The class estimate.mpin is the blueprint of S4 objects
that store the results of the estimation of the MPIN model, using the
function mpin_ml().
# S4 method for estimate.mpin
show(object)an object of class estimate.mpin
success(logical) returns the value TRUE when the
estimation has succeeded, FALSE otherwise.
errorMessage(character) returns an error message if the estimation
of the MPIN model 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 used, and is
equal to 'Maximum Likelihood Estimation'.
layers(numeric) returns the number of layers detected in the trading
data, or provided by the user.
detection(logical) returns a reference to the layer-detection
algorithm used ("E", "EG", "ECM"), if any algorithm is used. If the
number of layers is provided by the user, detection takes the value "USER".
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'
estimated 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 maximum likelihood estimation of the MPIN model.
initialsets(dataframe) returns the initial parameter sets used
in the maximum likelihood estimation of the MPIN model.
details(dataframe) returns a dataframe containing the estimated
parameters of the MLE method for each initial parameter set.
runningtime(numeric) returns the running time of the estimation of
the MPIN model in seconds.