Package check result: ERROR Check: CRAN incoming feasibility, Result: NOTE Maintainer: ‘Zhongli Jiang ’ New maintainer: Zhongli Jiang Old maintainer(s): Zhongli Jiang Check: examples, Result: ERROR Running examples in ‘misspi-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: misspi > ### Title: Missing Value Imputation in Parallel > ### Aliases: misspi > > ### ** Examples > > > ## No test: > # Quick example 1 > # Load a small data > data(iris) > # Keep numerical columns > num.col <- which(sapply(iris, is.numeric)) > iris.numeric <- as.matrix(iris[, num.col]) > set.seed(0) > iris.miss <- missar(iris.numeric, 0.3, 1) > iris.impute <- misspi(iris.miss) Parallel computing using 2 cores ... Imputing a matrix with 150 rows and 4 columns ... Highest missing rate is 0.326666666666667 ... Initializing ... | | | 0% | | * * * * * * | 25% | | * * * * * * * * * * * * | 50% | | * * * * * * * * * * * * * * * * * | 75% | | * * * * * * * * * * * * * * * * * * * * * * * | 100% Iteration 1 ... | | * * * * * * | 25% | | * * * * * * * * * * * * | 50% | | * * * * * * * * * * * * * * * * * | 75% | | * * * * * * * * * * * * * * * * * * * * * * * | 100% Relative squared difference is 0.0108515119590513 ... Iteration 2 ... | | * * * * * * | 25% | | * * * * * * * * * * * * | 50% | | * * * * * * * * * * * * * * * * * | 75% | | * * * * * * * * * * * * * * * * * * * * * * * | 100% Relative squared difference is 0.0110954717794601 ... Early stopping invoked ... > iris.impute $x.imputed Sepal.Length Sepal.Width Petal.Length Petal.Width [1,] 5.100000 3.100000 1.466667 0.2000000 [2,] 4.700000 3.000000 1.400000 0.2000000 [3,] 4.700000 3.200000 1.300000 0.2000000 [4,] 4.600000 3.100000 1.500000 0.2000000 [5,] 5.000000 3.600000 1.400000 0.2000000 [6,] 5.400000 3.900000 1.700000 0.2666667 [7,] 4.600000 3.400000 1.400000 0.2000000 [8,] 5.000000 3.400000 1.500000 0.2000000 [9,] 4.400000 2.900000 3.933333 1.2000000 [10,] 5.066667 3.100000 1.500000 0.1000000 [11,] 5.400000 3.700000 1.500000 0.2000000 [12,] 4.800000 3.400000 1.600000 0.2000000 [13,] 4.800000 3.000000 1.400000 0.1000000 [14,] 4.300000 3.100000 1.100000 0.1000000 [15,] 5.800000 4.000000 1.466667 0.2000000 [16,] 5.700000 2.966667 1.500000 1.4333333 [17,] 5.400000 3.900000 1.566667 0.4000000 [18,] 5.100000 3.500000 1.400000 0.3000000 [19,] 5.700000 3.900000 1.700000 0.3000000 [20,] 5.100000 3.600000 1.500000 0.3000000 [21,] 5.400000 3.400000 1.566667 0.2000000 [22,] 5.100000 3.600000 1.500000 0.4000000 [23,] 4.600000 3.600000 1.000000 0.2000000 [24,] 5.366667 3.300000 1.700000 0.5000000 [25,] 4.800000 3.400000 1.433333 0.2000000 [26,] 4.800000 3.000000 1.600000 0.1333333 [27,] 5.000000 3.400000 1.600000 0.2000000 [28,] 5.200000 3.500000 1.500000 0.2666667 [29,] 5.200000 3.100000 1.400000 0.2666667 [30,] 4.700000 3.200000 1.600000 0.2333333 [31,] 4.800000 3.100000 1.600000 0.2000000 [32,] 5.400000 3.400000 1.500000 0.4000000 [33,] 5.200000 4.100000 1.533333 0.1000000 [34,] 5.500000 4.200000 1.400000 0.2000000 [35,] 4.900000 3.100000 1.466667 0.2000000 [36,] 5.000000 3.100000 1.200000 0.2000000 [37,] 4.900000 3.100000 1.433333 0.2000000 [38,] 4.900000 3.600000 1.400000 0.1000000 [39,] 4.400000 3.100000 1.300000 0.2000000 [40,] 5.100000 3.100000 1.466667 0.2000000 [41,] 5.000000 3.600000 1.300000 0.3000000 [42,] 4.500000 2.300000 2.100000 0.3000000 [43,] 4.900000 3.100000 1.300000 0.2666667 [44,] 5.200000 3.500000 1.533333 0.2666667 [45,] 5.100000 3.766667 1.900000 0.2000000 [46,] 4.800000 3.000000 1.400000 0.1333333 [47,] 5.100000 3.800000 1.600000 0.2000000 [48,] 4.900000 3.100000 1.433333 0.2000000 [49,] 5.300000 3.100000 1.566667 0.2000000 [50,] 5.000000 3.300000 1.400000 0.2000000 [51,] 7.000000 2.633333 4.866667 1.4000000 [52,] 6.400000 3.200000 4.100000 1.5000000 [53,] 6.900000 3.133333 4.900000 1.5000000 [54,] 5.500000 2.300000 3.933333 1.3000000 [55,] 6.500000 2.800000 4.600000 1.7333333 [56,] 5.700000 2.800000 5.466667 1.5000000 [57,] 5.800000 3.300000 4.700000 1.6000000 [58,] 4.900000 2.400000 3.300000 1.4333333 [59,] 6.600000 2.900000 5.866667 1.8333333 [60,] 5.200000 2.700000 3.900000 1.4000000 [61,] 5.000000 2.000000 3.300000 1.4333333 [62,] 5.033333 3.000000 4.200000 1.6666667 [63,] 6.000000 2.200000 4.000000 1.0000000 [64,] 6.900000 3.033333 4.700000 1.7666667 [65,] 5.600000 2.900000 3.600000 1.3000000 [66,] 6.700000 3.100000 4.400000 1.4000000 [67,] 5.600000 2.966667 4.500000 1.5000000 [68,] 5.666667 2.700000 4.100000 1.0000000 [69,] 5.866667 2.200000 4.500000 1.3666667 [70,] 5.600000 2.500000 3.900000 1.1000000 [71,] 5.900000 3.200000 4.800000 1.5000000 [72,] 6.100000 2.800000 4.000000 1.3000000 [73,] 6.300000 2.500000 4.900000 1.6000000 [74,] 6.100000 2.800000 4.700000 1.2000000 [75,] 6.400000 2.866667 5.466667 1.5666667 [76,] 6.600000 3.000000 4.600000 1.4000000 [77,] 6.800000 2.966667 5.966667 1.8333333 [78,] 6.700000 3.000000 5.266667 2.2000000 [79,] 6.900000 2.900000 4.500000 1.5000000 [80,] 5.700000 2.600000 3.500000 1.0000000 [81,] 5.500000 2.400000 3.800000 1.1000000 [82,] 5.500000 2.400000 3.933333 1.2000000 [83,] 5.600000 2.700000 3.900000 1.2000000 [84,] 6.000000 3.166667 4.900000 1.6000000 [85,] 5.400000 3.000000 4.500000 1.5000000 [86,] 6.000000 3.166667 4.900000 1.6000000 [87,] 6.500000 3.100000 4.100000 1.5000000 [88,] 6.300000 2.300000 4.400000 1.3000000 [89,] 5.866667 3.000000 4.100000 1.3000000 [90,] 5.500000 2.500000 4.000000 1.2000000 [91,] 5.500000 2.600000 3.933333 1.2000000 [92,] 6.233333 2.933333 4.600000 1.4000000 [93,] 5.800000 2.600000 4.000000 1.2000000 [94,] 5.000000 2.300000 3.300000 1.0000000 [95,] 5.600000 2.700000 3.800000 1.3000000 [96,] 6.033333 3.000000 4.266667 1.2000000 [97,] 5.866667 2.900000 4.200000 1.3000000 [98,] 5.866667 2.700000 4.300000 1.3000000 [99,] 5.600000 2.500000 4.100000 1.1000000 [100,] 5.700000 2.800000 4.100000 1.6000000 [101,] 6.300000 3.300000 3.966667 2.5000000 [102,] 6.133333 2.700000 5.100000 2.0000000 [103,] 7.100000 2.966667 5.900000 2.2000000 [104,] 6.300000 2.633333 5.466667 1.6000000 [105,] 6.500000 3.133333 5.966667 2.2000000 [106,] 7.600000 3.000000 5.266667 2.1000000 [107,] 4.900000 2.500000 4.500000 1.7000000 [108,] 7.300000 2.900000 6.300000 1.8333333 [109,] 6.700000 2.500000 5.800000 1.8000000 [110,] 7.200000 2.966667 6.100000 2.5000000 [111,] 6.500000 2.966667 5.100000 2.0000000 [112,] 6.400000 2.700000 5.300000 1.9000000 [113,] 6.800000 3.000000 5.500000 2.2000000 [114,] 5.700000 2.500000 5.000000 2.0000000 [115,] 6.933333 2.800000 5.100000 1.8333333 [116,] 6.400000 2.966667 5.300000 2.3000000 [117,] 6.500000 2.733333 5.500000 1.8000000 [118,] 7.700000 3.800000 6.700000 1.9333333 [119,] 7.700000 2.600000 6.900000 2.0000000 [120,] 6.100000 2.200000 5.000000 1.5000000 [121,] 7.033333 2.966667 5.700000 1.8333333 [122,] 5.600000 3.166667 4.900000 1.5000000 [123,] 7.033333 2.800000 6.700000 1.8333333 [124,] 6.300000 2.666667 5.466667 1.8000000 [125,] 6.700000 3.300000 5.466667 2.1000000 [126,] 7.200000 2.966667 6.000000 2.2000000 [127,] 6.333333 3.033333 4.800000 1.8000000 [128,] 6.100000 3.000000 4.900000 1.8000000 [129,] 6.400000 2.966667 5.600000 2.1000000 [130,] 6.600000 2.600000 5.800000 1.6000000 [131,] 7.400000 2.800000 6.100000 1.9000000 [132,] 5.866667 3.800000 5.466667 2.0000000 [133,] 6.400000 2.966667 5.600000 2.2000000 [134,] 6.300000 2.800000 5.100000 1.5000000 [135,] 7.033333 2.966667 5.600000 1.8333333 [136,] 7.700000 3.000000 5.266667 2.3000000 [137,] 6.900000 3.400000 5.600000 2.4000000 [138,] 6.400000 2.733333 5.466667 1.8000000 [139,] 6.000000 3.000000 4.833333 1.8000000 [140,] 7.233333 2.966667 5.400000 1.8333333 [141,] 6.700000 2.966667 6.000000 2.4000000 [142,] 6.900000 3.100000 5.100000 1.7333333 [143,] 6.466667 3.033333 5.100000 1.9000000 [144,] 6.800000 3.200000 5.466667 2.3000000 [145,] 7.033333 2.966667 5.700000 2.5000000 [146,] 6.700000 3.000000 5.200000 2.3000000 [147,] 6.900000 2.500000 5.000000 1.9000000 [148,] 6.500000 3.000000 5.200000 2.0000000 [149,] 5.866667 3.400000 5.466667 2.3000000 [150,] 6.600000 3.000000 4.866667 1.8000000 $time.elapsed Time difference of 35.54483 secs $niter [1] 2 > > # Quick example 2 > # Load a high dimensional data > data(toxicity, package = "misspi") > set.seed(0) > toxicity.miss <- missar(toxicity, 0.4, 0.2) > toxicity.impute <- misspi(toxicity.miss) We highly recommend activate viselect since your data is in high dimension. This may highly improve the speed and imputation accuracy ... Parallel computing using 2 cores ... Imputing a matrix with 171 rows and 1203 columns ... Highest missing rate is 0.491228070175439 ... 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