Prints basic information and statistics from a netdx
object.
Usage
# S3 method for class 'netdx'
print(x, digits = 3, ...)Details
Given a netdx object, print.netdx prints the diagnostic method
(static/dynamic), number of simulations, and (if dynamic) the number of time
steps per simulation used in generating the netdx object, as well as
printing the formation statistics table and (if present) the duration and
dissolution statistics tables. The statistics tables are interpreted as
follows.
Each row has the name of a particular network statistic. In the formation
table, these correspond to actual network statistics in the obvious way.
In the duration and dissolution tables, these correspond to dissolution
model dyad types: in a homogeneous dissolution model, all dyads are of the
edges type; in a heterogeneous dissolution model, a dyad with a
nonzero nodematch or nodemix change statistic in the
dissolution model has type equal to that statistic, and has type equal to
edges otherwise. The statistics of interest for the duration and
dissolution tables are, respectively, the mean age of extant edges and the
edge dissolution rate, broken down by dissolution model dyad type. (The
current convention is to treat the mean age and dissolution rate for a
particular dissolution dyad type as 0 on time steps with no edges of that
type; this behavior may be changed in the future.)
The columns are named Target, Sim Mean, Pct Diff,
Sim SE, Z Score, SD(Sim Means), and
SD(Statistic). The Sim Mean column refers to the mean
statistic value, across all time steps in all simulations in the dynamic
case, and across all sampled networks in all simulations in the static case.
The Sim SE column refers to the standard error in the mean, estimated
using coda::effectiveSize. The Target
column indicates the target value (if present) for the statistic, and the
Pct Diff column gives (Sim Mean - Target)/Target when
Target is present. The Z Score column gives
(Sim Mean - Target)/(Sim SE). The SD(Sim Means) column gives
the empirical standard deviation across simulations of the mean statistic
value within simulation, and SD(Statistic) gives the empirical
standard deviation of the statistic value across all the simulated data.