nbdaData: Create an nbdaData object
In whoppitt/NBDA: A Package For Implementing Network-Based Diffusion Analysis

Description Usage Arguments Details Value See Also

nbdaData creates an object of class nbdaData required for conducting a network-based diffusion analysis (NBDA) using oadaFit or fitting a continuous TADA using tadaFit.

nbdaData(label, idname = NULL, assMatrix, asoc = NULL,
  asoc_ilv = "ILVabsent", int_ilv = "ILVabsent",
  multi_ilv = "ILVabsent", random_effects = "REabsent", orderAcq,
  timeAcq = NA, endTime = max(timeAcq) + 1, ties = NULL,
  trueTies = list(NULL), updateTimes = NULL, demons = NULL,
  presenceMatrix = NULL, assMatrixIndex = rep(1, length(orderAcq)),
  weights = rep(1, dim(assMatrix)[1]), asocialTreatment = "constant",
  offsetCorrection = NULL)

`label`	a string giving a name for the diffusion
`idname`	an optional vector giving an id for each individual in the diffusion. Order to match that used in assMatrix.
`assMatrix`	a three or four dimensional array specifying the association matrices or social network(s) to be included in the analysis. If assMatrix has three dimensions the networks are assumed to be static. The first dimension is the row of the social network(s) and the second dimension is the column. Connections are taken to be from the column to the row, such that the entry in row i and column j gives the connection from j to i. The third dimension contains the different networks to be included in the analysis, i.e. assMatrix[,,1] gives the first network, assMatrix[,,2] the second and so on. If a fourth dimension is included the network is taken to vary over time, with assMatrix[,,k,1] giving the values for network k in time period 1, assMatrix[,,k,2] giving the values for network k in time period 2 and so on (see assMatrixIndex below).
`asoc`	for backwards compatibility only, now replaced with asoc_ilv below.
`asoc_ilv`	an optional character vector giving the names of individual-level variables (ILVs) having an effect on the rate of asocial learning. Each entry in asoc_ilv must refer to an object containing the data for that variable. If asocialTreatment= "constant", each variable must be a single column matrix with rows equal to the number of rows in assMatrix, thus providing a single value for each individual in the diffusion. If asocialTreatment= "timevarying" then each variable must be a matrix with columns equal to the number of acquisition event, and rows equal to the number of rows in assMatrix, thus providing a value for each individual in the diffusion at the time of each acquisition event.
`int_ilv`	a optional character vector giving the names of individual-level variables (ILVs) having an (interactive) effect on the rate of social learning. These are specified as for asoc_ilv.
`multi_ilv`	a optional character vector giving the names of individual-level variables (ILVs) having the same effect on both asocial and social learning (referred to as the multiplicative model). These are specified as for asoc_ilv.
`random_effects`	a optional character vector giving the names of random effects. Each variable must be a single column matrix with rows equal to the number of rows in assMatrix, thus providing a single level for each individual in the diffusion. These only have an effect when using `oadaFit`, and are assumed to operate equally on social and asocial learning. If more complex effects are required, or random effects are required for a TADA then a Bayesian approach is recommended (not implemented in the NBDA package).
`orderAcq`	a numerical vector giving the order in which individuals acquired the target behaviour, with numbers referring to rows of assMatrix.
`timeAcq`	a numerical vector giving the time at which each individual acquired the target behaviour, given in the order matching orderAcq. This is necessary for conducting a TADA with `tadaFit` but not if conducting an OADA with `oadaFit`.
`endTime`	numeric giving the time at which the diffusion ended. This is necessary for conducting a TADA with `tadaFit` but not if conducting an OADA with `oadaFit`.
`ties`	numeric binary vector specifying if each acquisition was "tied" with the acquisition before. e.g. c(0,1,1,0,0,1,1) specifies that events 2 and 3 are tied, as are events 6 and 7. Events should be specified as ties if they occurred too close in time for the individuals in question to have plausibly have learned from one another(in contrast to trueTies below).
`trueTies`	a list of numeric vectors specifying which events are trueTies, i.e. where we do not know the order in which the events occurred. e.g. list(c(1,2),c(10,11,12)) specifies that we do not know the real order of events 1 and 2, or of events 10, 11 and 12. This is only applicable to OADA using `oadaFit`. trueTies are accounted for by adding the likelihood across all orders consistent with the trueTies, so can be highly computationally intensive for large trueTies or large numbers of trueTies.
`updateTimes`	non-functioning argument- can be ignored.
`demons`	an optional binary numeric vector specifying which individuals are trained demonstrators or had otherwise already acquired the target behaviour prior to the start of the diffusion. Length should match the number of rows of assMatrix. e.g. c(0,0,1,0,0,0,1) specifies that individuals 3 and 7 are trained demonstrators.
`presenceMatrix`	an optional binary matrix specifying who was present in the diffusion for each event- set to 1s by default. Number of rows to match the number of individuals, and the number of columns to match the number of events (length of orderAcq). 1 denotes that an individual was present in the diffusion for a given event, 0 denotes that an individual was absent, and so could neither learn nor transmit the behaviour to others for that event. Set to 1s by default.
`assMatrixIndex`	a numeric vector necessary if time-varying networks are used, i.e. if assMatrix is a four dimensional array. This specifies which time period (4th dimension of assMatrix) is applicable to each event. e.g. assMatrixIndex=c(1,1,1,2,2,2,3,3,3) specifies that assMatrix[,,,1] gives the networks for events 1-3, assMatrix[,,,2] gives the networks for events 4-6 and assMatrix[,,,3] gives the networks for events 7-9.
`weights`	an optional numeric vector giving the transmission weights for each individual, length to match the number of rows in assMatrix. It is assumed that the rate at which information is transmitted FROM individual j is multiplied by entry j in the weights vector.
`asocialTreatment`	a string- "constant" if ILVs are assumed to be constant over time and "timevarying" if they are assumed to be different for each acquistision event. See asoc_ilv, int_ilv and multi_ilv above.
`offsetCorrection`	a four column matrix with rows equal to the number of individuals, specifying the offset to be added to the social learning component and the linear predictors for the effect of ILVs on asocial learning, social learning, or on both (multiplicative effect). This is used by other functions calling the `nbdaData` function and it is not anticipated that users will need to use this argument directly.

An nbdaData object is required to contain the data for each diffusion to be used in the network based diffusion analysis when using the order of acquisition diffusion analysis (OADA) method (oadaFit function) or continuous time of acquistion diffusion analysis (cTADA) method (tadaFit function). When multiple diffusions are being modelled a list of nbdaData objects is provided to the oadaFit or tadaFit function.

Returns an object of class nbdaData, which can be used to conduct an NBDA using oadaFit or tadaFit functions.

To create a data object for discrete TADA models use dTADAData.

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