Running ‘emTests.R’
  Running ‘impTests.R’
  Comparing ‘impTests.Rout’ to ‘impTests.Rout.save’ ... OK
  Running ‘logpostTests.R’
  Comparing ‘logpostTests.Rout’ to ‘logpostTests.Rout.save’ ... OK
  Running ‘mcmcTests.R’
  Comparing ‘mcmcTests.Rout’ to ‘mcmcTests.Rout.save’ ... OK
  Running ‘miInferenceTests.R’
  Comparing ‘miInferenceTests.Rout’ to ‘miInferenceTests.Rout.save’ ... OK
Running the tests in ‘tests/emTests.R’ failed.
Complete output:
  > library(norm2)
  > 
  > ## run EM on fake data with no missing values
  > set.seed(1234)
  > simdata <- data.frame(
  +    Y1=rnorm(6), Y2=rnorm(6), Y3=rnorm(6), X1=rnorm(6) )
  > emResult <- emNorm( cbind(Y1,Y2,Y3) ~ X1, data=simdata )
  > print( summary( emResult ) )
  Predictor (X) variables:
                   Mean       SD Observed Missing Pct.Missing
  (Intercept) 1.0000000 0.000000        6       0           0
  X1          0.2068512 1.178512        6       0           0
  
  Response (Y) variables:
           Mean        SD Observed Missing Pct.Missing
  Y1 -0.2092854 1.2953550        6       0           0
  Y2 -0.6752401 0.2138685        6       0           0
  Y3 -0.2141319 0.6864124        6       0           0
  
  Missingness patterns for response (Y) variables
     (. denotes observed value, m denotes missing value)
     (variable names are displayed vertically)
     (rightmost column is the frequency):
  YYY
  123
  ... 6
  
  Method:                             EM
  Prior:                              "uniform"
  Convergence criterion:              1e-05
  Iterations:                         2
  Converged:                          TRUE
  Max. rel. difference:               0
  -2 Loglikelihood:                   -8.3665
  -2 Log-posterior density:           -8.3665
  Worst fraction missing information: 0
  
  Estimated coefficients (beta):
                      Y1         Y2         Y3
  (Intercept) -0.3069981 -0.6785479 -0.2457194
  X1           0.4723816  0.0159912  0.1527063
  
  Estimated covariance matrix (sigma):
             Y1         Y2         Y3
  Y1 1.14001811 0.06789367 0.13397509
  Y2 0.06789367 0.03782049 0.03942553
  Y3 0.13397509 0.03942553 0.36564511
  > 
  > ## impose missing values and run again
  > simdata$Y1[3] <- simdata$Y2[4] <- simdata$Y3[4] <- NA
  > emResult <- emNorm( cbind(Y1,Y2,Y3) ~ X1, data=simdata )
  > print( summary( emResult ) )
  Predictor (X) variables:
                   Mean       SD Observed Missing Pct.Missing
  (Intercept) 1.0000000 0.000000        6       0           0
  X1          0.2068512 1.178512        6       0           0
  
  Response (Y) variables:
           Mean        SD Observed Missing Pct.Missing
  Y1 -0.4680307 1.2630567        5       1    16.66667
  Y2 -0.6322806 0.2081665        5       1    16.66667
  Y3 -0.2349012 0.7653216        5       1    16.66667
  
  Missingness patterns for response (Y) variables
     (. denotes observed value, m denotes missing value)
     (variable names are displayed vertically)
     (rightmost column is the frequency):
  YYY
  123
  ... 4
  .mm 1
  m.. 1
  
  Method:                             EM
  Prior:                              "uniform"
  Convergence criterion:              1e-05
  Iterations:                         114
  Converged:                          TRUE
  Max. rel. difference:               9.8836e-06
  -2 Loglikelihood:                   -10.70465
  -2 Log-posterior density:           -10.70465
  Worst fraction missing information: 0.9313
  
  Estimated coefficients (beta):
                      Y1          Y2         Y3
  (Intercept) -1.0195738 -0.61863204 -0.1874030
  X1           0.5166042 -0.01611584  0.1214564
  
  Estimated covariance matrix (sigma):
              Y1          Y2          Y3
  Y1  1.80844106 -0.06800034 -0.75502100
  Y2 -0.06800034  0.03443290  0.05759078
  Y3 -0.75502100  0.05759078  0.41668488
  > 
  > ## redundant Y-variable
  > simdata$Y3 <- simdata$Y1 + simdata$Y2
  > emResult <- emNorm( cbind(Y1,Y2,Y3) ~ X1, data=simdata )
  
   *** caught segfault ***
  address 0, cause 'memory not mapped'
  
  Traceback:
   1: emNorm.default(y, x = x, intercept = FALSE, iter.max = iter.max,     criterion = criterion, estimate.worst = estimate.worst, starting.values = starting.values,     prior = prior, prior.df = prior.df, prior.sscp = prior.sscp,     ...)
   2: emNorm.formula(cbind(Y1, Y2, Y3) ~ X1, data = simdata)
   3: emNorm(cbind(Y1, Y2, Y3) ~ X1, data = simdata)
  An irrecoverable exception occurred. R is aborting now ...