`LF_base.Rd`

A Likelihood factor models a conditional density function.
The conditioning set is defined as all parent nodes (defined in `tmle3_Task`

). In the case of a continuous
outcome variable, where a full density isn't needed, this can also model a conditional mean. This is the base class, which
is intended to be abstract. See below for a list of possible likelihood factor classes.

`LF_base`

`R6Class`

object.

`LF_base`

object

`define_lf(LF_base, name, ..., type = "density")`

`name`

character, the name of the factor. Should match a node name in the nodes specified by

`tmle3_Task$npsem`

`...`

Not currently used.

`type`

character, either "density", for conditional density or, "mean" for conditional mean

`get_density(tmle_task)`

Get conditional density values for for the observations in

`tmle_task`

.`tmle_task`

:`tmle3_Task`

to get likelihood values for

`get_mean(tmle_task)`

Get conditional mean values for for the observations in

`tmle_task`

.`tmle_task`

:`tmle3_Task`

to get likelihood values for

`name`

character, the name of the factor. Should match a node name in the nodes specified by

`tmle3_Task$npsem`

`type`

character, either "density", for conditional density or, "mean" for conditional mean

`variable_type`

`variable_type`

object, specifying the data type of the outcome variable. Only available after Likelihood training.`values`

Possible values of the outcome variable, retrivied from the

`variable_type`

object. Only available after Likelihood training.

Other Likelihood objects: `CF_Likelihood`

,
`LF_emp`

, `LF_fit`

,
`LF_known`

, `LF_static`

,
`Likelihood`

,
`Targeted_Likelihood`

,
`define_lf`