ContinSummaryModel: R6 class for fitting and predicting joint probability for a univariate continuous summary measure sA[j]
Description
This R6 class defines and fits a conditional probability model P(sA[j]|sW,...)
for a univariate
continuous 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.
Continuous sA[j]
is discretized using either of the 3 interval cutoff methods,
defined via RegressionClass
object reg
passed to this class constructor.
The fitting algorithm estimates the binary regressions for hazard Bin_sA[j][i] ~ sW
,
i.e., the probability that continuous sA[j]
falls into bin i
, Bin_sA[j]_i
,
given that sA[j]
does not belong to any prior bins Bin_sA[j]_1, ..., Bin_sA[j]_{i-1}
.
The dataset of discretized summary measures (BinsA[j]_1,...,BinsA[j]_M
) is created
inside the passed data
or newdata
object while discretizing sA[j]
into M
bins.
Methods
new(reg, DatNet.sWsA.g0, DatNet.sWsA.gstar, ...)
- ...
fit(data)
- ...
predict(newdata)
- ...
predictAeqa(newdata)
- ...
Details
reg
- .
outvar
- .
intrvls
- .
intrvls.width
- .
bin_weights
- .