sienaFit: Methods for processing sienaFit objects, produced by...

In RSiena: Siena - Simulation Investigation for Empirical Network Analysis

Description

print, summary, and xtable methods for sienaFit objects.

Usage

## S3 method for class 'sienaFit'
print(x, tstat=TRUE, ...)

## S3 method for class 'sienaFit'
summary(object, ...)

## S3 method for class 'summary.sienaFit'
print(x, matrices=TRUE, ...)

## S3 method for class 'sienaFit'
xtable(x, caption = NULL, label = NULL, align = NULL,
                digits = NULL, display = NULL, ...)

siena.table(x, type="tex", file=paste(deparse(substitute(x)), ".", type,sep=""),
            vertLine=TRUE, tstatPrint=FALSE, sig=FALSE, d=3, nfirst=NULL)

Arguments

object	An object of class sienaFit, produced by siena07.
x	An object of class sienaFit, or summary.sienaFit as appropriate.
matrices	Boolean: whether also to print in the summary the covariance matrix of the estimates, the derivative matrix of expected statistics X by parameters, and the covariance matrix of the statistics.
tstat	Boolean: if this is NULL, the t-statistics for convergence will not be added to the report.
type	Type of output to produce; must be either "tex" or "html".
file	Name of the file; defaults to the name of the sienaFit object. "" indicates output to the console.
vertLine	Boolean: add vertical lines separating the columns in siena.table.
tstatPrint	Boolean: add a column of significance t values (parameter estimate/standard error estimate) to siena.table.
sig	Boolean: adds symbols (daggers and asterisks) indicating significance levels for the parameter estimates to siena.table.
d	The number of decimals places used in siena.table.
caption	See documentation for xtable.
label	See documentation for xtable.
align	See documentation for xtable.
digits	See documentation for xtable.
display	See documentation for xtable
nfirst	Only relevant for the RSienaTest package.
...	Add extra parameters for print.xtable here. e.g. type, file.

Value

The function print.sienaFit prints a table containing estimated parameter values, standard errors and (optionally) t-statistics for convergence.

The function summary.sienaFit prints a table containing estimated parameter values, standard errors and t-statistics for convergence together with the covariance matrix of the estimates, the derivative matrix of expected statistics X by parameters, and the covariance matrix of the expected statistics X.

The function xtable.sienaFit creates an object of class xtable.sienaFit which inherits from class xtable and passes an extra arguments to the print.xtable.

The function siena.table outputs a latex or html table of the estimates and standards errors of a sienaFit object. The table will be written to a file in the current directory and has a footnote reporting the maximum of the convergence t-ratios.

See the manual for how to import these tables easily into MS-Word.

Author(s)

Ruth Ripley, Charlotte Greenan, Tom Snijders

References

See http://www.stats.ox.ac.uk/~snijders/siena/

See Also

xtable, print.xtable, siena07

Examples

myalgorithm <- sienaAlgorithmCreate(nsub=2, n3=100, projname=NULL)
mynet1 <- sienaDependent(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)
ans
summary(ans)
## Not run: 
xtable(ans, type="html", file="ans.html")
siena.table(ans, type="html", tstat=TRUE, d=2)

## End(Not run)

Example output

Warning message:
no DISPLAY variable so Tk is not available 

Start phase 0 
theta: -0.56  0.00 

Start phase 1 
Phase 1 Iteration 1 Progress: 0%
Phase 1 Iteration 2 Progress: 0%
Phase 1 Iteration 3 Progress: 0%
Phase 1 Iteration 4 Progress: 1%
Phase 1 Iteration 5 Progress: 1%
Phase 1 Iteration 10 Progress: 1%
Phase 1 Iteration 15 Progress: 2%
Phase 1 Iteration 20 Progress: 3%
Phase 1 Iteration 25 Progress: 3%
Phase 1 Iteration 30 Progress: 4%
Phase 1 Iteration 35 Progress: 5%
Phase 1 Iteration 40 Progress: 6%
Phase 1 Iteration 45 Progress: 6%
Phase 1 Iteration 50 Progress: 7%
theta: -0.728  0.462 

Start phase 2.1
Phase 2 Subphase 1 Iteration 1 Progress: 21%
Phase 2 Subphase 1 Iteration 2 Progress: 21%
theta -0.858  0.885 
ac 0.602 1.307 
Phase 2 Subphase 1 Iteration 3 Progress: 21%
Phase 2 Subphase 1 Iteration 4 Progress: 21%
theta -1.12  1.66 
ac 0.664 1.314 
Phase 2 Subphase 1 Iteration 5 Progress: 21%
Phase 2 Subphase 1 Iteration 6 Progress: 21%
theta -1.25  2.01 
ac 0.667 1.162 
Phase 2 Subphase 1 Iteration 7 Progress: 22%
Phase 2 Subphase 1 Iteration 8 Progress: 22%
theta -1.30  2.17 
ac 0.647 1.152 
Phase 2 Subphase 1 Iteration 9 Progress: 22%
Phase 2 Subphase 1 Iteration 10 Progress: 22%
theta -1.31  2.18 
ac 0.318 1.155 
theta -1.14  1.79 
ac  0.0658 -0.1161 
theta: -1.14  1.79 

Start phase 2.2
Phase 2 Subphase 2 Iteration 1 Progress: 51%
Phase 2 Subphase 2 Iteration 2 Progress: 51%
Phase 2 Subphase 2 Iteration 3 Progress: 52%
Phase 2 Subphase 2 Iteration 4 Progress: 52%
Phase 2 Subphase 2 Iteration 5 Progress: 52%
Phase 2 Subphase 2 Iteration 6 Progress: 52%
Phase 2 Subphase 2 Iteration 7 Progress: 52%
Phase 2 Subphase 2 Iteration 8 Progress: 52%
Phase 2 Subphase 2 Iteration 9 Progress: 52%
Phase 2 Subphase 2 Iteration 10 Progress: 53%
theta -1.12  1.73 
ac -0.023 -0.275 
theta: -1.12  1.73 

Start phase 3 
Estimates, standard errors and convergence t-ratios

                              Estimate   Standard   Convergence 
                                           Error      t-ratio   

Rate parameters: 
  0       Rate parameter       2.9954  ( 0.4779   )             
  1. eval outdegree (density) -1.1172  ( 0.2005   )    0.1580   
  2. eval reciprocity          1.7320  ( 0.4091   )   -0.1804   

Overall maximum convergence ratio:    0.3606 


Total of 427 iteration steps.

Estimates, standard errors and convergence t-ratios

                              Estimate   Standard   Convergence 
                                           Error      t-ratio   

Rate parameters: 
  0       Rate parameter       2.9954  ( 0.4779   )             
  1. eval outdegree (density) -1.1172  ( 0.2005   )    0.1580   
  2. eval reciprocity          1.7320  ( 0.4091   )   -0.1804   

Overall maximum convergence ratio:    0.3606 


Total of 427 iteration steps.

Covariance matrix of estimates (correlations below diagonal)

       0.040       -0.049
      -0.599        0.167

Derivative matrix of expected statistics X by parameters:

      39.897       16.909
      13.646       18.008

Covariance matrix of X (correlations below diagonal):

      41.656       21.598
       0.559       35.832

RSiena documentation built on Sept. 24, 2020, 3 p.m.