Creates an object of class "sienaGroup" from a list of Siena data objects.
sienaGroupCreate(objlist, singleOK = FALSE, getDocumentation=FALSE)List of objects of class "siena".
Boolean: is it OK to only have one object?
Flag to allow documentation of internal functions, not for use by users.
An object of class "sienaGroup"; this is a list containing the input objects, with attributes:
names of the dependent variables in each set
vector of booleans, one for each dependent variable. TRUE if all occurrences of the network are symmetric.
vector of booleans, indicating whether structurally fixed values occur in this network
vector of booleans, indicating whether changes are all upwards in all the occurrences of this network
similar to previous, but for downward changes
vector of booleans, indicating whether changes are all upwards in any of the occurrences of this network
similar to previous, but for downward changes
vector of network types of the dependent variables
Total number of periods to process
Sequence of numbers of periods which are not skipped in multigroup processing
list of names of the node sets corresponding to the dependent variables
names of the constant covariates, if any
names of the changing covariates, if any
names of the constant dyadic covariates, if any
names of the changing dyadic covariates, if any
list of the names of the node sets corresponding to the constant covariates
list of the names of the node sets corresponding to the changing covariates
list of the names of the node sets corresponding to the constant dyadic covariates
list of the names of the node sets corresponding to the changing dyadic covariates
boolean: any composition change at all?
named vector of composition change options for the node sets
Either from the input objects or "Data1", "Data2" etc
"sienaGroup" inheriting from "siena"
vector of means for balance calculations
vector of difference between maximum and minimum values for behavior variables, NA for other dependent variables
matrix of maximum and minimum values for behavior variables, NA for other dependent variables
vector of similarity means for behavior variables, NA for other dependent variables
vector of booleans indicating positive variance for behavior variables. NA for other dependent variables
vector of booleans indicating whether the behavior variables take more than 2 distinct values
vector of booleans indicating positive variance for constant covariates
vector of booleans indicating whether the constant covariates take more than 2 distinct values
vector of difference between maximum and minimum values for constant covariates
matrix of maximum and minimum values for constant covariates
vector of similarity means for constant covariates
vector of means for constant covariates
vector of difference between maximum and minimum values for changing covariates
vector of similarity means for changing covariates
vector of booleans indicating whether the changing covariates take more than 2 distinct values
vector of booleans indicating positive variance for changing covariates
vector of means for changing covariates
vector of means for constant dyadic covariates
vector of ranges for constant dyadic covariates
matrix of maximum and minimum values for constant dyadic covariates
vector of ranges for changing dyadic covariates
vector of means for changing dyadic covariates
vector of booleans, one for each dependent variable, indicating the presence of any missing values
matrix of maximum and minimum values for dependent networks, NA for behavior variables
This function creates a Siena group object from several Siena data
objects ('groups'), all of which use networks, covariates and actor sets
with the same names. The variables must correspond exactly
between all data objects; the numbers of waves may differ.
It can be used as data input to siena07 for the
multigroup option.
Also used internally for convenience with a single Siena data object.
Each covariate should either be centered in all groups, or non-centered in all groups. For centered actor covariates, the original values are used, and the overall mean of all non-missing observations is subtracted. Note that this implies that group-dependent variables that are constant for all actors in each group, can be used as centered actor covariates.
For combining two-wave with more-wave groups in one group object, covariates that are changing covariates for the more-wave groups have to be specified as changing covariates also for the two-wave groups. This can be done by specifying them with values for the two waves; for actor covariates this will be by using an \(n \times 2\) matrix, for dyadic covariates an \(n \times n \times 2\) array (or \(n \times m \times 2\) for the two-mode case). The values for the second wave should be identical to those for the first wave (they will be used only for centering operations).
For later use in siena07,
it will often (but not always...) be helpful when creating the
Siena data objects in objlist
to use allowOnly=FALSE in the call of
sienaDependent; see the help page for this function.
If there are multiple dependent networks, it may be necessary to run
sienaDataConstraint before sienaGroupCreate
to ensure that these constraints are equal for all groups.
See the Section on Multi-group Siena analysis in the manual available from http://www.stats.ox.ac.uk/~snijders/siena/ .
# NOT RUN {
Group1 <- sienaDependent(array(c(N3401, HN3401), dim=c(45, 45, 2)))
Group3 <- sienaDependent(array(c(N3403, HN3403), dim=c(37, 37, 2)))
Group4 <- sienaDependent(array(c(N3404, HN3404), dim=c(33, 33, 2)))
Group6 <- sienaDependent(array(c(N3406, HN3406), dim=c(36, 36, 2)))
dataset.1 <- sienaDataCreate(Friends = Group1)
dataset.3 <- sienaDataCreate(Friends = Group3)
dataset.4 <- sienaDataCreate(Friends = Group4)
dataset.6 <- sienaDataCreate(Friends = Group6)
FourGroups <- sienaGroupCreate(list(dataset.1, dataset.3, dataset.4, dataset.6))
# }
Run the code above in your browser using DataLab