nbdaPropSolveByST.byevent: Estimate the probability that each event occured by social...
In whoppitt/NBDA: A Package For Implementing Network-Based Diffusion Analysis

Description Usage Arguments Details Value See Also

Generates a table giving the predicted probability that each event occured by social transmission via each network.

1 2	nbdaPropSolveByST.byevent(par = NULL, nbdadata = NULL, model = NULL, type = "social", retainInt = NULL)

`par`	optional numeric giving the parameter values. Not necessary if `model` is specified.
`nbdadata`	object of class `nbdaData`, list of objects of class `nbdaData`, object of class `dTADAData` or list of objects of class `dTADAData`. Not necessary if `model` is specified.
`model`	object of class `oadaFit` or `tadaFit`.
`type`	string determining whether the estimates are for an "asocial" or "social" model. This is taken from `model` if provided.
`retainInt`	logical, can be used to force the model to retain int_ilvs in an asocial model. This is used internally by other functions when there is an offset on the s parameters, but can usually be safely ignored by the user.

The user can provide the name of a model- in which case the MLEs of that model are input as parameter values. Instead the user can input their own parameter values in which case nbdadata must also be provided. The function works for multiple diffusions. A column is also provided for P(S offset), which gives the probability that transmission was via network effects that have been allocated to the offset fo s parameters, see constrainedNBDAdata.

A dataframe giving the predicted probability that each event occured by social transmission via each network.

nbdaPropSolveByST

whoppitt/NBDA documentation built on April 25, 2021, 7:55 a.m.