Running ‘testthat.R’ [53s/49s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(umx)
  Loading required package: OpenMx
  Loading required package: digest
  Loading required package: MASS
  Loading required package: Matrix
  Loading required package: Rcpp
  Loading required package: parallel
  To take full advantage of multiple cores, use:
    mxOption(NULL, 'Number of Threads', parallel::detectCores())
  
  Attaching package: 'OpenMx'
  
  The following objects are masked from 'package:Matrix':
  
      %&%, expm
  
  For an overview type '?umx'
  
  Attaching package: 'umx'
  
  The following object is masked from 'package:stats':
  
      loadings
  
  > 
  > test_check("umx")
  Running One Factor with 10 parameters
  
  
  |   |x1   |x2    |x3   |x4    |x5 |
  |:--|:----|:-----|:----|:-----|:--|
  |x1 |.    |.     |0.01 |.     |.  |
  |x2 |.    |.     |0.01 |-0.01 |.  |
  |x3 |0.01 |0.01  |.    |.     |.  |
  |x4 |.    |-0.01 |.    |.     |.  |
  |x5 |.    |.     |.    |.     |.  |
  [1] "nb: You can zoom in on bad values with, e.g. suppress = .01, which will hide values smaller than this. Use digits = to round"
  Running m1 with 38 parameters
  Running by_bracket1 with 38 parameters
  Running bylabel with 38 parameters
  Running m1 with 26 parameters
  Running by_bracket with 26 parameters
  Running bylabel with 26 parameters
  Running One Factor with 15 parameters
  Running Saturated One Factor with 20 parameters
  Running Independence One Factor with 10 parameters
  Running One Factor with 10 parameters
  Error: The job for model 'One Factor' exited abnormally with the error message: Non-conformable matrices [(5 x 1) and (5 x 1)] in Matrix Multiply.
  In addition: Warning messages:
  1: In model 'One Factor' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) 
  2: In model 'm1' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) 
  3: In model 'by_bracket1' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) 
  4: In model 'bylabel' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) 
  5: In model 'm1' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) 
  6: In model 'by_bracket' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) 
  7: In model 'bylabel' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) 
  8: In model 'One Factor' Optimizer returned a non-zero status code 6. The model does not satisfy the first-order optimality conditions to the required accuracy, and no improved point for the merit function could be found during the final linesearch (Mx status RED) 
  9: In mxMLObjective("R", dimnames = names(demoOneFactor)) :
    Objective functions like mxMLObjective() have been deprecated in favor of expectation and fit functions.
   Please use mxExpectationNormal(covariance= , ...) instead, and add a call to mxFitFunctionML()
   See examples at help(mxExpectationNormal)
  testthat results ================================================================
  OK: 9 SKIPPED: 0 FAILED: 0
  Execution halted