The PSI matrix contains the covariances of the continuous variables, which is where the covariances of the independent variables in this model would be listed, if they were estimated (all other matrices were omitted to save space). The PSI matrix can be read like a correlation matrix, except that the values listed (in this case) are the parameter numbers, rather than the estimates of the values of those parameters. Note that not all matrices are required for every model, and only the relevant matrices are printed.
#STATPLUS REGRESSION WONT PICK UP DEPENDENT VARIABLE MANUAL#
The content of each of these matrices is described in the chapter of the Mplus manual titled “Output, savedata, and plot commands,” under the heading “Mplus Parameter Arrays”. The matrices (and sometimes vectors) are identified by Greek letters. Parameters that are not estimated are represented by 0s in the matrices. Within the model, estimated parameters are identified with numbers, starting with one and numbering sequentially. Each parameter estimated by the model is listed in one of the matrices printed by tech1. We can use the listing of model parameters to confirm that the covariances were not estimated. The tech1 option produces two sets of matrices, one of which shows all the estimated parameters (the other shows their starting values, but we won’t deal with that set in this FAQ). Any time we are unsure about which parameters are being estimated, we can use the option tech1 to get a listing of all parameters in a model. We already have a fairly good idea that the covariances of the independent variables (i.e., x1, x2 and x3) are not being modeled, but if we want to check further, we can. This model are also identical to those you would find if you ran a regression in any statistical package. Although Mplus does not explicitly include the covariances between the independent variables by default, the coefficient estimates are identical to those you would receive if you explicitly modeled the covariances between the independent variables (see below for a demonstration of this). Note that the covariances of the x variables are not listed, indicating that they were not estimated as part of the model. Under “MODEL RESULTS” we see estimated coefficients for the regression of y on the three x variables, as well as estimates of the intercept of y and the residual variance of y. You can view the entire output by clicking here.
Data:īelow are selected portions of the output generated by the input file.
We have included tech1 under Output, this will allow us to see a listing of all parameters estimated in the model. Note that we have not specifically included correlations between the x variables in our model. The Model section of the input file contains the command y on x1 x2 x3, which specifies that y should be regressed on the three x variables. To fit this model we use the Mplus input file below. all variables are observed) we have a single dependent variable ( y), predicted by three independent or predictor variables ( x1, x2 and x3). Assume that all of the variables are continuous. In this simple example of a path model (i.e.
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