modelAverageEstimates: Get the model averaged estimate for each variable from an...
In whoppitt/NBDA: A Package For Implementing Network-Based Diffusion Analysis

Description Usage Arguments Details Value See Also

Calculates the model averaged estimate for each variable fitted in a set of models fitted using oadaAICtable or codetadaAICtable.

1
2
3

modelAverageEstimates(nbdaAICtable, typeFilter = NULL,
  netFilter = NULL, baselineFilter = NULL, includeAsocial = TRUE,
  averageType = "mean")

`nbdaAICtable`	an object of class `oadaAICtable` or `tadaAICtable`.
`typeFilter`	an optional string allowing the user to get the support for variables within a specified type of models. e.g. `typeFilter="additive"` gets the variable support in the subset of additive models. See `typeSupport` for an explanation of the different model types.
`netFilter`	an optional string allowing the user to get the support for variables from models with a specific combination of network effects, e.g. `netFilter = "0:1:0"` gets the variable support in the subset of models containing only network 2. See `networksSupport` for an explanation of network combinations.
`baselineFilter`	an optional string allowing the user to get the support for variables within the subset of models with a specific baseline function, e.g. `typeFilter="gamma"` gets the variable support in the subset of models with a gamma baseline rate function. e.g. `typeFilter="additive"` gets the variable support in the subset of additive models.
`includeAsocial`	logical indicating whether asocial models should be included.
`averageType`	string indicating whether a "mean" or "median" should be calculated.

By default the model averaged estimate is calculated as the weighted mean of maximum likelihood estimates in each model, weighted by Akaike weights. This can be changed to a weighted median using the averageType argument. This could be useful in cases where a few models (potentially of low weight) have an essentially infinite estimate for a model parameter, e.g. this can occur for s parameters in an OADA when the diffusion follows the network closely. In such cases a Akaike weighted median may better reflect the overall findings of the multi-model analysis. Models where a variable does not appear are treated as models in which the variable has a MLE=0.

numeric vector giving the model averaged estimate for each variable.

oadaAICtable, tadaAICtable, typeSupport, typeByNetworksSupport, networksSupport, modelAverageEstimates, unconditionalStdErr.