expectation-Conditional Maximization algorithm, or provided by the user.
optimal(logical) returns whether the number of layers used for
the estimation is provided by the user (optimal=FALSE), or determined
by the ECM algorithm (optimal=TRUE).
parameters(list) returns the list of the maximum likelihood
estimates (\(\alpha\), \(\delta\), \(\mu\), \(\epsilon\)b, \(\epsilon\)s), where
\(\alpha\), \(\delta\), and \(\mu\) are numeric vectors of
length layers.
aggregates(numeric) returns an aggregation of information layers'
parameters alongside with \(\epsilon\)b and \(\epsilon\)s. The aggregated parameters are
calculated as follows:
\(\alpha_{agg} = \sum \alpha_j\)\(\alpha*= \sum
\alpha\)j \(\delta_{agg} = \sum \alpha_j \times \delta_j\)
\(\delta*= \sum \alpha\)j\(\delta\)j,
and \(\mu_{agg} = \sum \alpha_j \times \mu_j\)\(\mu*= \sum
\alpha\)j\(\mu\)j.
likelihood(numeric) returns the value of the (log-)likelihood
function evaluated at the optimal set of parameters.
mpinJ(numeric) returns the values of the multilayer probability of
informed trading per layer, calculated using the layer-specific estimated
parameters.
mpin(numeric) returns the global value of the multilayer probability
of informed trading. It is the sum of the multilayer probabilities of
informed trading per layer stored in the slot mpinJ.
mpin.goodbad(list) returns a list containing a decomposition of
MPIN into good-news, and bad-news MPIN components. The decomposition
has been suggested for PIN measure in
Brennan2016;textualPINstimation. The list has four elements:
mpinG, and mpinB are the global good-news, and bad-news components of
MPIN, while mpinGj, and mpinBj are two vectors containing the
good-news (bad-news) components of MPIN computed per layer.
dataset(dataframe) returns the dataset of buys and sells used
in the ECM estimation of the MPIN model.
initialsets(dataframe) returns the initial parameter sets used
in the ECM estimation of the MPIN model.
details(dataframe) returns a dataframe containing the estimated
parameters of the ECM method for each initial parameter set.
models(list) returns the list of estimate.mpin.ecm objects
storing the results of estimation using the function mpin_ecm() for
different values of the argument layers. It returns NULL when the
argument layers of the function mpin_ecm() take a specific value.
AIC(numeric) returns the value of the Akaike Information Criterion
(AIC).
BIC(numeric) returns the value of the Bayesian Information Criterion
(BIC).
AWE(numeric) returns the value of the Approximate Weight of
Evidence.
criterion(character) returns the model selection criterion used to
find the optimal estimate for the MPIN model. It takes one of these values
'BIC', 'AIC', 'AWE'; which stand for Bayesian Information Criterion,
Akaike Information Criterion, and Approximate Weight of Evidence,
respectively.
hyperparams(list) returns the hyperparameters of the ECM
algorithm, which are minalpha, maxeval, tolerance, and maxlayers.
Check the details section of mpin_ecm() to know more about these
parameters.
runningtime(numeric) returns the running time of the estimation
in seconds.