This R6 class defines and fits a conditional probability model `P(sA[j]|sW,...)`

for a univariate
categorical summary measure `sA[j]`

. This class inherits from `SummariesModel`

class.
Defines the fitting algorithm for a regression model `sA[j] ~ sW + ...`

.
Reconstructs the likelihood `P(sA[j]=sa[j]|sW,...)`

afterwards.
Categorical `sA[j]`

is first redefined into `length(levels)`

bin indicator variables, where
`levels`

is a numeric vector of all unique categories in `sA[j]`

.
The fitting algorithm estimates the binary regressions for hazard for each bin indicator, `Bin_sA[j][i] ~ sW`

,
i.e., the probability that categorical `sA[j]`

falls into bin `i`

, `Bin_sA[j]_i`

,
given that `sA[j]`

does not fall in any prior bins `Bin_sA[j]_1, ..., Bin_sA[j]_{i-1}`

.
The dataset of bin indicators (`BinsA[j]_1,...,BinsA[j]_M`

) is created
inside the passed `data`

or `newdata`

object when defining `length(levels)`

bins for `sA[j]`

.

1 |

An `R6Class`

generator object

`reg`

- .`outvar`

- .`levels`

- .`nbins`

- .

`new(reg, DatNet.sWsA.g0, ...)`

...

`fit(data)`

...

`predict(newdata)`

...

`predictAeqa(newdata)`

...

`cats`

...

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