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This is a helper function which facilitates the calculation of round robin effects if many variables are assessed. Only univariate analyses are possible at the moment.
getEffects(formule, data, varlist, by=NA, na.rm=TRUE,
minVar=localOptions$minVar, gm=FALSE, ...)
The right hand side of the formula, specifying the actor, partner and group variable.
The data frame.
A vector with the column names (the column numbers are not possible!) of the variables which should be inserted at the left hand side of the formula.
By which variables should the results be merged? If by is NA (the default), a intelligent default handling is performed. It is strongly recommended to keep the defaults.
This argument is passed to function RR
.
Set the minVar parameter for all analyses. See RR
for details on this parameter.
Should effects returned as group centered (gm=FALSE
, or with group means included (gm=TRUE
)?)
Additional parameters passed to RR (e.g., selfenhance)
A data frame with all effects is returned
# NOT RUN {
data(likingLong)
res <- getEffects(~perceiver.id*target.id,
data=likingLong,
varlist=c("liking_a", "liking_b", "metaliking_a", "metaliking_b"))
str(res)
# effects including group means:
res.gm <- getEffects(~perceiver.id*target.id,
data=likingLong, gm=TRUE,
varlist=c("liking_a", "liking_b", "metaliking_a", "metaliking_b"))
str(res.gm)
# Multipe groups
#-----------------------
data("multiLikingLong")
res.g <- getEffects(~perceiver.id*target.id|group.id,
data=multiLikingLong,
varlist=c("liking_a", "liking_b", "metaliking_a", "metaliking_b"))
str(res.g)
# effects including group means:
res.g.gm <- getEffects(~perceiver.id*target.id|group.id,
data=multiLikingLong, gm=TRUE,
varlist=c("liking_a", "liking_b", "metaliking_a", "metaliking_b"))
str(res.g.gm)
# }
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