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()`

.

## Slots

`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 Brennan et al. (2016) . 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.