factorComposite | R Documentation |
Create a composite using a Factor Model
factorComposite( object, type = c("onefactor", "secondorderfactor", "bifactor"), factors = list(NA_character_) )
object |
An object of class |
type |
A character string indicating the type of factor model to use |
factors |
A named list where names are the factor names and each element is a character string of the indicator names. |
An S4 object of class FactorScores
.
Other composite:
mahalanobisComposite()
,
sumComposite()
# this example creates distances for the built in mtcars data # see ?mtcars for more details # The distances are calculated from the "best" in the dataset # First we create an appropriate CompositeData class object # higher mpg & hp are better and lower wt & qsec are better d <- CompositeData(mtcars[, c("mpg", "hp", "wt", "qsec")], thresholds = list(one = with(mtcars, c( mpg = max(mpg), hp = max(hp), wt = min(wt), qsec = min(qsec))) ), higherisbetter = c(TRUE, TRUE, FALSE, FALSE)) # create the distance scores # and the composite # covariance matrix will be calculated from the data # and data will be standardized to unit variance by default dres <- prepareComposite(d) # see a density plot of the distance scores dres@distanceDensity # regular summary of distance scores summary(dres@distances) # examine covariance matrix round(dres@covmat,2) # now we can create the composite based on summing the (standardized) # distances from our defined thresholds # by default, distances are squared, then summed, and then square rooted # to be back on the original scale fcomp <- factorComposite(dres, type = "onefactor") # view a histogram of the composite scores fcomp@scoreHistogram # summarize the composite scores summary(fcomp@scores) ## Not run: # we can also fit a second-order factor model # there are not enough indicators to identify the factor # and so lavaan gives us warning messages fcomp2 <- factorComposite(dres, type = "secondorderfactor", factors = list(speed = c("hp", "qsec"))) # view a histogram of the composite scores fcomp2@scoreHistogram # summarize the composite scores summary(fcomp2@scores) # compare one and second-order factor model scores plot(fcomp@scores, fcomp2@scores) # cleanup rm(d, dres, fcomp, fcomp2) ## End(Not run)
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