Running ‘emTests.R’ [7s/10s]
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 ...