Running ‘clustering1test.R’ [0s/1s]
  Running ‘clusteringParametertest.R’ [1s/1s]
  Running ‘clusteringPartitiontest.R’ [1s/1s]
  Running ‘heterogeneousDAtest.R’ [0s/1s]
  Running ‘heterogeneousclusteringtest.R’ [1s/1s]
  Running ‘ssupervised.R’ [1s/1s]
  Running ‘test.R’ [1s/2s]
Running the tests in ‘tests/clustering1test.R’ failed.
Complete output:
  > library(Rmixmod);
  Loading required package: Rcpp
  Rmixmod v. 2.1.6 / URI: www.mixmod.org
  > data(iris)
  > data <- iris[,1:4]
  > 
  > mixmodCluster(data, nbCluster=c(2,3,4));
  Error in xtfrm.data.frame(x) : cannot xtfrm data frames
  Calls: mixmodCluster ... lapply -> FUN -> as.vector -> xtfrm -> xtfrm.data.frame
  Execution halted
Running the tests in ‘tests/clusteringParametertest.R’ failed.
Complete output:
  > library(Rmixmod);
  Loading required package: Rcpp
  Rmixmod v. 2.1.6 / URI: www.mixmod.org
  > 
  > createGaussianParameter <- function(nbCluster, pbDimension){
  +     proportions <- rep(1.0/nbCluster, nbCluster);
  +     means <- matrix(rep(0, nbCluster*pbDimension), nrow=nbCluster,
  +           ncol=pbDimension);
  +     for(k in 1:nbCluster){
  +         for(p in 1:pbDimension){
  +               means[k,p] = k;
  +         }
  +     }   
  +     variances <- list(c(1:nbCluster))
  +     for(k in 1:nbCluster){
  +         variances[[k]] <- diag(pbDimension)
  +     }   
  +     par <- new("GaussianParameter",proportions=proportions,
  +     mean=means, variance=variances);
  +     return(par);
  + }
  > 
  > data(iris)
  > data <- iris[,1:4]
  > 
  > param <- createGaussianParameter(3,4);
  > 
  > strategy <- mixmodStrategy(initMethod="parameter", parameter=param)
  > out <- mixmodCluster(data=data, nbCluster=3 , strategy=strategy);
  Error in xtfrm.data.frame(x) : cannot xtfrm data frames
  Calls: mixmodCluster ... lapply -> FUN -> as.vector -> xtfrm -> xtfrm.data.frame
  Execution halted
Running the tests in ‘tests/clusteringPartitiontest.R’ failed.
Complete output:
  > library(Rmixmod);
  Loading required package: Rcpp
  Rmixmod v. 2.1.6 / URI: www.mixmod.org
  > data(iris)
  > data <- iris[,1:4]
  > 
  > labels <- c(rep(1,40),rep(2,70),rep(3,40))
  > 
  > #strategy = new("Strategy", initMethod="partition", labels=labels)
  > strategy = mixmodStrategy(initMethod="label", labels=labels)
  > 
  > out <- mixmodCluster(data, nbCluster=3, strategy=strategy);
  Error in xtfrm.data.frame(x) : cannot xtfrm data frames
  Calls: mixmodCluster ... lapply -> FUN -> as.vector -> xtfrm -> xtfrm.data.frame
  Execution halted
Running the tests in ‘tests/heterogeneousclusteringtest.R’ failed.
Complete output:
  > library(Rmixmod)
  Loading required package: Rcpp
  Rmixmod v. 2.1.6 / URI: www.mixmod.org
  > data(heterodata)
  > out<-mixmodCluster(heterodata[-1],2)
  Error in xtfrm.data.frame(x) : cannot xtfrm data frames
  Calls: mixmodCluster ... lapply -> FUN -> as.vector -> xtfrm -> xtfrm.data.frame
  Execution halted
Running the tests in ‘tests/ssupervised.R’ failed.
Complete output:
  > library(Rmixmod);
  Loading required package: Rcpp
  Rmixmod v. 2.1.6 / URI: www.mixmod.org
  > data(iris)
  > data <- iris[,1:4]
  > labels <- ifelse(iris[5]=='virginica',3,ifelse(iris[5]=='versicolor',2,1))
  > rand <-sample(c(rep(1,each=50),rep(0,each=100)))
  > kl <- labels*rand
  > mixmodCluster(data, nbCluster=3, knownLabels=kl);
  Error in xtfrm.data.frame(x) : cannot xtfrm data frames
  Calls: mixmodCluster ... lapply -> FUN -> as.vector -> xtfrm -> xtfrm.data.frame
  Execution halted
Running the tests in ‘tests/test.R’ failed.
Complete output:
  > library(Rmixmod)
  Loading required package: Rcpp
  Rmixmod v. 2.1.6 / URI: 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)
  > 
  > # TODO: some issues with this strategy (because of SEM algorithm ?)
  > 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 = strategy1) #TODO: was strategy2
  Error in xtfrm.data.frame(x) : cannot xtfrm data frames
  Calls: mixmodCluster ... lapply -> FUN -> as.vector -> xtfrm -> xtfrm.data.frame
  Execution halted