r - Runif generating NaN terms -

i have matrix of baseline values different parameters, 1 column x 305 row matrix, , want use runif generate matrix of random numbers (100 each parameter value) between +- 10% of initial parameter value. problem there nan terms randomly interspersed in output when there shouldn't be, , warning message:

warning message: in runif(n = 30500, min = lower, max = upper) : nas produced 

this code far:

baseline <- scan("baseline.txt") baseline <- as.matrix(baseline) base <- t(baseline) upper <- 1.1 * base lower <- 0.9 * base matrice2 <- matrix(runif(n=30500, min = lower, max = upper), nrow=305)  

(i had take t(baseline) resulting matrice2 in right form passed function i'm using later). numbers in baseline range -0.281 800.

anyone have ideas why generating nan? using exact code before took time off holidays, , working then.

edit: baseline.txt data, 305 row x 1 column text file

baseline                 [,1]   [1,]   0.400000000   [2,]   0.800000000   [3,]   0.200000000   [4,]   0.800000000   [5,]   0.600000000   [6,]   0.800000000   [7,]   0.150000000   [8,]   0.900000000   [9,]   0.500000000  [10,]   0.600000000  [11,]   0.500000000  [12,]   0.500000000  [13,]   0.600000000  [14,]   0.500000000  [15,]   0.500000000  [16,]   0.600000000  [17,]   0.200000000  [18,]   0.700000000  [19,]   0.800000000  [20,]   0.300000000  [21,]   0.700000000  [22,]   0.800000000  [23,]   0.300000000  [24,]   0.700000000  [25,]   0.800000000  [26,]   0.200000000  [27,]   0.002000000  [28,]   0.400000000  [29,]   0.001000000  [30,]   0.001300000  [31,]   0.800000000  [32,]   0.526000000  [33,]   0.526000000  [34,]   0.714000000  [35,]   0.306000000  [36,]   0.306000000  [37,]   0.711000000  [38,]   0.711000000  [39,]   0.847000000  [40,]   0.300000000  [41,]   0.300000000  [42,]   0.300000000  [43,]   0.300000000  [44,]   0.300000000  [45,]   0.300000000  [46,]   0.050000000  [47,]   0.400000000  [48,]   0.450000000  [49,]   0.300000000  [50,]   0.400000000  [51,]   0.700000000  [52,]   0.400000000  [53,]   0.600000000  [54,]   0.306000000  [55,]   0.885000000  [56,]   0.008610000  [57,]   0.008610000  [58,]   0.008610000  [59,]   0.008610000  [60,]   0.016500000  [61,]   6.450000000  [62,]   6.450000000  [63,]   6.450000000  [64,]   6.450000000  [65,]   0.014650000  [66,]   0.014650000  [67,]   0.014650000  [68,]   0.014650000  [69,]   0.200000000  [70,]   0.400000000  [71,]   0.400000000  [72,]   3.000000000  [73,]   3.000000000  [74,]   3.000000000  [75,]   3.000000000  [76,]   3.000000000  [77,]   3.000000000  [78,]   3.000000000  [79,]   3.000000000  [80,]   3.000000000  [81,]   0.338000000  [82,]   0.013200000  [83,]   0.001350000  [84,]   0.001350000  [85,]   0.001350000  [86,]   0.338000000  [87,]  34.694000000  [88,]   0.002710000  [89,]   0.000420000  [90,]   0.015900000  [91,]   0.037000000  [92,]   0.024500000  [93,]   0.075000000  [94,]   0.045000000  [95,]   0.024800000  [96,]   0.015900000  [97,]   0.045000000  [98,]   0.045000000  [99,]   2.800000000 [100,]   0.001590000 [101,]   0.004500000 [102,]   0.001000000 [103,]   0.020000000 [104,]   0.004800000 [105,]   7.500000000 [106,]  34.694000000 [107,]   0.338000000 [108,]   0.028900000 [109,]   0.022400000 [110,]   0.022400000 [111,]   0.040000000 [112,]   0.008000000 [113,]   0.338000000 [114,]   0.026400000 [115,]   0.046080000 [116,]   0.057600000 [117,]   0.700000000 [118,]   0.400000000 [119,]   0.200000000 [120,]   0.700000000 [121,]   0.300000000 [122,]   0.750000000 [123,]   4.000000000 [124,]   0.500000000 [125,]   8.000000000 [126,] 800.000000000 [127,]   3.000000000 [128,]   3.000000000 [129,]   6.480000000 [130,]   1.150000000 [131,]   0.700000000 [132,]   0.100000000 [133,]   1.793000000 [134,]   1.291000000 [135,]   1.793000000 [136,]   1.291000000 [137,]   1.793000000 [138,]   1.291000000 [139,]   5.944000000 [140,]   4.877000000 [141,]   0.000210000 [142,]   0.006700000 [143,]   2.000000000 [144,]   0.080000000 [145,]   0.110000000 [146,]   0.008500000 [147,] 120.000000000 [148,]  60.000000000 [149,]   3.200000000 [150,]   0.800000000 [151,]   1.300000000 [152,]   1.300000000 [153,]   1.300000000 [154,]   2.690000000 [155,]   0.024500000 [156,]   7.850000000 [157,]   1.500000000 [158,]  10.000000000 [159,]  10.000000000 [160,]   4.300000000 [161,]   0.050000000 [162,]   0.400000000 [163,]   7.860000000 [164,]   3.000000000 [165,]   0.052800000 [166,]   0.052800000 [167,]  15.439000000 [168,]   7.860000000 [169,]  15.439000000 [170,]   0.200000000 [171,]   0.053000000 [172,]   0.022000000 [173,]   0.164600000 [174,]   0.164600000 [175,]   0.164600000 [176,]   0.164600000 [177,]   4.100000000 [178,]  15.439000000 [179,]   7.860000000 [180,]   0.160000000 [181,]   0.040000000 [182,]   0.014400000 [183,]   7.860000000 [184,]   6.000000000 [185,]   0.300000000 [186,]   0.300000000 [187,]  33.500000000 [188,]   0.020000000 [189,]   1.277000000 [190,]   0.981700000 [191,]   1.000000000 [192,]   1.000000000 [193,]   1.000000000 [194,]   0.016666667 [195,]   0.271000000 [196,]   0.504000000 [197,]   0.504000000 [198,]   0.504000000 [199,]   0.713000000 [200,]   0.742000000 [201,]   0.713000000 [202,]   0.981700000 [203,]   0.058800000 [204,]   1.257000000 [205,]   1.257000000 [206,]   2.723000000 [207,]   3.969000000 [208,]   2.462000000 [209,]   2.492000000 [210,]   3.000000000 [211,]   0.815000000 [212,]   1.191000000 [213,]   1.191000000 [214,]   1.191000000 [215,]   2.241000000 [216,]   3.969000000 [217,]   2.241000000 [218,]   3.969000000 [219,] 152.000000000 [220,]   0.085000000 [221,]   0.493900000 [222,]   0.231000000 [223,]   0.246000000 [224,]   0.500000000 [225,]   0.011363636 [226,]   0.133000000 [227,]   0.133000000 [228,]   0.133000000 [229,]   0.198000000 [230,]   0.493900000 [231,]   0.198000000 [232,]   0.493900000 [233,]   1.447000000 [234,]   1.970000000 [235,]   0.995000000 [236,]   0.995000000 [237,]   1.000000000 [238,]   0.271000000 [239,]   0.623000000 [240,]   0.623000000 [241,]   0.623000000 [242,]   1.128000000 [243,]   1.970000000 [244,]   1.128000000 [245,]   1.970000000 [246,]   2.256000000 [247,]   2.951000000 [248,]   3.000000000 [249,]   3.000000000 [250,]   3.000000000 [251,]  -0.281000000 [252,]   1.065000000 [253,]   1.065000000 [254,]   1.065000000 [255,]   0.949000000 [256,]   2.951000000 [257,]   0.949000000 [258,]   2.951000000 [259,]   0.000840000 [260,]   0.000120000 [261,]   0.006200000 [262,]   0.006200000 [263,]   0.006200000 [264,]   0.085000000 [265,]   0.030500000 [266,]   0.459000000 [267,]   0.030500000 [268,]   0.459000000 [269,]   0.030500000 [270,]   0.011100000 [271,]   0.001481000 [272,]   0.000210000 [273,]   0.000210000 [274,]   0.000210000 [275,]   0.000210000 [276,] 142.050000000 [277,]   0.468000000 [278,]   0.355000000 [279,]   0.335000000 [280,]   0.013513514 [281,]   0.729000000 [282,]   0.154000000 [283,]   0.154000000 [284,]   0.154000000 [285,]   0.396000000 [286,]   0.396000000 [287,]   0.009100000 [288,]   0.006700000 [289,]   0.006700000 [290,]   0.006700000 [291,]   0.006700000 [292,] 112.500000000 [293,] 112.500000000 [294,] 112.500000000 [295,]   0.009803922 [296,]   0.237000000 [297,]   0.237000000 [298,]   0.237000000 [299,]   0.017900000 [300,]   0.017900000 [301,]   0.000120000 [302,]   0.000120000 [303,]   0.000120000 [304,]   0.006200000 [305,]   0.006200000 

as pointed out in comments, in runif min must less max. method of calculating min , max move max farther 0 , mins closer 0 works when numbers positive.

as suggested in comments, there several ways this, example pmin , pmax:

upper = pmax(0.9 * base, 1.1 * base) lower = pmin(0.9 * base, 1.1 * base) 

another way, more efficient, be:

offset = 0.1 * abs(base) upper = base + offset lower = base - offset