sienaGroupCreate: Function to group together several Siena data objects

In RSiena: Siena - Simulation Investigation for Empirical Network Analysis

Function to group together several Siena data objects

Description

Creates an object of class "sienaGroup" from a list of Siena data objects.

Usage

sienaGroupCreate(objlist, singleOK = FALSE, getDocumentation=FALSE)

Arguments

objlist	List of objects of class siena.
singleOK	Boolean: is it OK to only have one object?
getDocumentation	Flag to allow documentation of internal functions, not for use by users.

Details

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. Centered actor covariates are re-centered at the overall mean. This means that 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.

Value

An object of class sienaGroup; this is a list containing the input objects, with attributes:

netnames	names of the dependent variables in each set
symmetric	vector of booleans, one for each dependent variable. TRUE if all occurrences of the network are symmetric.
structural	vector of booleans, indicating whether structurally fixed values occur in this network
allUpOnly	vector of booleans, indicating whether changes are all upwards in all the occurrences of this network
allDownOnly	similar to previous, but for downward changes
anyUpOnly	vector of booleans, indicating whether changes are all upwards in any of the occurrences of this network
anyDownOnly	similar to previous, but for downward changes
types	vector of network types of the dependent variables
observations	Total number of periods to process
periodNos	Sequence of numbers of periods which are not skipped in multigroup processing
netnodeSets	list of names of the node sets corresponding to the dependent variables
cCovars	names of the constant covariates, if any
vCovars	names of the changing covariates, if any
dycCovars	names of the constant dyadic covariates, if any
dyvCovars	names of the changing dyadic covariates, if any
ccnodeSets	list of the names of the node sets corresponding to the constant covariates
cvnodeSets	list of the names of the node sets corresponding to the changing covariates
dycnodeSets	list of the names of the node sets corresponding to the constant dyadic covariates
dyvcnodeSets	list of the names of the node sets corresponding to the changing dyadic covariates
compositionChange	boolean: any composition change at all?
exooptions	named vector of composition change options for the node sets
names	Either from the input objects or "Data1", "Data2" etc
class	"sienaGroup" inheriting from "siena"
balmean	vector of means for balance calculations
bRange	vector of difference between maximum and minimum values for behavior variables, NA for other dependent variables
behRange	matrix of maximum and minimum values for behavior variables, NA for other dependent variables
bSim	vector of similarity means for behavior variables, NA for other dependent variables
bPoszvar	vector of booleans indicating positive variance for behavior variables. NA for other dependent variables
bMoreThan2	vector of booleans indicating whether the behavior variables take more than 2 distinct values
cCovarPoszvar	vector of booleans indicating positive variance for constant covariates
cCovarMoreThan2	vector of booleans indicating whether the constant covariates take more than 2 distinct values
cCovarRange	vector of difference between maximum and minimum values for constant covariates
cCovarRange2	matrix of maximum and minimum values for constant covariates
cCovarSim	vector of similarity means for constant covariates
cCovarMean	vector of means for constant covariates
vCovarRange	vector of difference between maximum and minimum values for changing covariates
vCovarSim	vector of similarity means for changing covariates
vCovarMoreThan2	vector of booleans indicating whether the changing covariates take more than 2 distinct values
vCovarPoszvar	vector of booleans indicating positive variance for changing covariates
vCovarMean	vector of means for changing covariates
dycCovarMean	vector of means for constant dyadic covariates
dycCovarRange	vector of ranges for constant dyadic covariates
dycCovarRange2	matrix of maximum and minimum values for constant dyadic covariates
dyvCovarRange	vector of ranges for changing dyadic covariates
dyvCovarMean	vector of means for changing dyadic covariates
anyMissing	vector of booleans, one for each dependent variable, indicating the presence of any missing values
netRanges	matrix of maximum and minimum values for dependent networks, NA for behavior variables

Author(s)

Ruth Ripley, Modification by Tom Snijders

References

See the Section on Multi-group Siena analysis in the manual available from https://www.stats.ox.ac.uk/~snijders/siena/.

See Also

sienaDataCreate, sienaDataConstraint

Examples

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)))
# Illustration of the use of group-level variables:
# dum1 is a dummy variable for group 1,
# having constant value 1 in group 1, and constant value 0 in the other groups.
dum1.1  <- coCovar(c(rep(1,45)), warn = FALSE)
dum1.3  <- coCovar(c(rep(0,37)), warn = FALSE)
dum1.4  <- coCovar(c(rep(0,33)), warn = FALSE)
dum1.6  <- coCovar(c(rep(0,36)), warn = FALSE)
# In a similar way, dummies for the other groups can be defined.
dataset.1 <- sienaDataCreate(Friends = Group1, dum1 = dum1.1)
dataset.3 <- sienaDataCreate(Friends = Group3, dum1 = dum1.3)
dataset.4 <- sienaDataCreate(Friends = Group4, dum1 = dum1.4)
dataset.6 <- sienaDataCreate(Friends = Group6, dum1 = dum1.6)
(FourGroups <- sienaGroupCreate(list(dataset.1, dataset.3, dataset.4,
                                     dataset.6)))
class(FourGroups)
# The main effect of the group-level variable is the \code{egoX} effect:
myeff <- getEffects(FourGroups)
(myeff <- includeEffects(myeff, egoX, interaction1 = "dum1"))

RSiena documentation built on Nov. 2, 2023, 5:19 p.m.