Description Objects from the Class Slots Extends Methods Note See Also Examples

This class encapsulates the output from the maximum likelihood estimation of a Poisson process where the intensity is modeled as a linear function of covariates.

Objects can be created by calls of the form `new("mlePP", ...)`

, but most often as the
result of a call to `fitPP.fun`

.

`call`

:Object of class

`"language"`

. The call to`fitPP.fun`

.`coef`

:Object of class

`"numeric"`

. The estimated coefficientes of the model.`fullcoef`

:Object of class

`"numeric"`

. The full coefficient vector, including the fixed parameters of the model. It has an attribute, called 'TypeCoeff' which shows the names of the fixed parameters.`vcov`

:Object of class

`"matrix"`

. Approximate variance-covariance matrix of the estimated coefficients. It has an attribute, called 'CalMethod' which shows the method used to calcualte the inverse of the information matrix: 'Solve function', 'Cholesky', 'Not possible' or 'Not required' if`modCI=FALSE`

.`min`

:Object of class

`"numeric"`

. Minimum value of objective function, that is the negative of the loglikelihood function.`details`

:Object of class

`"list"`

. The output returned from`optim`

. If`nlminb`

is used to minimize the function, it is NULL.`minuslogl`

:Object of class

`"function"`

. The negative of the loglikelihood function.`nobs`

:Object of class

`"integer"`

. The number of observations.`method`

:Object of class

`"character"`

. It is a bit different from the slot in the extended class`mle`

: here, it is the input argument`minfun`

of`fitPP.fun`

instead of the method used in`optim`

(this information already appears in`details`

).`detailsb`

:Object of class

`"list"`

.The output returned from`nlminb`

. If`optim`

is used to minimize the function, it is NULL.`npar`

:Object of class

`"integer"`

. Number of estimated parameters.`inddat`

:Object of class

`"numeric"`

. Input argument of`fitPP.fun`

.`lambdafit`

:Object of class

`"numeric"`

. Vector of the fitted intensity*\hat λ(t)*.`LIlambda`

:Object of class

`"numeric"`

. Vector of lower limits of the CI.`UIlambda`

:Object of class

`"numeric"`

. Vector of upper limits of the CI.`convergence`

:Object of class

`"integer"`

. A code of convergence. 0 indicates successful convergence.`posE`

:Object of class

`"numeric"`

. Input argument of`fitPP.fun`

.`covariates`

:Object of class

`"matrix"`

. Input argument of`fitPP.fun`

.`tit`

:Object of class

`"character"`

. Input argument of`fitPP.fun`

.`tind`

:Object of class

`"logical"`

. Input argument of`fitPP.fun`

.`t`

:Object of class

`"numeric"`

. Input argument of`fitPP.fun`

.

Class `"mle"`

, directly.

Most of the S4 methods in stats4 for the S4-class `mle`

can be used. Also a `mle`

method
for the generic function `extractAIC`

and a version of the `profile`

`mle`

method adapted to the `mlePP`

objects are available:

- coef
`signature(object = "mle")`

- logLik
`signature(object = "mle")`

- nobs
`signature(object = "mle")`

- show
`signature(object = "mle")`

- summary
`signature(object = "mle")`

- update
`signature(object = "mle")`

- vcov
`signature(object = "mle")`

- confint
`signature(object = "mle")`

- extractAIC
`signature(object = "mle")`

- profile
`signature(fitted = "mlePP")`

Some other generic functions related to fitted models, such as `AIC`

or `BIC`

, can also
be applied to `mlePP`

objects.

Let us remind that, as in all the S4-classes, the symbol @ must be used instead of $ to name the slots: mlePP@covariates, mlepp@lambdafit, etc.

1 | ```
showClass("mlePP")
``` |

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