我是R的初学者，我想我需要帮助。我估计了这种类型的线性回归：y1 = a0 + a1 X + a2 X^2
这是我的数据框：
structure(list(Y = c(NA, -1.793, -0.642, 1.189, -0.823, -1.715,
1.623, 0.964, 0.395, -3.736, -0.47, 2.366, 0.634, -0.701, -1.692,
0.155, 2.502, -2.292, 1.967, -2.326, -1.476, 1.464, 1.45, -0.797,
1.27, 2.515, -0.765, 0.261, 0.423, 1.698, -2.734, 0.743, -2.39,
0.365, 2.981, -1.185, -0.57, 2.638, -1.046, 1.931, 4.583, -1.276,
1.075, 2.893, -1.602, 1.801, 2.405, -5.236, 2.214, 1.295, 1.438,
-0.638, 0.716, 1.004, -1.328, -1.759, -1.315, 1.053, 1.958, -2.034,
2.936, -0.078, -0.676, -2.312, -0.404, -4.091, -2.456, 0.984,
-1.648, 0.517, 0.545, -3.406, -2.077, 4.263, -0.352, -1.107,
-2.478, -0.718, 2.622, 1.611, -4.913, -2.117, -1.34, -4.006,
-1.668, -1.934, 0.972, 3.572, -3.332, 1.094, -0.273, 1.078, -0.587,
-1.25, -4.231, -0.439, 1.776, -2.077, 1.892, -1.069, 4.682, 1.665,
1.793, -2.133, 1.651, -0.065, 2.277, 0.792, -3.469, 1.48, 0.958,
-4.68, -2.909, 1.169, -0.941, -1.863, 1.814, -2.082, -3.087,
0.505, -0.013, -0.12, -0.082, -1.944, 1.094, -1.418, -1.273,
0.741, -1.001, -1.945, 1.026, 3.24, 0.131, -0.061, 0.086, 0.35,
0.22, -0.704, 0.466, 8.255, 2.302, 9.819, 5.162, 6.51, -0.275,
1.141, -0.56, -3.324, -8.456, -2.105, -0.666, 1.707, 1.886, -3.018,
0.441, 1.612, 0.774, 5.122, 0.362, -0.903, 5.21, -2.927, -4.572,
1.882, -2.5, -1.449, 2.627, -0.532, -2.279, -1.534, 1.459, -3.975,
1.328, 2.491, -2.221, 0.811, 4.423, -3.55, 2.592, 1.196, -1.529,
-1.222, -0.019, -1.62, 5.356, -1.885, 0.105, -1.366, -1.652,
0.233, 0.523, -1.416, 2.495, 4.35, -0.033, -2.468, 2.623, -0.039,
0.043, -2.015, -4.58, 0.793, -1.938, -1.105, 0.776, -1.953, 0.521,
-1.276, 0.666, -1.919, 1.268, 1.646, 2.413, 1.323, 2.135, 0.435,
3.747, -2.855, 4.021, -3.459, 0.705, -3.018, 0.779, 1.452, 1.523,
-1.938, 2.564, 2.108, 3.832, 1.77, -3.087, -1.902, 0.644, 8.507
), X = c(0.056, 0.053, 0.033, 0.053, 0.062, 0.09, 0.11, 0.124,
0.129, 0.129, 0.133, 0.155, 0.143, 0.155, 0.166, 0.151, 0.144,
0.168, 0.171, 0.162, 0.168, 0.169, 0.117, 0.105, 0.075, 0.057,
0.031, 0.038, 0.034, -0.016, -0.001, -0.031, -0.001, -0.004,
-0.056, -0.016, 0.007, 0.015, -0.016, -0.016, -0.053, -0.059,
-0.054, -0.048, -0.051, -0.052, -0.072, -0.063, 0.02, 0.034,
0.043, 0.084, 0.092, 0.111, 0.131, 0.102, 0.167, 0.162, 0.167,
0.187, 0.165, 0.179, 0.177, 0.192, 0.191, 0.183, 0.179, 0.176,
0.19, 0.188, 0.215, 0.221, 0.203, 0.2, 0.191, 0.188, 0.19, 0.228,
0.195, 0.204, 0.221, 0.218, 0.224, 0.233, 0.23, 0.258, 0.268,
0.291, 0.275, 0.27, 0.276, 0.276, 0.248, 0.228, 0.223, 0.218,
0.169, 0.188, 0.159, 0.156, 0.15, 0.117, 0.088, 0.068, 0.057,
0.035, 0.021, 0.014, -0.005, -0.014, -0.029, -0.043, -0.046,
-0.068, -0.073, -0.042, -0.04, -0.027, -0.018, -0.021, 0.002,
0.002, 0.006, 0.015, 0.022, 0.039, 0.044, 0.055, 0.064, 0.096,
0.093, 0.089, 0.173, 0.203, 0.216, 0.208, 0.225, 0.245, 0.23,
0.218, -0.267, 0.193, -0.013, 0.087, 0.04, 0.012, -0.008, 0.004,
0.01, 0.002, 0.008, 0.006, 0.013, 0.018, 0.019, 0.018, 0.021,
0.024, 0.017, 0.015, -0.005, 0.002, 0.014, 0.021, 0.022, 0.022,
0.02, 0.025, 0.021, 0.027, 0.034, 0.041, 0.04, 0.038, 0.033,
0.034, 0.031, 0.029, 0.029, 0.029, 0.022, 0.021, 0.019, 0.021,
0.016, 0.007, 0.002, 0.011, 0.01, 0.01, 0.003, 0.009, 0.015,
0.018, 0.017, 0.021, 0.021, 0.021, 0.022, 0.023, 0.025, 0.022,
0.022, 0.019, 0.02, 0.023, 0.022, 0.024, 0.022, 0.025, 0.025,
0.022, 0.027, 0.024, 0.016, 0.024, 0.018, 0.024, 0.021, 0.021,
0.021, 0.021, 0.022, 0.016, 0.015, 0.017, -0.017, -0.009, -0.003,
-0.012, -0.009, -0.008, -0.024, -0.023), X2 = c(0.003136, 0.002809,
0.001089, 0.002809, 0.003844, 0.0081, 0.0121, 0.015376, 0.016641,
0.016641, 0.017689, 0.024025, 0.020449, 0.024025, 0.027556, 0.022801,
0.020736, 0.028224, 0.029241, 0.026244, 0.028224, 0.028561, 0.013689,
0.011025, 0.005625, 0.003249, 0.000961, 0.001444, 0.001156, 0.000256,
1e-06, 0.000961, 1e-06, 1.6e-05, 0.003136, 0.000256, 4.9e-05,
0.000225, 0.000256, 0.000256, 0.002809, 0.003481, 0.002916, 0.002304,
0.002601, 0.002704, 0.005184, 0.003969, 4e-04, 0.001156, 0.001849,
0.007056, 0.008464, 0.012321, 0.017161, 0.010404, 0.027889, 0.026244,
0.027889, 0.034969, 0.027225, 0.032041, 0.031329, 0.036864, 0.036481,
0.033489, 0.032041, 0.030976, 0.0361, 0.035344, 0.046225, 0.048841,
0.041209, 0.04, 0.036481, 0.035344, 0.0361, 0.051984, 0.038025,
0.041616, 0.048841, 0.047524, 0.050176, 0.054289, 0.0529, 0.066564,
0.071824, 0.084681, 0.075625, 0.0729, 0.076176, 0.076176, 0.061504,
0.051984, 0.049729, 0.047524, 0.028561, 0.035344, 0.025281, 0.024336,
0.0225, 0.013689, 0.007744, 0.004624, 0.003249, 0.001225, 0.000441,
0.000196, 2.5e-05, 0.000196, 0.000841, 0.001849, 0.002116, 0.004624,
0.005329, 0.001764, 0.0016, 0.000729, 0.000324, 0.000441, 4e-06,
4e-06, 3.6e-05, 0.000225, 0.000484, 0.001521, 0.001936, 0.003025,
0.004096, 0.009216, 0.008649, 0.007921, 0.029929, 0.041209, 0.046656,
0.043264, 0.050625, 0.060025, 0.0529, 0.047524, 0.071289, 0.037249,
0.000169, 0.007569, 0.0016, 0.000144, 6.4e-05, 1.6e-05, 1e-04,
4e-06, 6.4e-05, 3.6e-05, 0.000169, 0.000324, 0.000361, 0.000324,
0.000441, 0.000576, 0.000289, 0.000225, 2.5e-05, 4e-06, 0.000196,
0.000441, 0.000484, 0.000484, 4e-04, 0.000625, 0.000441, 0.000729,
0.001156, 0.001681, 0.0016, 0.001444, 0.001089, 0.001156, 0.000961,
0.000841, 0.000841, 0.000841, 0.000484, 0.000441, 0.000361, 0.000441,
0.000256, 4.9e-05, 4e-06, 0.000121, 1e-04, 1e-04, 9e-06, 8.1e-05,
0.000225, 0.000324, 0.000289, 0.000441, 0.000441, 0.000441, 0.000484,
0.000529, 0.000625, 0.000484, 0.000484, 0.000361, 4e-04, 0.000529,
0.000484, 0.000576, 0.000484, 0.000625, 0.000625, 0.000484, 0.000729,
0.000576, 0.000256, 0.000576, 0.000324, 0.000576, 0.000441, 0.000441,
0.000441, 0.000441, 0.000484, 0.000256, 0.000225, 0.000289, 0.000289,
8.1e-05, 9e-06, 0.000144, 8.1e-05, 6.4e-05, 0.000576, 0.000529
)), .Names = c("Y", "X", "X2"), row.names = c(NA, -234L), class = "data.frame")
现在我需要在变量X的分位数0.25和0.75中调整的拟合值。换句话说，我将有两行拟合值：一行，以X（0.25）和X ^ 2（0.25）为条件。第二个是X（0.75）和X ^ 2（0.75）。我怎样才能做到这一点？
这是我的代码：
regression<-lm(data[,1]~data[,2]+data[,3], data=data)
X_0.25<-quantile(data[,2],0.25)
X2_0.25<-quantile(data[,3],0.25)
X_0.75<-quantile(data[,2],0.75)
X2_0.75<-quantile(data[,3],0.75)
Predict_Variables<-matrix(0,2,2)
Predict_Variables[1,1]<-X_0.25
Predict_Variables[1,2]<-X2_0.25
Predict_Variables[2,1]<-X_0.75
Predict_Variables[2,2]<-X2_0.75
newdata<-as.data.frame(Predict_Variables)
predict(regression,newdata)
我做错了什么？
谢谢！