Description Usage Format Details Methods Active Bindings
This R6 class defines and fits a conditional probability model P(A[j]|W,...)
for a univariate
categorical summary measure A[j]
. This class inherits from GenericModel
class.
Defines the fitting algorithm for a regression model A[j] ~ W + ...
.
Reconstructs the likelihood P(A[j]=a[j]|W,...)
afterwards.
Categorical A[j]
is first redefined into length(levels)
bin indicator variables, where
levels
is a numeric vector of all unique categories in A[j]
.
The fitting algorithm estimates the binary regressions for hazard for each bin indicator, Bin_A[j][i] ~ W
,
i.e., the probability that categorical A[j]
falls into bin i
, Bin_A[j]_i
,
given that A[j]
does not fall in any prior bins Bin_A[j]_1, ..., Bin_A[j]_{i-1}
.
The dataset of bin indicators (BinA[j]_1,...,BinA[j]_M
) is created
inside the passed data
or newdata
object when defining length(levels)
bins for A[j]
.
1 |
An R6Class
generator object
reg
- .
outvar
- .
levels
- .
nbins
- .
new(reg, DataStorageClass.g0, ...)
...
fit(data)
...
predict(newdata)
...
predictAeqa(newdata)
...
cats
...
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.