Learn R Programming

RSiena (version 1.2-23)

print.sienaEffects: Print methods for Siena effects objects

Description

Print the major columns of the effects object. Or all, with any non atomic columns listed separately.

Usage

# S3 method for sienaEffects
print(x, fileName = NULL, includeOnly=TRUE,
expandDummies = FALSE, includeRandoms = FALSE, dropRates=FALSE, ...)
# S3 method for sienaEffects
summary(object, fileName = NULL,
includeOnly=TRUE, expandDummies = FALSE, ...)
# S3 method for summary.sienaEffects
print(x, fileName = NULL, ...)

Arguments

object

An object of class sienaEffects.

x

An object of class sienaEffects or summary.sienaEffects as appropriate.

fileName

Character string denoting file name if file output desired.

includeOnly

Boolean. If TRUE, only effects with the include flag TRUE will be printed.

expandDummies

Interpret the timeDummy column and show any effects which would be added by sienaTimeFix.

includeRandoms

Boolean. If TRUE, also the randomEffects column will be printed.

dropRates

Boolean. If TRUE, do not print the rows for basic rate effects.

For extra arguments (none used at present).

Value

The function print.sienaEffects prints details of the main columns of the selected rows of the effects object.

The function summary.sienaEffects checks the rows for valid printing via print.data.frame and excludes any that will fail. The OK columns are printed first, followed by any others.

Output from either can be directed to a file by using the argument filename.

References

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

See Also

sienaTimeTest, effectsDocumentation

Examples

Run this code
# NOT RUN {
mynet1 <- sienaDependent(array(c(s501, s502, s503), dim=c(50, 50, 3)))
mybeh <- sienaDependent(s50a, type="behavior")
mycovar <- coCovar(rnorm(50))
mydyadcovar <- coDyadCovar(matrix(as.numeric(rnorm(2500) > 2), nrow=50))
mydata <- sienaDataCreate(mynet1, mybeh, mycovar, mydyadcovar)
myeff <- getEffects(mydata)
myeff
summary(myeff)
# }

Run the code above in your browser using DataLab