Description Usage Arguments Details Value See Also
Generates a Matrix of Implied Causation for a given model, following the rules in Brick and Bailey (submitted).
1 2 |
model |
An SEM model (see details for compatibility) |
latents |
Compute causal influences of/on latent variables? (default TRUE) |
standardized |
Compute causal influences using the standardized model parameters? (default FALSE) |
exogenous |
Compute causal influences of exogenous variables? (default TRUE) |
se |
Compute SEs? (default NA; see details) |
N |
Number of observations for hypothetical standard errors |
... |
Other parameters passed to mxEval (e.g. defvar.row=) |
MICs trace all paths from all variables to all others, and present the result as an asymmetric matrix. The value of a single path is the product of all single-headed arrows taken as part of that path, and the total causal influence is the sum of all paths that connect one variable to another by following only single-headed arrows, and only in the direction of the arrow.
Standard errors are computed by the delta method using the mxSE function. Options for SE computation are FALSE (do not compute), "observed" (compute using existing data), "hypothetical" (compute assuming perfect model fit). Any other value (default) selects observed if the model has been run and has not been modified since running, and hypothetical otherwise. Observed computes the SE as a traditional standard error on the fitted model; this is best if the goal is to examine an existing model fit. Hypothetical SEs assume the model is correct. The function generates a covariance and means matrix from the model expectation and fits a new model using those values as data. It then computes SEs using the delta method from these observed data. If data exists in the model, the number of data rows specified there is used. If not, N must be specified.
Currently accepts MxRAMModel objects, and converts basic lavaan and blavaan objects. Standard Errors are not currently available for converted or standardized models.
The MIC for this model.
MICTable
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