Running 'verifyTests.r' [178s]
Running the tests in 'tests/verifyTests.r' failed.
Complete output:
  > ## The codes to verify the calculated results for npordtests package
  > 
  > library(npordtests)
  > 
  > ## This is the code to verify the calculated results for AtTest by using
  > ## the example of Lehmann (1975) to determine whether a certain diagnostic
  > ## test can be interpreted successfully without much psychological training.
  > 
  > data("lehmann")
  > AtTest(Values~Group,lehmann)
  --------------------------------------------------------- 
    Test : Adaptive Test 
    data : Values and Group 
  
    Statistic = 851 
    Mean = 583.1944 
    Variance = 6570.726 
    Z = 3.303794 
    Asymp. p-value = 0.0004769302 
  
    Result : Null hypothesis is rejected. 
  --------------------------------------------------------- 
  
  > 
  > ## Results
  > # Test : Adaptive Test
  > # data : Values and Group
  > #
  > # Statistic = 851
  > # Mean = 583.1944
  > # Variance = 6570.726
  > # Z = 3.303794
  > # Asymp. p-value = 0.0004769302
  > #
  > # Result : Null hypothesis is rejected.
  > ## The results are the same as in Beier and Buning (1997).
  > 
  > # ----------------------------------------------------------------------------------
  > 
  > ## The data could not be obtained from the authors.
  > 
  > data("jdata")
  > FtmTest(Y~X,jdata)
  --------------------------------------------------------- 
    Test : Ferdhiana, Terpstra and Magel Test 
    data : Y and X 
  
    Statistic = 122.6667 
    Mean = 0 
    Variance = 3261.63 
    Z = 2.147876 
    Asymp. p-value = 0.0158618 
  
    Result : Null hypothesis is rejected. 
  --------------------------------------------------------- 
  
  > 
  > ## Results
  > # ---------------------------------------------------------
  > # Test : Ferdhiana, Terpstra and Magel Test
  > # data : Y and X
  > #
  > # Statistic = 122.6667
  > # Mean = 0
  > # Variance = 3261.63
  > # Z = 2.147876
  > # Asymp. p-value = 0.0158618
  > #
  > # Result : Null hypothesis is rejected.
  > # ---------------------------------------------------------
  > ## The results are consistent with our manual solutions.
  > 
  > 
  > # ----------------------------------------------------------------------------------
  > 
  > ## This is the code to verify the calculated results for JtTest by using
  > ## the fictive example data set of Neuhauser (1998).
  > 
  > data("neuhauser")
  > JtTest(value~group,neuhauser)
  --------------------------------------------------------- 
    Test : Jonckheere-Terpstra Test 
    data : value and group 
  
    Statistic = 442 
    Mean = 300 
    Variance = 1716.667 
    Z = 3.427247 
    Asymp. p-value = 0.0003048675 
  
    Result : Null hypothesis is rejected. 
  --------------------------------------------------------- 
  
  > 
  > ## Results
  > # Test : Jonckheere-Terpstra Test
  > # data : value and group
  > #
  > # Statistic = 442
  > # Mean = 300
  > # Variance = 1716.667
  > # Z = 3.427247
  > # Asymp. p-value = 0.0003048675
  > #
  > # Result : Null hypothesis is rejected.
  > ## The results are the same as in Neuhauser (1998).
  > 
  > # ----------------------------------------------------------------------------------
  > 
  > ## This is the code to verify the calculated results for MjtTest by using
  > ## the fictive example data set of Neuhauser (1998).
  > 
  > MjtTest(value~group,neuhauser)
  --------------------------------------------------------- 
    Test : Modified Jonckheere-Terpstra Test 
    data : value and group 
  
    Statistic = 784 
    Mean = 500 
    Variance = 6833.333 
    Z = 3.435596 
    Asymp. p-value = 0.0002956264 
  
    Result : Null hypothesis is rejected. 
  --------------------------------------------------------- 
  
  > 
  > ## Results
  > # Test : Modified Jonckheere-Terpstra Test
  > # data : value and group
  > #
  > # Statistic = 784
  > # Mean = 500
  > # Variance = 6833.333
  > # Z = 3.435596
  > # Asymp. p-value = 0.0002956264
  > #
  > # Result : Null hypothesis is rejected.
  > ## The results are the same as in Neuhauser (1998).
  > 
  > # ----------------------------------------------------------------------------------
  > 
  > ## This is the code to verify the calculated results for TmTest by using
  > ## the fictive example data set of Neuhauser (1998).
  > 
  > TmTest(value~group,neuhauser)
  --------------------------------------------------------- 
    Test : Terpstra-Magel Test 
    data : value and group 
  
    Statistic = 1274 
    Mean = 416.6667 
    Variance = 84595.73 
    Z = 2.947647 
    Asymp. p-value = 0.001601012 
  
    Result : Null hypothesis is rejected. 
  --------------------------------------------------------- 
  
  > 
  > ## Results
  > # Test : Terpstra-Magel Test
  > # data : value and group
  > #
  > # Statistic = 1274
  > # Mean = 416.6667
  > # Variance = 84595.73
  > # Z = 2.947647
  > # Asymp. p-value = 0.001601012
  > #
  > # Result : Null hypothesis is rejected.
  > ## The results are the same as in Terpstra et al. (2003).
  > 
  > # ----------------------------------------------------------------------------------
  > 
  > ## This is the code to verify the calculated results for KtpTest by using
  > ## the data is taken from Terpstra et al. (2011).
  > 
  > data("hvwi")
  > KtpTest(Values~Group,hvwi)
  --------------------------------------------------------- 
    Test : KTP Test 
    data : Values and Group 
  
    Statistic = 56108.28 
    Mean = 0 
    Variance = 362485627 
    Z = 2.94701 
    Asymp. p-value = 0.001604316 
  
    Result : Null hypothesis is rejected. 
  --------------------------------------------------------- 
  
  > 
  > ## Results
  > # Test : KTP Test
  > # data : Values and Group
  > #
  > # Statistic = 56108.28
  > # Mean = 0
  > # Variance = 362485627
  > # Z = 2.94701
  > # Asymp. p-value = 0.001604316
  > #
  > # Result : Null hypothesis is rejected.
  > ## The results are the same as in Terpstra et al. (2011).
  > 
  > # ----------------------------------------------------------------------------------
  > 
  > ## This is the code to verify the calculated results for SsTest by using
  > ## the data is taken from Shan et al. (2014).
  > 
  > data("hypertension")
  > SsTest(rdbp~doseLevel,hypertension)
  --------------------------------------------------------- 
    Test : Shan's S test 
    data : rdbp and doseLevel 
  
    Statistic = 28868 
    Mean = 19929.5 
    Variance = 2585436 
    Z = 5.559014 
    Asymp. p-value = 1.356515e-08 
  
    Result : Null hypothesis is rejected. 
  --------------------------------------------------------- 
  
  > 
  > ## Results
  > #   Test : Shan's S test
  > #   data : rdbp and doseLevel
  > #
  > #   Statistic = 28868
  > #   Mean = 19929.5
  > #   Variance = 2585436
  > #   Z = 5.559014
  > #   Asymp. p-value = 1.356515e-08
  > #
  > #   Result : Null hypothesis is rejected.
  > ## The results are the same as in Shan et al. (2014).
  > 
  > # ----------------------------------------------------------------------------------
  > 
  > ## There is no numerical example for GcTest in literature.
  > 
  > GcTest(Y~X,jdata)
  --------------------------------------------------------- 
    Test : Gaur's Gc Test 
    data : Y and X 
  
    Statistic = 0.375 
    Mean = 0 
    Variance = 0.06746032 
    Z = 1.4438 
    Asymp. p-value = 0.0743976 
  
    Result : Null hypothesis is not rejected. 
  --------------------------------------------------------- 
  
  > 
  > ## Results
  > #   Test : Gaur's Gc Test
  > #   data : Y and X
  > #
  > #   Statistic = 0.375
  > #   Mean = 0
  > #   Variance = 0.06746032
  > #   Z = 1.4438
  > #   Asymp. p-value = 0.0743976
  > #
  > #   Result : Null hypothesis is not rejected.
  > ## The results are consistent with our manual solutions.
  > 
  > 
  > proc.time()
     user  system elapsed 
     7.37    0.12    7.50