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General strategies for locating errors

  • Use the traceback() function in R after an error occurs.
    It will print out the sequence of function calls that lead to the error.

Elements of @output$status

Each of the output codes should report the appropriate error message at the end of evaluation of the mxRun() statement.

@output$status[[1]]

This status element is returned from the optimizer (NPSOL).

A value of −1 means that the optimizer found itself stuck in a location where the objective function could not be calculated, and could not find a way out. This most often happens if the starting values make the calculation impossible.

Codes 2 and 3 mean that the bounds and constraints, respectively, could not be satisfied.

NPSOL output 4 means that the iteration limit was reached, with no solution found. You can use mxOptions() to set a higher iteration limit, or just run mxRun() using the output –it will restart from the most recent set of estimates, and make another run of the same number of iterations.

Any other status codes should be reported.

@output$status[[2]]

output$status[[2]] returns the error code from OpenMx, which is usually −1. This indicates that some aspect of the objective calculation could not be completed at the current location.

@output$status[[3]]

The third element of status provides detailed information on why the objective function could not be calculated. Examples are:

@output$status[[3]] [CHARSXP: "Covariance matrix is not positive-definite."]
  • Look at your starting values. if you have used starting values 1s, you are building an initial expected covariance that is exactly singular. At the first iteration, OpenMx can't invert the expected covariance matrix, and crashes.
  • Solution: Try changing your starting values (say, by making all of the covariances and free regressions .5) and see if it runs.

Error messages and their meaning

Note, while R coders often use the period character inside variable names, this is not legal in OpenMx, which uses this character to specify the container model for variables.

"Argument is missing, with no default"

  • Variations include:
  • Error in single.na(to) : argument is missing, with no default
  • Typical cause: A leading or trailing comma in your code makes R think that you wanted to pass in a parameter, but left it empty (hence "missing, with not default")
    • Examples:
    • leading comma
      to=c(,'x','y','z')
    • trailing comma
      mxMatrix("Name", nrow=3,ncol=3,free=TRUE, values=.5,)
       
    • Cure: remove the errant comma

    Error in convertVFN(... NAs introduced by coercion

    • Whenever you see NAs being introduced by coercion, suspect that you have the wrong type of value for a parameter: For instance, perhaps you are setting a number to string or vice versa... the coercion will fail, leaving an NA, which R warns you about.
    • Example:
      #You say:
      			mxMatrix(type = "Full", nrow = 1, ncol = 1, free = FALSE, 
      			values = "l1", labels = "aa", 
      			lbound = 0, ubound = 2, name = "la"),
       
    • We try and convert the character "l1" to a numeric value, can't, return an NA... hence the error: NAs induced by coercion.

    • The error:
      			Warning messages:
      			1: In convertVFN(values, free, labels, lbound, ubound, nrow, ncol) :
      			NAs introduced by coercion...
       
    • Solution: in this case, set the value to a numeric, (and remember that to equate values, you set the labels to be the same)