问题描述
我有一个数据集，该数据集由两个数字变量D和O组成，还有第三个变量指示总共310次观测的采样位置（东或西）。
D和O是线性相关的，我已经在R中使用odregress（）来创建总的最小二乘回归。对两个采样位置分别执行此操作将导致fairly similar regression lines。 我想统计测试这两个位置的坡度和/或截距是否不同。如果我使用了简单的线性回归，则对此将使用ANOVA。但是据我所知，这不适用于完全最小二乘方法，对吗？我在Google上搜索了很多，但是找不到任何解决方案。有人建议分别引导两个组，获得100个不同的斜率和截距，然后再测试这些斜率和截距组，但我觉得应该有一种更简单的方法？
我将不胜感激！
编辑：这是数据集
structure(list(O = c(0.7324,0.78124,0.78556,0.78704,0.81002,0.87443,0.8924,0.91224,0.92517,0.92573,0.92924,0.93397,0.95854,0.96477,0.98186,0.99257,0.9931,0.99488,0.99901,1.0071,1.0119,1.0275,1.0453,1.0467,1.0526,1.0622,1.0644,1.0694,1.0781,1.0785,1.0808,1.0847,1.0855,1.0871,1.0872,1.0935,1.1028,1.1067,1.1106,1.1207,1.1251,1.131,1.1359,1.1387,1.1419,1.1496,1.151,1.1526,1.1536,1.1538,1.1592,1.1595,1.1645,1.1705,1.1766,1.1842,1.1997,1.2,1.2011,1.2077,1.2085,1.2175,1.2183,1.2287,1.2301,1.2362,1.2449,1.248,1.2508,1.254,1.2638,1.2721,1.2745,1.2858,1.3039,1.306,1.3082,1.3151,1.3162,1.3195,1.3365,1.3392,1.3413,1.355,1.3614,1.3675,1.3826,1.3882,1.3926,1.4075,1.4094,1.4109,1.4155,1.4414,1.4487,1.4554,1.4642,1.4771,1.4782,1.4845,1.5,1.5136,1.6112,1.6173,1.6228,1.6761,1.7301,1.7614,1.8407,1.893,2.1033,2.433,0.13104,0.18361,0.25999,0.26253,0.26939,0.36762,0.37335,0.38632,0.39468,0.48303,0.50917,0.5375,0.54697,0.55499,0.55638,0.55957,0.56313,0.58666,0.58995,0.61187,0.63833,0.63971,0.65251,0.66876,0.67105,0.67192,0.67333,0.67489,0.69292,0.69587,0.71104,0.71439,0.715,0.72289,0.72526,0.75811,0.75894,0.76593,0.76717,0.77036,0.7803,0.78315,0.78472,0.78818,0.78862,0.79408,0.79605,0.79814,0.80054,0.81471,0.81491,0.8154,0.81859,0.82125,0.8259,0.8261,0.83919,0.8415,0.84694,0.85396,0.86645,0.86752,0.86833,0.87828,0.88269,0.88637,0.8927,0.90286,0.90783,0.91026,0.91418,0.91427,0.91478,0.9166,0.91863,0.91865,0.92676,0.9295,0.9334,0.93429,0.93487,0.93529,0.94348,0.95148,0.952,0.95354,0.95403,0.9548,0.96222,0.96533,0.96921,0.97023,0.97109,0.97247,0.97251,0.98598,0.98738,0.99089,0.99475,0.99679,0.99814,1.0051,1.0061,1.0074,1.0097,1.0189,1.0231,1.0305,1.0379,1.0452,1.0469,1.0473,1.0474,1.0476,1.0521,1.06,1.0652,1.0673,1.0737,1.0739,1.0796,1.0832,1.0864,1.0922,1.0923,1.0928,1.0987,1.1098,1.1108,1.112,1.119,1.1319,1.135,1.1355,1.1368,1.1406,1.15,1.1532,1.1548,1.1561,1.1563,1.161,1.1628,1.1657,1.1695,1.1726,1.1762,1.1847,1.1851,1.186,1.1861,1.1864,1.1892,1.1944,1.2014,1.2034,1.2035,1.2088,1.209,1.2093,1.21,1.226,1.234,1.2457,1.258,1.2597,1.2601,1.261,1.2619,1.2648,1.2724,1.2785,1.3138,1.3148,1.3452,1.3537,1.3553,1.3613,1.3634,1.374,1.3804,1.39,1.3964,1.4017,1.4033,1.4124,1.454,1.4909,1.4933,1.5095,1.6554,2.2905,2.2935),D = c(6.7335,5.9842,7.2607,6.6692,6.7883,6.3851,6.9412,6.0943,6.617,6.3907,7.8515,7.5698,7.0378,7.4205,7.499,6.8394,7.0227,7.5943,7.6416,6.2831,8.1137,6.5366,8.5699,7.9497,6.6017,6.6698,7.4538,8.0064,7.9721,8.618,8.0632,7.9414,7.1566,6.7663,8.7359,6.8296,7.0741,7.7438,6.97,8.4848,7.6823,7.8706,7.2941,7.6861,7.8883,7.2856,7.7869,7.6533,8.2157,8.0779,7.4342,6.9185,8.6697,8.1597,6.869,7.8173,8.0279,7.3248,8.1733,8.8169,8.1503,8.6909,8.7486,6.9067,8.4934,7.891,8.5693,8.9372,8.5297,8.1161,8.1002,7.764,7.4613,8.3119,8.1478,8.0479,8.0527,8.6343,7.8803,8.032,7.2934,7.7577,10.055,6.9696,8.2901,9.4509,8.6809,8.3964,9.8273,8.0222,8.933,8.6679,9.4189,9.7793,9.468,9.4953,8.9932,9.9725,9.2968,9.9642,9.3904,8.9943,8.8995,9.4839,9.9091,9.3051,9.8542,8.5494,8.5515,8.287,10.293,8.591,7.8362,11.147,2.8316,3.8897,4.5759,3.7706,4.225,4.8624,3.444,4.0051,4.3401,2.938,4.8966,3.5711,4.4281,5.0723,5.4119,5.472,4.9745,5.52,5.5544,6.9943,4.8439,5.0446,4.5001,5.1235,6.3628,4.7419,5.8969,5.5802,5.6402,5.5854,4.9522,4.7576,4.1654,5.571,5.6993,4.6309,5.115,5.5524,5.6906,5.7571,5.6431,5.1011,5.0844,5.6666,4.5314,6.456,5.1582,5.3766,4.7862,5.4651,5.914,5.345,5.494,6.9188,5.7707,4.9756,5.1671,6.1472,5.2446,6.1542,5.9616,5.9502,5.4772,6.1042,5.7241,5.592,4.9474,6.8122,7.1245,6.4829,5.0722,6.7933,6.2212,6.2546,6.434,6.9884,6.4172,5.7726,5.5066,5.8896,6.9811,5.0706,6.2065,6.6623,6.0453,6.6251,5.7937,6.889,6.4776,6.3118,6.2194,4.9703,6.4035,7.5873,6.4064,7.1442,6.461,5.4649,5.3957,6.7057,6.4148,6.52,5.878,6.8155,7.4694,6.4654,6.081,8.053,6.5501,6.6835,6.8489,6.2467,7.4948,7.1152,7.1818,6.4375,7.3438,7.2218,5.4177,7.0612,6.8986,6.9722,6.5899,6.876,6.817,6.9595,7.674,6.3334,6.9009,6.236,7.1216,6.4549,6.8034,6.379,6.6753,6.9686,7.4758,7.2485,6.9605,6.7682,6.7202,7.3145,7.266,6.1579,7.5649,7.1079,7.0922,6.886,6.9401,6.8369,6.8474,7.2315,6.3774,5.6486,7.1576,7.5174,7.3115,7.998,8.4278,7.5225,7.5302,7.196,7.5353,6.5144,7.8133,6.4237,7.9279,6.9488,7.5694,7.078,6.9277,8.1135,7.7531,6.6827,6.9672,7.6023,8.957,7.3327,7.6301,8.0807,6.824,9.4569,8.8401,7.1244,8.6603,7.8307,7.8158,6.5451,8.2186,8.4406,8.3064,6.3104,7.3834,7.3139),Location = structure(c(1L,1L,2L,2L),.Label =
c("East","West"),class = "factor")),class = "data.frame",row.names = c(NA,-309L))
解决方法
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