Running ‘testthat.R’ [123s/133s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(dimRed)
  Loading required package: DRR
  Loading required package: kernlab
  Loading required package: CVST
  Loading required package: Matrix
  
  Attaching package: 'dimRed'
  
  The following object is masked from 'package:stats':
  
      embed
  
  The following object is masked from 'package:base':
  
      as.data.frame
  
  > 
  > test_check("dimRed")
  1. Error: high level functions working? (@test_all.R#18) -----------------------
  require 'diffusionMap' package, install it using install.packages('diffusionMap')
  1: suppressWarnings(embedded_data[[e]] <- embed(scurve, e, .mute = c("message", "output"))) at testthat/test_all.R:18
  2: withCallingHandlers(expr, warning = function(w) invokeRestart("muffleWarning"))
  3: embed(scurve, e, .mute = c("message", "output"))
  4: embed(scurve, e, .mute = c("message", "output"))
  5: .local(.data, ...)
  6: do.call(methodObject@fun, args)
  7: (function (data, pars, keep.org.data = TRUE) 
     {
         chckpkg("diffusionMap")
         meta <- data@meta
         orgdata <- if (keep.org.data) 
             data@data
         else NULL
         indata <- data@data
         distmat <- pars$d(indata)
         if (pars$eps == "auto") 
             pars$eps <- diffusionMap::epsilonCompute(distmat)
         diffres <- diffusionMap::diffuse(D = distmat, t = pars$t, eps.val = pars$eps, 
             neigen = pars$ndim, maxdim = pars$ndim, delta = pars$delta)
         outdata <- diffres$X
         appl <- function(x) {
             appl.meta <- if (inherits(x, "dimRedData")) 
                 x@meta
             else data.frame()
             proj <- if (inherits(x, "dimRedData")) 
                 x@data
             else x
             if (ncol(proj) != ncol(data@data)) 
                 stop("x must have the same number of dimensions ", "as the original data")
             dd <- sqrt(pdist2(proj, indata))
             appl.res <- diffusionMap::nystrom(diffres, dd, sigma = diffres$epsilon)
             dimnames(appl.res) <- list(rownames(x), paste0("diffMap", seq_len(ncol(outdata))))
             return(appl.res)
         }
         colnames(outdata) <- paste0("diffMap", seq_len(ncol(outdata)))
         return(new("dimRedResult", data = new("dimRedData", data = outdata, meta = meta), 
             org.data = orgdata, apply = appl, has.apply = TRUE, has.org.data = keep.org.data, 
             method = "diffmap", pars = pars))
     })(data = <S4 object of class structure("dimRedData", package = "dimRed")>, keep.org.data = TRUE, 
         pars = structure(list(d = function (x, method = "euclidean", diag = FALSE, upper = FALSE, 
             p = 2) 
         {
             if (!is.na(pmatch(method, "euclidian"))) 
                 method <- "euclidean"
             METHODS <- c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski")
             method <- pmatch(method, METHODS)
             if (is.na(method)) 
                 stop("invalid distance method")
             if (method == -1) 
                 stop("ambiguous distance method")
             x <- as.matrix(x)
             N <- nrow(x)
             attrs <- if (method == 6L) 
                 list(Size = N, Labels = dimnames(x)[[1L]], Diag = diag, Upper = upper, 
                     method = METHODS[method], p = p, call = match.call(), class = "dist")
             else list(Size = N, Labels = dimnames(x)[[1L]], Diag = diag, Upper = upper, 
                 method = METHODS[method], call = match.call(), class = "dist")
             .Call(C_Cdist, x, method, attrs, p)
         }, ndim = 2, eps = "auto", t = 0, delta = 1e-05), .Names = c("d", "ndim", "eps", 
         "t", "delta")))
  8: chckpkg("diffusionMap")
  9: stop(paste0("require '", pkg, "' package, install it using install.packages('", pkg, 
         "')"))
  
  predictors:  PC1 PC2 dependent:  PC3 
  sigma=0.001 kernel=rbfdot lambda=0 nblocks=4 ( 565.8693 )
  sigma=0.01 kernel=rbfdot lambda=0 nblocks=4 ( 20895.76 )
  sigma=0.1 kernel=rbfdot lambda=0 nblocks=4 ( 5309736 )
  sigma=1 kernel=rbfdot lambda=0 nblocks=4 ( 11319.29 )
  sigma=10 kernel=rbfdot lambda=0 nblocks=4 ( 0.2339086 )
  sigma=100 kernel=rbfdot lambda=0 nblocks=4 ( 0.2988236 )
  sigma=1000 kernel=rbfdot lambda=0 nblocks=4 ( 0.4544619 )
  sigma=10000 kernel=rbfdot lambda=0 nblocks=4 ( 0.4903384 )
  sigma=0.001 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.5017752 )
  sigma=0.01 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.5064654 )
  sigma=0.1 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.5344733 )
  sigma=1 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.167943 )
  sigma=10 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.1273338 )
  sigma=100 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.2951492 )
  sigma=1000 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.4546157 )
  sigma=10000 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.4903479 )
  sigma=0.001 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.5012588 )
  sigma=0.01 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.5069183 )
  sigma=0.1 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.5144523 )
  sigma=1 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.1862135 )
  sigma=10 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.1073758 )
  sigma=100 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.2979379 )
  sigma=1000 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.454796 )
  sigma=10000 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.4903775 )
  sigma=0.001 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.5003823 )
  sigma=0.01 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.502637 )
  sigma=0.1 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.5119597 )
  sigma=1 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.2409232 )
  sigma=10 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.1132138 )
  sigma=100 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.3086203 )
  sigma=1000 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.4580334 )
  sigma=10000 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.4907056 )
  sigma=0.001 kernel=rbfdot lambda=1 nblocks=4 ( 0.4981432 )
  sigma=0.01 kernel=rbfdot lambda=1 nblocks=4 ( 0.5000305 )
  sigma=0.1 kernel=rbfdot lambda=1 nblocks=4 ( 0.507911 )
  sigma=1 kernel=rbfdot lambda=1 nblocks=4 ( 0.3868163 )
  sigma=10 kernel=rbfdot lambda=1 nblocks=4 ( 0.2234607 )
  sigma=100 kernel=rbfdot lambda=1 nblocks=4 ( 0.376793 )
  sigma=1000 kernel=rbfdot lambda=1 nblocks=4 ( 0.4733848 )
  sigma=10000 kernel=rbfdot lambda=1 nblocks=4 ( 0.4924069 )
  sigma=0.001 kernel=rbfdot lambda=10 nblocks=4 ( 0.4970285 )
  sigma=0.01 kernel=rbfdot lambda=10 nblocks=4 ( 0.4976835 )
  sigma=0.1 kernel=rbfdot lambda=10 nblocks=4 ( 0.5001475 )
  sigma=1 kernel=rbfdot lambda=10 nblocks=4 ( 0.476975 )
  sigma=10 kernel=rbfdot lambda=10 nblocks=4 ( 0.4198245 )
  sigma=100 kernel=rbfdot lambda=10 nblocks=4 ( 0.4690047 )
  sigma=1000 kernel=rbfdot lambda=10 nblocks=4 ( 0.4905632 )
  sigma=10000 kernel=rbfdot lambda=10 nblocks=4 ( 0.4942438 )
  sigma=0.001 kernel=rbfdot lambda=100 nblocks=4 ( 0.4958531 )
  sigma=0.01 kernel=rbfdot lambda=100 nblocks=4 ( 0.4958847 )
  sigma=0.1 kernel=rbfdot lambda=100 nblocks=4 ( 0.4959895 )
  sigma=1 kernel=rbfdot lambda=100 nblocks=4 ( 0.4928096 )
  sigma=10 kernel=rbfdot lambda=100 nblocks=4 ( 0.4855224 )
  sigma=100 kernel=rbfdot lambda=100 nblocks=4 ( 0.4917517 )
  sigma=1000 kernel=rbfdot lambda=100 nblocks=4 ( 0.4942167 )
  sigma=10000 kernel=rbfdot lambda=100 nblocks=4 ( 0.494623 )
  predictors:  PC1 dependent:  PC2 
  sigma=0.001 kernel=rbfdot lambda=0 nblocks=4 ( 1104.079 )
  sigma=0.01 kernel=rbfdot lambda=0 nblocks=4 ( 55180.29 )
  sigma=0.1 kernel=rbfdot lambda=0 nblocks=4 ( 36469.65 )
  sigma=1 kernel=rbfdot lambda=0 nblocks=4 ( 7962520827 )
  sigma=10 kernel=rbfdot lambda=0 nblocks=4 ( 3.2648e+12 )
  sigma=100 kernel=rbfdot lambda=0 nblocks=4 ( 5.591089e+13 )
  sigma=1000 kernel=rbfdot lambda=0 nblocks=4 ( 207247.4 )
  sigma=10000 kernel=rbfdot lambda=0 nblocks=4 ( 1078.095 )
  sigma=0.001 kernel=rbfdot lambda=0.001 nblocks=4 ( 2.013791 )
  sigma=0.01 kernel=rbfdot lambda=0.001 nblocks=4 ( 2.012317 )
  sigma=0.1 kernel=rbfdot lambda=0.001 nblocks=4 ( 2.020947 )
  sigma=1 kernel=rbfdot lambda=0.001 nblocks=4 ( 1.963204 )
  sigma=10 kernel=rbfdot lambda=0.001 nblocks=4 ( 2.031079 )
  sigma=100 kernel=rbfdot lambda=0.001 nblocks=4 ( 3.994655 )
  sigma=1000 kernel=rbfdot lambda=0.001 nblocks=4 ( 3.029213 )
  sigma=10000 kernel=rbfdot lambda=0.001 nblocks=4 ( 2.498812 )
  sigma=0.001 kernel=rbfdot lambda=0.01 nblocks=4 ( 2.009901 )
  sigma=0.01 kernel=rbfdot lambda=0.01 nblocks=4 ( 2.011834 )
  sigma=0.1 kernel=rbfdot lambda=0.01 nblocks=4 ( 2.023041 )
  sigma=1 kernel=rbfdot lambda=0.01 nblocks=4 ( 1.986458 )
  sigma=10 kernel=rbfdot lambda=0.01 nblocks=4 ( 1.934931 )
  sigma=100 kernel=rbfdot lambda=0.01 nblocks=4 ( 2.367779 )
  sigma=1000 kernel=rbfdot lambda=0.01 nblocks=4 ( 2.376345 )
  sigma=10000 kernel=rbfdot lambda=0.01 nblocks=4 ( 2.093853 )
  sigma=0.001 kernel=rbfdot lambda=0.1 nblocks=4 ( 2.009493 )
  sigma=0.01 kernel=rbfdot lambda=0.1 nblocks=4 ( 2.011713 )
  sigma=0.1 kernel=rbfdot lambda=0.1 nblocks=4 ( 2.014242 )
  sigma=1 kernel=rbfdot lambda=0.1 nblocks=4 ( 1.980285 )
  sigma=10 kernel=rbfdot lambda=0.1 nblocks=4 ( 1.865564 )
  sigma=100 kernel=rbfdot lambda=0.1 nblocks=4 ( 1.977874 )
  sigma=1000 kernel=rbfdot lambda=0.1 nblocks=4 ( 1.921394 )
  sigma=10000 kernel=rbfdot lambda=0.1 nblocks=4 ( 1.978143 )
  sigma=0.001 kernel=rbfdot lambda=1 nblocks=4 ( 2.006086 )
  sigma=0.01 kernel=rbfdot lambda=1 nblocks=4 ( 2.009929 )
  sigma=0.1 kernel=rbfdot lambda=1 nblocks=4 ( 2.011043 )
  sigma=1 kernel=rbfdot lambda=1 nblocks=4 ( 1.979492 )
  sigma=10 kernel=rbfdot lambda=1 nblocks=4 ( 1.857816 )
  sigma=100 kernel=rbfdot lambda=1 nblocks=4 ( 1.82952 )
  sigma=1000 kernel=rbfdot lambda=1 nblocks=4 ( 1.876356 )
  sigma=10000 kernel=rbfdot lambda=1 nblocks=4 ( 1.936347 )
  sigma=0.001 kernel=rbfdot lambda=10 nblocks=4 ( 2.004256 )
  sigma=0.01 kernel=rbfdot lambda=10 nblocks=4 ( 2.006015 )
  sigma=0.1 kernel=rbfdot lambda=10 nblocks=4 ( 2.008906 )
  sigma=1 kernel=rbfdot lambda=10 nblocks=4 ( 1.996846 )
  sigma=10 kernel=rbfdot lambda=10 nblocks=4 ( 1.94026 )
  sigma=100 kernel=rbfdot lambda=10 nblocks=4 ( 1.902143 )
  sigma=1000 kernel=rbfdot lambda=10 nblocks=4 ( 1.949081 )
  sigma=10000 kernel=rbfdot lambda=10 nblocks=4 ( 1.980828 )
  sigma=0.001 kernel=rbfdot lambda=100 nblocks=4 ( 2.001802 )
  sigma=0.01 kernel=rbfdot lambda=100 nblocks=4 ( 2.001967 )
  sigma=0.1 kernel=rbfdot lambda=100 nblocks=4 ( 2.002927 )
  sigma=1 kernel=rbfdot lambda=100 nblocks=4 ( 2.000535 )
  sigma=10 kernel=rbfdot lambda=100 nblocks=4 ( 1.989808 )
  sigma=100 kernel=rbfdot lambda=100 nblocks=4 ( 1.984006 )
  sigma=1000 kernel=rbfdot lambda=100 nblocks=4 ( 1.992428 )
  sigma=10000 kernel=rbfdot lambda=100 nblocks=4 ( 1.996993 )
  2. Error: check vs vegan isomap (@test_isomap.R#17) ----------------------------
  there is no package called 'igraph'
  1: embed(a, "Isomap", knn = 8, ndim = 2) at testthat/test_isomap.R:17
  2: embed(a, "Isomap", knn = 8, ndim = 2)
  3: .local(.data, ...)
  4: do.call(methodObject@fun, args)
  5: (function (data, pars, keep.org.data = TRUE) 
     {
         message(Sys.time(), ": Isomap START")
         meta <- data@meta
         orgdata <- if (keep.org.data) 
             data@data
         else NULL
         indata <- data@data
         if (is.null(pars$eps)) 
             pars$eps <- 0
         message(Sys.time(), ": constructing knn graph")
         knng <- makeKNNgraph(x = indata, k = pars$knn, eps = pars$eps)
         message(Sys.time(), ": calculating geodesic distances")
         geodist <- igraph::distances(knng, algorithm = "dijkstra")
         message(Sys.time(), ": cmdscale")
         cmdout <- stats::cmdscale(geodist, k = pars$ndim, eig = TRUE)
         message(Sys.time(), ": post processing")
         neig <- sum(cmdout$eig > 0)
         if (neig < pars$ndim) {
             warning("Isomap: eigenvalues < 0, returning less dimensions!")
             cmdout$points <- cmdout$points[, seq_len(neig), drop = FALSE]
             cmdout$eig <- cmdout$eig[seq_len(neig)]
         }
         else {
             cmdout$eig <- cmdout$eig[seq_len(pars$ndim)]
         }
         colnames(cmdout$points) <- paste0("iso", seq_len(ncol(cmdout$points)))
         appl <- function(x) {
             message(Sys.time(), ": L-Isomap embed START")
             appl.meta <- if (inherits(x, "dimRedData")) 
                 x@meta
             else data.frame()
             indata <- if (inherits(x, "dimRedData")) 
                 x@data
             else x
             if (ncol(indata) != ncol(data@data)) 
                 stop("x must have the same number of dimensions as the original data")
             nindata <- nrow(indata)
             norg <- nrow(orgdata)
             message(Sys.time(), ": constructing knn graph")
             lknng <- makeKNNgraph(rbind(indata, orgdata), k = pars$knn, eps = pars$eps)
             message(Sys.time(), ": calculating geodesic distances")
             lgeodist <- igraph::distances(lknng, seq_len(nindata), nindata + seq_len(norg))
             message(Sys.time(), ": embedding")
             dammu <- sweep(lgeodist^2, 2, colMeans(geodist^2), "-")
             Lsharp <- sweep(cmdout$points, 2, cmdout$eig, "/")
             out <- -0.5 * (dammu %*% Lsharp)
             message(Sys.time(), ": DONE")
             return(new("dimRedData", data = out, meta = appl.meta))
         }
         return(new("dimRedResult", data = new("dimRedData", data = cmdout$points, meta = meta), 
             org.data = orgdata, has.org.data = keep.org.data, apply = appl, has.apply = TRUE, 
             method = "isomap", pars = pars))
     })(data = <S4 object of class structure("dimRedData", package = "dimRed")>, keep.org.data = TRUE, 
         pars = structure(list(knn = 8, ndim = 2), .Names = c("knn", "ndim")))
  6: makeKNNgraph(x = indata, k = pars$knn, eps = pars$eps)
  7: igraph::make_empty_graph
  8: getExportedValue(pkg, name)
  9: asNamespace(ns)
  10: getNamespace(ns)
  11: tryCatch(loadNamespace(name), error = function(e) stop(e))
  12: tryCatchList(expr, classes, parentenv, handlers)
  13: tryCatchOne(expr, names, parentenv, handlers[[1L]])
  14: value[[3L]](cond)
  
  predictors:  PC1 PC2 PC3 dependent:  PC4 
  sigma=0.001 kernel=rbfdot lambda=0 nblocks=4 ( 273.954 )
  sigma=0.01 kernel=rbfdot lambda=0 nblocks=4 ( 27.43465 )
  sigma=0.1 kernel=rbfdot lambda=0 nblocks=4 ( 4.103242e+29 )
  sigma=1 kernel=rbfdot lambda=0 nblocks=4 ( 0.04173489 )
  sigma=10 kernel=rbfdot lambda=0 nblocks=4 ( 0.02353896 )
  Error in solve(Matrix(K + diag(lambda, N))) : 
    Lapack dgecon(): system computationally singular, reciprocal condition number = 0
  Error in solve(Matrix(K + diag(lambda, N))) : 
    Lapack routine dsytrf returned error code 26
  sigma=100 kernel=rbfdot lambda=0 nblocks=4 ( NA )
  sigma=1000 kernel=rbfdot lambda=0 nblocks=4 ( 0.02367451 )
  Error in LU.dgC(a) : cs_lu(A) failed: near-singular A (or out of memory)
  Error in LU.dgC(a) : cs_lu(A) failed: near-singular A (or out of memory)
  sigma=10000 kernel=rbfdot lambda=0 nblocks=4 ( NA )
  sigma=0.001 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.02743262 )
  sigma=0.01 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.02845059 )
  sigma=0.1 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.03326983 )
  sigma=1 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.03310791 )
  sigma=10 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.02356115 )
  sigma=100 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.02370639 )
  sigma=1000 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.02367448 )
  sigma=10000 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.02367483 )
  sigma=0.001 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.0251192 )
  sigma=0.01 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.02672104 )
  sigma=0.1 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.02639024 )
  sigma=1 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.02517166 )
  sigma=10 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.02357156 )
  sigma=100 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.02370294 )
  sigma=1000 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.0236745 )
  sigma=10000 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.02367484 )
  sigma=0.001 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.02441629 )
  sigma=0.01 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.02551454 )
  sigma=0.1 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.02683658 )
  sigma=1 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.02465014 )
  sigma=10 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.02354049 )
  sigma=100 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.02370057 )
  sigma=1000 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.02367465 )
  sigma=10000 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.02367493 )
  sigma=0.001 kernel=rbfdot lambda=1 nblocks=4 ( 0.02401984 )
  sigma=0.01 kernel=rbfdot lambda=1 nblocks=4 ( 0.02444703 )
  sigma=0.1 kernel=rbfdot lambda=1 nblocks=4 ( 0.02460651 )
  sigma=1 kernel=rbfdot lambda=1 nblocks=4 ( 0.02314811 )
  sigma=10 kernel=rbfdot lambda=1 nblocks=4 ( 0.02333835 )
  sigma=100 kernel=rbfdot lambda=1 nblocks=4 ( 0.02368268 )
  sigma=1000 kernel=rbfdot lambda=1 nblocks=4 ( 0.02367529 )
  sigma=10000 kernel=rbfdot lambda=1 nblocks=4 ( 0.02367546 )
  sigma=0.001 kernel=rbfdot lambda=10 nblocks=4 ( 0.02389043 )
  sigma=0.01 kernel=rbfdot lambda=10 nblocks=4 ( 0.02394256 )
  sigma=0.1 kernel=rbfdot lambda=10 nblocks=4 ( 0.02405596 )
  sigma=1 kernel=rbfdot lambda=10 nblocks=4 ( 0.02346617 )
  sigma=10 kernel=rbfdot lambda=10 nblocks=4 ( 0.02357006 )
  sigma=100 kernel=rbfdot lambda=10 nblocks=4 ( 0.02367645 )
  sigma=1000 kernel=rbfdot lambda=10 nblocks=4 ( 0.02367602 )
  sigma=10000 kernel=rbfdot lambda=10 nblocks=4 ( 0.02367605 )
  sigma=0.001 kernel=rbfdot lambda=100 nblocks=4 ( 0.02373591 )
  sigma=0.01 kernel=rbfdot lambda=100 nblocks=4 ( 0.02373923 )
  sigma=0.1 kernel=rbfdot lambda=100 nblocks=4 ( 0.02374323 )
  sigma=1 kernel=rbfdot lambda=100 nblocks=4 ( 0.02364668 )
  sigma=10 kernel=rbfdot lambda=100 nblocks=4 ( 0.02366279 )
  sigma=100 kernel=rbfdot lambda=100 nblocks=4 ( 0.0236762 )
  sigma=1000 kernel=rbfdot lambda=100 nblocks=4 ( 0.02367617 )
  sigma=10000 kernel=rbfdot lambda=100 nblocks=4 ( 0.02367618 )
  predictors:  PC1 PC2 dependent:  PC3 
  sigma=0.001 kernel=rbfdot lambda=0 nblocks=4 ( 308120352 )
  sigma=0.01 kernel=rbfdot lambda=0 nblocks=4 ( 134027.6 )
  sigma=0.1 kernel=rbfdot lambda=0 nblocks=4 ( 404429.7 )
  sigma=1 kernel=rbfdot lambda=0 nblocks=4 ( 5.373782e+23 )
  sigma=10 kernel=rbfdot lambda=0 nblocks=4 ( 0.2365149 )
  Error in solve(Matrix(K + diag(lambda, N))) : 
    Lapack dgecon(): system computationally singular, reciprocal condition number = 2.59276e-17
  sigma=100 kernel=rbfdot lambda=0 nblocks=4 ( NA )
  Error in .local(a, b, ...) : 
    cs_lu(A) failed: near-singular A (or out of memory)
  sigma=1000 kernel=rbfdot lambda=0 nblocks=4 ( NA )
  Error in LU.dgC(a) : cs_lu(A) failed: near-singular A (or out of memory)
  Error in LU.dgC(a) : cs_lu(A) failed: near-singular A (or out of memory)
  sigma=10000 kernel=rbfdot lambda=0 nblocks=4 ( NA )
  sigma=0.001 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.08873512 )
  sigma=0.01 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.0800424 )
  sigma=0.1 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.09269512 )
  sigma=1 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.1381545 )
  sigma=10 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.08883935 )
  sigma=100 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.07373225 )
  sigma=1000 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.07504299 )
  sigma=10000 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.07548306 )
  sigma=0.001 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.08553901 )
  sigma=0.01 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.08853537 )
  sigma=0.1 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.07481014 )
  sigma=1 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.09257936 )
  sigma=10 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.07924333 )
  sigma=100 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.07367066 )
  sigma=1000 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.07506132 )
  sigma=10000 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.07550018 )
  sigma=0.001 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.08314728 )
  sigma=0.01 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.08624175 )
  sigma=0.1 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.07987849 )
  sigma=1 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.07975153 )
  sigma=10 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.07591839 )
  sigma=100 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.07397001 )
  sigma=1000 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.07524738 )
  sigma=10000 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.07565807 )
  sigma=0.001 kernel=rbfdot lambda=1 nblocks=4 ( 0.08196305 )
  sigma=0.01 kernel=rbfdot lambda=1 nblocks=4 ( 0.08323541 )
  sigma=0.1 kernel=rbfdot lambda=1 nblocks=4 ( 0.08298814 )
  sigma=1 kernel=rbfdot lambda=1 nblocks=4 ( 0.07530584 )
  sigma=10 kernel=rbfdot lambda=1 nblocks=4 ( 0.07337727 )
  sigma=100 kernel=rbfdot lambda=1 nblocks=4 ( 0.07540772 )
  sigma=1000 kernel=rbfdot lambda=1 nblocks=4 ( 0.07626578 )
  sigma=10000 kernel=rbfdot lambda=1 nblocks=4 ( 0.07651293 )
  sigma=0.001 kernel=rbfdot lambda=10 nblocks=4 ( 0.08073818 )
  sigma=0.01 kernel=rbfdot lambda=10 nblocks=4 ( 0.08128202 )
  sigma=0.1 kernel=rbfdot lambda=10 nblocks=4 ( 0.0818451 )
  sigma=1 kernel=rbfdot lambda=10 nblocks=4 ( 0.0777247 )
  sigma=10 kernel=rbfdot lambda=10 nblocks=4 ( 0.07654825 )
  sigma=100 kernel=rbfdot lambda=10 nblocks=4 ( 0.07722627 )
  sigma=1000 kernel=rbfdot lambda=10 nblocks=4 ( 0.07741497 )
  sigma=10000 kernel=rbfdot lambda=10 nblocks=4 ( 0.07746377 )
  sigma=0.001 kernel=rbfdot lambda=100 nblocks=4 ( 0.0785548 )
  sigma=0.01 kernel=rbfdot lambda=100 nblocks=4 ( 0.0785795 )
  sigma=0.1 kernel=rbfdot lambda=100 nblocks=4 ( 0.0785702 )
  sigma=1 kernel=rbfdot lambda=100 nblocks=4 ( 0.07781369 )
  sigma=10 kernel=rbfdot lambda=100 nblocks=4 ( 0.0775506 )
  sigma=100 kernel=rbfdot lambda=100 nblocks=4 ( 0.07763633 )
  sigma=1000 kernel=rbfdot lambda=100 nblocks=4 ( 0.07765804 )
  sigma=10000 kernel=rbfdot lambda=100 nblocks=4 ( 0.07766344 )
  predictors:  PC1 dependent:  PC2 
  sigma=0.001 kernel=rbfdot lambda=0 nblocks=4 ( 134604 )
  sigma=0.01 kernel=rbfdot lambda=0 nblocks=4 ( 15960.87 )
  sigma=0.1 kernel=rbfdot lambda=0 nblocks=4 ( 8969914741 )
  sigma=1 kernel=rbfdot lambda=0 nblocks=4 ( 95079270831 )
  sigma=10 kernel=rbfdot lambda=0 nblocks=4 ( 30241033620 )
  sigma=100 kernel=rbfdot lambda=0 nblocks=4 ( 1.17592 )
  Error in .local(a, b, ...) : 
    cs_lu(A) failed: near-singular A (or out of memory)
  sigma=1000 kernel=rbfdot lambda=0 nblocks=4 ( NA )
  Error in LU.dgC(a) : cs_lu(A) failed: near-singular A (or out of memory)
  Error in .local(a, b, ...) : 
    cs_lu(A) failed: near-singular A (or out of memory)
  sigma=10000 kernel=rbfdot lambda=0 nblocks=4 ( NA )
  sigma=0.001 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.2478375 )
  sigma=0.01 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.1902558 )
  sigma=0.1 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.1692638 )
  sigma=1 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.3418196 )
  sigma=10 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.3060716 )
  sigma=100 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.2929054 )
  sigma=1000 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.2229361 )
  sigma=10000 kernel=rbfdot lambda=0.001 nblocks=4 ( 0.2232804 )
  sigma=0.001 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.281593 )
  sigma=0.01 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.1867514 )
  sigma=0.1 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.1738676 )
  sigma=1 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.1330691 )
  sigma=10 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.2106499 )
  sigma=100 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.2381649 )
  sigma=1000 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.2209173 )
  sigma=10000 kernel=rbfdot lambda=0.01 nblocks=4 ( 0.2237982 )
  sigma=0.001 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.2788309 )
  sigma=0.01 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.2306335 )
  sigma=0.1 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.1864453 )
  sigma=1 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.1353952 )
  sigma=10 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.1564832 )
  sigma=100 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.1915038 )
  sigma=1000 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.2222627 )
  sigma=10000 kernel=rbfdot lambda=0.1 nblocks=4 ( 0.2253416 )
  sigma=0.001 kernel=rbfdot lambda=1 nblocks=4 ( 0.2616173 )
  sigma=0.01 kernel=rbfdot lambda=1 nblocks=4 ( 0.268142 )
  sigma=0.1 kernel=rbfdot lambda=1 nblocks=4 ( 0.1889565 )
  sigma=1 kernel=rbfdot lambda=1 nblocks=4 ( 0.1622785 )
  sigma=10 kernel=rbfdot lambda=1 nblocks=4 ( 0.1695001 )
  sigma=100 kernel=rbfdot lambda=1 nblocks=4 ( 0.2039082 )
  sigma=1000 kernel=rbfdot lambda=1 nblocks=4 ( 0.2227087 )
  sigma=10000 kernel=rbfdot lambda=1 nblocks=4 ( 0.2315596 )
  sigma=0.001 kernel=rbfdot lambda=10 nblocks=4 ( 0.253423 )
  sigma=0.01 kernel=rbfdot lambda=10 nblocks=4 ( 0.2562445 )
  sigma=0.1 kernel=rbfdot lambda=10 nblocks=4 ( 0.2325686 )
  sigma=1 kernel=rbfdot lambda=10 nblocks=4 ( 0.2076748 )
  sigma=10 kernel=rbfdot lambda=10 nblocks=4 ( 0.2179739 )
  sigma=100 kernel=rbfdot lambda=10 nblocks=4 ( 0.2295869 )
  sigma=1000 kernel=rbfdot lambda=10 nblocks=4 ( 0.2364778 )
  sigma=10000 kernel=rbfdot lambda=10 nblocks=4 ( 0.2390248 )
  sigma=0.001 kernel=rbfdot lambda=100 nblocks=4 ( 0.2445296 )
  sigma=0.01 kernel=rbfdot lambda=100 nblocks=4 ( 0.2446051 )
  sigma=0.1 kernel=rbfdot lambda=100 nblocks=4 ( 0.2409523 )
  sigma=1 kernel=rbfdot lambda=100 nblocks=4 ( 0.2356689 )
  sigma=10 kernel=rbfdot lambda=100 nblocks=4 ( 0.2379864 )
  sigma=100 kernel=rbfdot lambda=100 nblocks=4 ( 0.239644 )
  sigma=1000 kernel=rbfdot lambda=100 nblocks=4 ( 0.2405234 )
  sigma=10000 kernel=rbfdot lambda=100 nblocks=4 ( 0.2408276 )
  testthat results ================================================================
  OK: 112 SKIPPED: 0 FAILED: 2
  1. Error: high level functions working? (@test_all.R#18) 
  2. Error: check vs vegan isomap (@test_isomap.R#17) 
  
  Error: testthat unit tests failed
  Execution halted