simplifiedpimestimation.marginalcoefficients: Simplified fitting of PIMs with only one categorical...
In pimold: Probabilistic Index Models

Simplified fitting of PIMs with only one categorical predictor

simplifiedpimestimation.marginalcoefficients(data, out, group, block)

simplifiedpimestimation.marginalcovariance(data, out, group, block)

simplifiedpimestimation.pairwisecoefficients(data, out, group, block)

simplifiedpimestimation.pairwisecovariance(data, out, group, block)

`data`	Context where `out`, `group` and `block` are to be interpreted
`out`	Column of `data` that holds the outcomes (responses)
`group`	Column of `data` that holds the predicting variable
`block`	Column of `data` that holds the blocking variable. This can be left out if no blocking is present

for simplifiedpimestimation.marginalcoefficients: The coefficient estimates of the marginal model, in order of the levels of the predicting variable

for simplifiedpimestimation.marginalcovariance: The covariance estimates of the marginal model, in order of the levels of the predicting variable

for simplifiedpimestimation.pairwisecoefficients: The coefficient estimates of the pairwise model, in order of the combined levels of the predicting variable (as combn generates them)

for simplifiedpimestimation.pairwisecovariance: The covariance estimates of the pairwise model, in order of the combined levels of the predicting variable (as combn generates them)

These functions are merely provided for comparison. The pim function supports generic ways of estimating these values.

pimold documentation built on May 2, 2019, 5:50 p.m.