我有一个不同参数的基线值矩阵，它是1列x 305行矩阵，我想用runif生成一个随机数矩阵（每个参数值100个）+ - 初始参数值的10％。问题是当不应该有输出时随机散布NaN个术语，我收到此警告信息：
Warning message:
In runif(n = 30500, min = lower, max = upper) : NAs produced
到目前为止，这是我的代码：
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)
（我必须使用t(baseline)，以便生成的matrice2以正确的形式传递给我稍后使用的另一个函数。基线数字范围为-0.281至800.
任何人都有任何关于它为什么会产生NaN的想法？在假期休假之前，我正在使用这个确切的代码，然后就可以了。
编辑：这是baseline.txt数据，305行x 1列文本文件
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
答案 0 :(得分：6)
正如评论中指出的那样，runif min必须小于max。你计算最小值和最大值的方法将最大值从0移动到最远，并且分钟越接近于0，这只有在数字为正时才有效。
正如评论中所建议的，有几种方法可以做到这一点，例如pmin和pmax：
upper = pmax(0.9 * base, 1.1 * base)
lower = pmin(0.9 * base, 1.1 * base)
另一种可能更有效的方式是：
offset = 0.1 * abs(base)
upper = base + offset
lower = base - offset