wiki:DurationEstimate

Draft Specification for Duration Estimation (DE)

Compiled by Kirk Li Skye Bender-deMoll, Martina Morris

v0.1

Definition: When observation/sampling period is shorter than overall temporal network, especially when most edges duration are relatively long compared with the observation period, it is possible that some edges start and/or end time are not observed/measured. Hence network. collapse() or edges.activate() may not produce the unbiased duration summary estimates, e.g.: mean, median, quantiles. We will need to rely on survival analysis techniques.

General Requirements:

  1. DE must be compatible with the existing network/networkDynamic class specification.
  1. All operations involving DE must be implementable within the existing network/networkDynamic class API.
  1. DE must allow for the same time/state semantics as other elements of the networkDynamic extended class and survival class on CRAN.
    1. Implement onset,terminus, at, and length alternatives for specifying spells and queires
    2. Avoid changing the estimated nD.
    3. Implement right censoring,(left censoring,unlikely),left truncation, (right truncation,unlikely) on duration.
    4. Be consistence and make distinctions on duration time and calendar time when using the terms in part c.
  1. DE methods should allow estimating the following edges: a with complete observation.
    1. with only start time unknown.
    2. with only end time unknown.
    3. with both start time and end time unknown.
  1. DE should be applicable to both nodes and edges, with certain attribute values.
    1. Implement edge.attributes, vertex.attributes. Be able to DE on subset of edges and nodes.
    2. Be able to consider the change of node values and edge values during observation period.

  1. DE should produce proper survival plots.
    1. Survival analysis should express the effect of right censoring.
    2. If we assume the true starting time are available, survival plots should adjust for the effect of left truncation.
    3. Implement most standard survival package for generating the plots, rather then make our own unless absolutely necessary.
    4. Giving options on what survival plot to produce (KM plot, cumulative hazard plot, hazard plot, etc.)
    5. Possibly produce the left time table.

Acceptable Limitations (?):

How quickly does this need to compute? (is it something that will be called over-and-over inside a simulation, or just a summary function to be called after a model is complete? the latter.

duration.edges.summary

duration.edges.summary(nD, obs.start.time, obs.end.time, edge.value, selection.criterion=c("only non-censored", "only right censored at start", "only right censored at end", "right censored at start and end"), is.truncation.time=T)

Parameters:

nD
networkDynamic object
obs.start.time
onset of observation window?
obs.end.timne
terminus of observation window?
edge.value
name of edge attribute?
selection.criterion
what do each of these do?
is.truncation.time
?

Behavior Sketch:

duration.edges.summary() should return:

  1. 5 estimated summary statistics for duration estimates and boxplot with input/known mean duration. (how will these be returned, as a list with named elements? Is there an option to disable plotting if desired? Return a list, has option to suppress the plot)
  2. summary statistics on the number/percentage of each censoring scenario. (how will these be returned?)

duration.vertices.summary

similar to duration.edges.summary

network.duration.survival.plot

network.duration.survival.plot(nD, obs.start.time, obs.end.time, edge.value, selection.criterion=c("only non-censored", "only right censored at start", "only right censored at end", "right censored at start and end"), is.truncation.time=T, ploting parameter)

Parameters:

nD
networkDynamic object
obs.start.time
onset of observation window?
obs.end.timne
terminus of observation window?
edge.value
name of edge attribute?
selection.criterion
what do each of these do?
is.truncation.time
?
plotting.paramter
what is this?

Behavior Sketch:

network.duration.survival.plot() should return:

  1. Conduct survival fitting on durations , with or without left truncation adjustment.
  2. A set of survival plots.

Questions

If it is covering vertex and edge attributes, should it look at network-level TEA attributes also? Yes.

does network.duration.survival.plot also include vertex durations, and or attribute durations? It is possible to include.

Last modified 4 years ago Last modified on 03/31/13 22:56:11