[su_tabs] [su_tab title=“xxM”]
For each parameter matrix, construct three related matrices:
xxmModel()
is used to declare level names. The function returns a model object that is passed as a parameter to subsequent stattements.
For each declared level xxmSubmodel()
is invoked to add corresponding submodel to the model object. The function adds three pieces of information:
1. parents declares a list of parents of the current level.
2. variables declares names of observed dependent (ys), observed independent (xs) and latent variables (etas) for the level.
3. data R data object for the current level.
For each declared level xxmWithinMatrix()
is used to add within-level parameter matrices. For each parameter matrix, the function adds the three matrices constructed earlier:
Pairs of levels that share parent-child relationship have regression relationships. xxmBetweenMatrix()
is used to add corresponding parameter matrices connecting the two levels.
For each parameter matrix, the function adds the three matrices constructed earlier:
Estimation process is initiated by xxmRun()
. If all goes well, a quick printed summary of results is produced.
Once parameters are estimated, confidence inetrvals are estimated by invoking xxmCI()
. Depending on the the number of observations and the complexity of the dependence structure xxmCI()
may take very long. xxMCI()
displays a table of parameter estimates and CIS.
A summary of results may be retrived as an R list by a call to xxmSummary()
xxM
model object may hog a large amount of RAM outside of R's memory. This memory will automatically be released, when R's workspace is cleared by a call to rm(list=ls())
or at the end of the R session. Alternatively, xxmFree()
may be called to release memory.
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