Running ‘clustering1test.R’ [1s/1s]
  Running ‘clusteringParametertest.R’ [1s/1s]
  Running ‘clusteringPartitiontest.R’ [1s/1s]
  Running ‘heterogeneousDAtest.R’ [1s/1s]
  Running ‘heterogeneousclusteringtest.R’ [1s/1s]
  Running ‘ssupervised.R’ [1s/1s]
  Running ‘test.R’ [12s/12s]
Running the tests in ‘tests/test.R’ failed.
Complete output:
  > library(Rmixmod)
  Loading required package: Rcpp
  Rmixmod version 2.1.1 loaded
  R package of mixmodLib version 3.2.2
  
  Condition of use
  ----------------
  Copyright (C)  MIXMOD Team - 2001-2013
  
  MIXMOD is publicly available under the GPL license (see www.gnu.org/copyleft/gpl.html)
  You can redistribute it and/or modify it under the terms of the GPL-3 license.
  Please understand that there may still be bugs and errors. Use it at your own risk.
  We take no responsibility for any errors or omissions in this package or for any misfortune that may befall you or others as a result of its use.
  
  Please report bugs at: http://www.mixmod.org/article.php3?id_article=23
  
  More information on : www.mixmod.org
  
  > 
  > ### Models
  > 
  > # Gaussian models
  > all <- mixmodGaussianModel()
  > all
  ****************************************
  *** MIXMOD Models:
  * list =  Gaussian_pk_L_I Gaussian_pk_Lk_I Gaussian_pk_L_B Gaussian_pk_Lk_B Gaussian_pk_L_Bk Gaussian_pk_Lk_Bk Gaussian_pk_L_C Gaussian_pk_Lk_C Gaussian_pk_L_D_Ak_D Gaussian_pk_Lk_D_Ak_D Gaussian_pk_L_Dk_A_Dk Gaussian_pk_Lk_Dk_A_Dk Gaussian_pk_L_Ck Gaussian_pk_Lk_Ck Gaussian_p_L_I Gaussian_p_Lk_I Gaussian_p_L_B Gaussian_p_Lk_B Gaussian_p_L_Bk Gaussian_p_Lk_Bk Gaussian_p_L_C Gaussian_p_Lk_C Gaussian_p_L_D_Ak_D Gaussian_p_Lk_D_Ak_D Gaussian_p_L_Dk_A_Dk Gaussian_p_Lk_Dk_A_Dk Gaussian_p_L_Ck Gaussian_p_Lk_Ck 
  * This list includes models with free and equal proportions.
  ****************************************
  > 
  > # Multinomial models
  > all <- mixmodMultinomialModel()
  > all
  ****************************************
  *** MIXMOD Models:
  * list =  Binary_pk_E Binary_pk_Ekj Binary_pk_Ekjh Binary_pk_Ej Binary_pk_Ek Binary_p_E Binary_p_Ekj Binary_p_Ekjh Binary_p_Ej Binary_p_Ek 
  * This list includes models with free and equal proportions.
  ****************************************
  > 
  > only.free.proportions <- mixmodMultinomialModel(equal.proportions = FALSE)
  > list.models <- mixmodMultinomialModel(listModels = c("Binary_p_E","Binary_p_Ekjh", "Binary_pk_Ekj", "Binary_pk_Ej"))
  > 
  > var.independent <- mixmodMultinomialModel(variable.independency = TRUE)
  > var.comp.independent <- mixmodMultinomialModel(variable.independency = TRUE, component.independency = TRUE)
  > 
  > # Strategy
  > mixmodStrategy()
  ****************************************
  *** MIXMOD Strategy:
  * algorithm            =  EM 
  * number of tries      =  1 
  * number of iterations =  200 
  * epsilon              =  0.001 
  *** Initialization strategy:
  * algorithm            =  smallEM 
  * number of tries      =  10 
  * number of iterations =  5 
  * epsilon              =  0.001 
  * seed                 =  NULL 
  ****************************************
  > strategy1 <- mixmodStrategy(algo = "CEM", initMethod = "random", nbTry = 10, epsilonInInit = 0.000001)
  > strategy2 <- mixmodStrategy(algo = c("SEM", "EM"), nbIterationInAlgo = c(200, 100), epsilonInAlgo = c(NA, 0.0001))
  > 
  > ###  Unsupervised classification
  > 
  > # Continuous variables: Geyser dataset
  > data("geyser")
  > xem.geyser <- mixmodCluster(data = geyser, nbCluster = 2:8,criterion = c("BIC", "ICL", "NEC"), models = mixmodGaussianModel(),strategy = strategy2)
  > xem.geyser
  ****************************************
  *** INPUT:
  ****************************************
  * nbCluster =  2 3 4 5 6 7 8 
  * criterion =  BIC ICL NEC 
  ****************************************
  *** MIXMOD Models:
  * list =  Gaussian_pk_L_I Gaussian_pk_Lk_I Gaussian_pk_L_B Gaussian_pk_Lk_B Gaussian_pk_L_Bk Gaussian_pk_Lk_Bk Gaussian_pk_L_C Gaussian_pk_Lk_C Gaussian_pk_L_D_Ak_D Gaussian_pk_Lk_D_Ak_D Gaussian_pk_L_Dk_A_Dk Gaussian_pk_Lk_Dk_A_Dk Gaussian_pk_L_Ck Gaussian_pk_Lk_Ck Gaussian_p_L_I Gaussian_p_Lk_I Gaussian_p_L_B Gaussian_p_Lk_B Gaussian_p_L_Bk Gaussian_p_Lk_Bk Gaussian_p_L_C Gaussian_p_Lk_C Gaussian_p_L_D_Ak_D Gaussian_p_Lk_D_Ak_D Gaussian_p_L_Dk_A_Dk Gaussian_p_Lk_Dk_A_Dk Gaussian_p_L_Ck Gaussian_p_Lk_Ck 
  * This list includes models with free and equal proportions.
  ****************************************
  * data (limited to a 10x10 matrix) =
        Duration Waiting.Time
   [1,] 3.6      79          
   [2,] 1.8      54          
   [3,] 3.333    74          
   [4,] 2.283    62          
   [5,] 4.533    85          
   [6,] 2.883    55          
   [7,] 4.7      88          
   [8,] 3.6      85          
   [9,] 1.95     51          
  [10,] 4.35     85          
  * ... ...
  ****************************************
  *** MIXMOD Strategy:
  * algorithm            =  SEM EM 
  * number of tries      =  1 
  * number of iterations =  200 100 
  * epsilon              =  NaN 1e-04 
  *** Initialization strategy:
  * algorithm            =  smallEM 
  * number of tries      =  10 
  * number of iterations =  5 
  * epsilon              =  0.001 
  * seed                 =  NULL 
  ****************************************
  
  
  ****************************************
  *** BEST MODEL OUTPUT:
  *** According to the BIC criterion
  ****************************************
  * nbCluster   =  6 
  * model name  =  Gaussian_pk_Lk_D_Ak_D 
  * criterion   =  BIC(2152.5833) ICL(2238.5153) NEC(0.1444)
  * likelihood  =  -992.2046 
  ****************************************
  *** Cluster 1 
  * proportion =  0.0147 
  * means      =  1.8500 48.0000 
  * variances  = |        NaN        NaN |
                 |        NaN        NaN |
  *** Cluster 2 
  * proportion =  0.4562 
  * means      =  4.4401 81.4443 
  * variances  = |        NaN        NaN |
                 |        NaN        NaN |
  *** Cluster 3 
  * proportion =  0.0173 
  * means      =  1.7852 51.2280 
  * variances  = |        NaN        NaN |
                 |        NaN        NaN |
  *** Cluster 4 
  * proportion =  0.0437 
  * means      =  4.4409 77.0000 
  * variances  = |        NaN        NaN |
                 |        NaN        NaN |
  *** Cluster 5 
  * proportion =  0.1457 
  * means      =  3.7590 76.0516 
  * variances  = |        NaN        NaN |
                 |        NaN        NaN |
  *** Cluster 6 
  * proportion =  0.3223 
  * means      =  2.0544 54.9134 
  * variances  = |        NaN        NaN |
                 |        NaN        NaN |
  ****************************************
  > summary(xem.geyser)
  **************************************************************
  * Number of samples    =  272 
  * Problem dimension    =  2 
  **************************************************************
  *       Number of cluster =  6 
  *              Model Type =  Gaussian_pk_Lk_D_Ak_D 
  *               Criterion =  BIC(2152.5833) ICL(2238.5153) NEC(0.1444)
  *              Parameters =  list by cluster
  *                  Cluster  1 : 
                           Proportion =  0.0147 
                                Means =  1.8500 48.0000 
                            Variances = |        NaN        NaN |
                                        |        NaN        NaN |
  *                  Cluster  2 : 
                           Proportion =  0.4562 
                                Means =  4.4401 81.4443 
                            Variances = |        NaN        NaN |
                                        |        NaN        NaN |
  *                  Cluster  3 : 
                           Proportion =  0.0173 
                                Means =  1.7852 51.2280 
                            Variances = |        NaN        NaN |
                                        |        NaN        NaN |
  *                  Cluster  4 : 
                           Proportion =  0.0437 
                                Means =  4.4409 77.0000 
                            Variances = |        NaN        NaN |
                                        |        NaN        NaN |
  *                  Cluster  5 : 
                           Proportion =  0.1457 
                                Means =  3.7590 76.0516 
                            Variances = |        NaN        NaN |
                                        |        NaN        NaN |
  *                  Cluster  6 : 
                           Proportion =  0.3223 
                                Means =  2.0544 54.9134 
                            Variances = |        NaN        NaN |
                                        |        NaN        NaN |
  *          Log-likelihood =  -992.2046 
  **************************************************************
  > plot(xem.geyser)
  Error in eigen(A) : infinite or missing values in 'x'
  Calls: plot -> plot -> .local -> plotCluster -> ellipse -> eigen
  Execution halted