mxBootstrapStdizeRAMpaths {OpenMx}R Documentation

Bootstrap distribution of standardized RAM path coefficients


Uses the bootstrap distribution of a RAM model's free parameters to produce a bootstrap distribution of standardized path coefficients. Model must have already been run through mxBootstrap().


mxBootstrapStdizeRAMpaths(model, bq=c(.25,.75), 
	method=c('bcbci','quantile'), returnRaw=FALSE)



An MxModel that uses RAM expectation and has already been run through mxBootstrap().


Quantiles corresponding to the lower and upper limits, respectively, of the bootstrap confidence interval.


Character string. One of 'bcbci' or 'quantile'.


Logical; should the function return the raw bootstrapping results? Defaults to FALSE, in which case a dataframe summarizing the results is returned.


In effect, what mxBootstrapStdizeRAMpaths() does is use the point estimates from each bootstrap replication to produce mxStandardizeRAMpaths() output for each replication. The output of mxStandardizeRAMpaths() has one entry for each nonzero path coefficient. Therefore, it is possible (though unlikely) that the number of nonzero paths, or which elements of the A and S RAM matrices are nonzero, may vary among bootstrap replications. Such an occurrence defies simple summary of the standardized paths' bootstrapping results. In this case, a raw list of bootstrapping results is returned, with a warning, if returnRaw=TRUE, or an error is throw if returnRaw=FALSE.

mxBootstrapStdizeRAMpaths() ignores any submodels of model. That is, it must be able to find, in the MxModel it is provided, a RAM expectation and an MxComputeBootstrap object. It can be run on submodels directly,


Under extraordinary circumstances described above, mxBootstrapStdizeRAMpaths() may return a list object. In ordinary circumstances, if returnRaw=FALSE (default), it returns a dataframe containing, inter alia, the standardized path coefficients as estimated from the real data, their bootstrap SEs, and the lower and upper limits of a bootstrap confidence interval. If instead returnRaw=TRUE, mxBootstrapStdizeRAMpaths() returns a matrix containing the raw bootstrap results; this matrix has one column per nonzero path coefficient, and one row for each succesfully converged bootstrap replication.

See Also

mxBootstrap(), mxStandardizeRAMpaths(), mxBootstrapEval, mxSummary


myFADataRaw <- myFADataRaw[,c("x1","x2","x3","x4","x5","x6")]
dataRaw      <- mxData( observed=myFADataRaw, type="raw" )
resVars      <- mxPath( from=c("x1","x2","x3","x4","x5","x6"), arrows=2,
                        free=TRUE, values=c(1,1,1,1,1,1),
                        labels=c("e1","e2","e3","e4","e5","e6") ) 
latVar       <- mxPath( from="F1", arrows=2,
                        free=TRUE, values=1, labels ="varF1" )
facLoads     <- mxPath( from="F1", to=c("x1","x2","x3","x4","x5","x6"), arrows=1,
                        free=c(FALSE,TRUE,TRUE,TRUE,TRUE,TRUE), values=c(1,1,1,1,1,1),
                        labels =c("l1","l2","l3","l4","l5","l6") )
means        <- mxPath( from="one", to=c("x1","x2","x3","x4","x5","x6","F1"), arrows=1,
                        labels =c("meanx1","meanx2","meanx3",
                                  "meanx4","meanx5","meanx6",NA) ) 
oneFactorModel <- mxModel("Common Factor Model Path Specification", type="RAM",
                        manifestVars=c("x1","x2","x3","x4","x5","x6"), latentVars="F1",
                        dataRaw, resVars, latVar, facLoads, means)
oneFactorFit <- mxRun(oneFactorModel)    

# replications=20 is only here to make the example run quickly.
# You should use many more replications for higher accuracy.

oneFactorBoot <- mxBootstrap(oneFactorFit, replications=20)

[Package OpenMx version 2.7.16 Index]