首先，我很抱歉数据帧太长。但是我得到了这些数据帧的错误。
让我有下面的数据帧(df_1(：
df1_1<-structure(list(realized1 = c(11.121, 0.122, 0.009, 0.007, 0.008,
0.013, 0.003, 0.004, 0.01, 0.01, 0.015, 0.006, 0.001, 0.018,
0.01, 0.011, 0.001, 0.012, 0.014, 0.028, 0.001, 0.024, 0.007,
0.025, 0.025, 0.006, 0.005, 0.005, 0.018, 0.005, 0.003, 0.004,
0.009, 0.009, 0, 0.013, 0.007, 0.024, 0.038, 0.01, 0.002, 0.043,
0, 0.029, 0.012, 0.015, 0.018, 0.019, 0.011, 0.01, 0.012, 0.005,
0.015, 0.003, 0.006, 0.006, 0.01, 0.018, 0.01, 0.002, 0, 0.008,
0.013, 0.002, 0.02, 0.028, 0.001, 0.014, 0.012, 0.02, 0.002,
0.003, 0.003, 0.004, 0.007, 0.027, 0.017, 0.004, 0.007, 0.002,
0.003, 0.005, 0.011, 0.01, 0.012, 0.004, 0.008, 0.009, 0.001,
0.016, 0.004, 0.01, 0.024, 0.014, 0.03, 0.013, 0.001, 0.026,
0.006, 0.001, 0.021, 0.015, 0.002, 0.021, 0.015, 0.025, 0.002,
0.002, 0.005, 0.011, 0.015, 0.006, 0.006, 0, 0.003, 0.021, 0.02,
0.003, 0.01, 0.012, 0.017, 0.013, 0.006, 0.008, 0.002, 0.014,
0.003, 0.004, 0, 0.015, 0.005, 0.01, 0.014, 0.008, 0.011, 0.003,
0.009, 0.008, 0.002, 0.01, 0.003, 0.003, 0.012, 0.018, 0.021,
0.005, 0.003, 0.022, 0.004, 0.006, 0.003, 0.007, 0.033, 0.053,
0.073, 0.014, 0.052, 0.034, 0.003, 0.024, 0.011, 0.002, 0.043,
0.022, 0.004, 0.017, 0.012, 0.002, 0.014, 0.004, 0.026, 0.024,
0.018, 0.004, 0.026, 0.014, 0.005, 0.019, 0.01, 0.018, 0.013,
0, 0.05, 0.002, 0.048, 0.028, 0.009, 0.025, 0.012, 0.013, 0.001,
0.004, 0.038, 0.002, 0.002, 0.002, 0.017, 0.006, 0.013, 0.015,
0.013, 0.015, 0.005, 0.016, 0.033, 0.016, 0.01, 0, 0.015, 0.021,
0.007, 0.017, 0.022, 0.016, 0.014, 0.006, 0.011, 0.012, 0.006,
0.001, 0, 0.015, 0.011, 0.032, 0.014, 0.017, 0.029, 0.029, 0.023,
0.004, 0.046, 0.014, 0.018, 0.007, 0.01, 0.009, 0.03, 0.007,
0.026, 0.002, 0.023, 0.011, 0.004, 0.018, 0.027, 0.008, 0.003,
0.007, 0.011, 0.001, 0.019, 0.01, 0.015, 0.002, 0.029, 0.026,
0.006, 0.02, 0.007, 0.019, 0.012, 0.014, 0.012, 0.024, 0.014,
0.016, 0.004, 0.005, 0.005, 0.007, 0.002, 0.036, 0.006, 0.008,
0.011, 0.035, 0.014, 0.001, 0.009, 0.002, 0.01, 0.017, 0.014,
0.021, 0.015, 0.003, 0.003, 0.018, 0.005, 0.003, 0.006, 0.02,
0.001, 0.016, 0.02, 0.012, 0, 0.003, 0.02, 0.009, 0.006, 0.003,
0.007, 0.004, 0.006, 0.024, 0.007, 0.017, 0.003, 0.006, 0.002,
0.004, 0.006, 0.018, 0.001, 0.011, 0.004, 0.014, 0.009, 0.005,
0.013, 0.006, 0.018, 0.002, 0.014, 0.012, 0.003, 0.005, 0.002,
0.003, 0.009, 0.005, 0.005, 0.007, 0.004, 0.012, 0.002, 0.01,
0.018, 0.006, 0.005, 0.003, 0.001, 0.011, 0.013, 0.001, 0.018,
0.007, 0.011, 0.014, 0.007, 0.007, 0.01, 0.024, 0.015, 0.002,
0.001, 0.011, 0.006, 0, 0.009, 0.003, 0.001, 0.008, 0.008, 0.011,
0.003, 0.012, 0.001, 0.002, 0.002, 0.002, 0.017, 0.002, 0.017,
0, 0.016, 0.011, 0.017, 0.006, 0.028, 0.01, 0.013, 0.004, 0.016,
0.013, 0.003, 0.008, 0.001, 0.012, 0.005, 0, 0.003, 0, 0.002,
0.009, 0.017, 0.01, 0.006, 0.034, 0.008, 0.01, 0.012, 0.003,
0.005, 0.024, 0.004, 0.01, 0.004, 0.003, 0.01, 0, 0.015, 0.007,
0.005, 0.013, 0.005, 0, 0.006, 0.004, 0.003, 0, 0.018, 0.003,
0.005, 0.01, 0.002, 0.003, 0.003, 0, 0.001, 0.013, 0.013, 0.001,
0.007, 0.007, 0.002, 0.013, 0.01, 0.009, 0.017, 0.002, 0.006,
0.005, 0.011, 0.01, 0.001, 0.007, 0.001, 0.009, 0.006, 0.016,
0, 0.002, 0.007, 0.006, 0.005, 0.006, 0.006, 0.006, 0.001, 0.014,
0.011, 0, 0.016, 0.018, 0.01, 0.006, 0.015, 0.012, 0.006, 0.009,
0.01, 0.004, 0.011, 0.002, 0, 0.002, 0.008, 0.015, 0.012, 0.002,
0, 0.007, 0.008, 0.017, 0.016, 0.007, 0.003, 0.006, 0.006, 0.001,
0.008, 0.006, 0.009, 0.015, 0.002, 0.006, 0, 0.002, 0.008, 0.004,
0.005, 0.006, 0.002, 0.005, 0.018, 0.005, 0.006, 0.008, 0.001,
0.012, 0.004, 0.002, 0.012, 0.005, 0.02, 0.009, 0.002, 0.003,
0.004, 0.008, 0.001, 0.02, 0.043, 0.013, 0.04, 0.002, 0.018,
0.006, 0.005, 0.003, 0.024, 0.007, 0.018, 0.011, 0.002, 0.005,
0.006, 0.002, 0.005, 0.008, 0.021, 0.018, 0.01, 0.011, 0.008,
0.015, 0.006, 0.009, 0.005, 0.014, 0.004, 0.021, 0.005, 0.003,
0.009, 0.008, 0.014, 0.003, 0.013, 0.001, 0.003, 0.001, 0.004,
0.014, 0.002, 0.013, 0.007, 0.002, 0.001, 0.014, 0.019, 0.014,
0.016, 0.007, 0.004, 0.02, 0.005, 0.006, 0.005, 0.004, 0.016,
0.003, 0.009, 0.009, 0.002, 0.002, 0, 0.01, 0.03, 0.004, 0.007,
0.003, 0.016, 0.01, 0.004, 0.002, 0.017, 0.009, 0.002, 0, 0.019,
0.012, 0.021, 0.02, 0.003, 0.005, 0.007, 0.031, 0, 0.014, 0.11,
0.049, 0.014, 0.048, 0.032, 0.025, 0.018, 0.024, 0.005, 0.045,
0.014, 0.015, 0.014, 0.071, 0.004, 0.034, 0.009, 0.036, 0.025,
0.007, 0.006, 0.004, 0.036, 0.023, 0.031, 0.019, 0.004, 0.013,
0.01, 0.029, 0.03, 0.008, 0.018, 0.001, 0.015, 0.001, 0.006,
0.018, 0.002, 0.018, 0.005, 0.02, 0.014, 0, 0.009, 0.003, 0.004,
0.007, 0.025, 0.002, 0.001, 0.021, 0.004, 0.026, 0.002, 0.035,
0.02, 0.005, 0.012, 0.049, 0, 0.014, 0.032, 0.024, 0, 0.004,
0.008, 0.036, 0.029, 0.001, 0.013, 0.015, 0.036, 0.007, 0.002,
0.062, 0.021, 0.013, 0.004, 0.004, 0.008, 0.011, 0.004, 0.026,
0.018, 0.015, 0.001, 0.004, 0.011, 0.008, 0.016, 0.007, 0.001,
0.034, 0.005, 0.01, 0.004, 0.005, 0.004, 0.008, 0.016, 0.014,
0.003, 0.02, 0.014, 0, 0.025, 0.024, 0.003, 0.006, 0.022, 0.004,
0.018, 0.013, 0.01, 0.01, 0.013, 0.012, 0.013, 0.009, 0.004,
0.016, 0.011, 0.025, 0, 0.015, 0.019, 0.011, 0.002, 0.002, 0.012,
0.014, 0.01, 0.054, 0.007, 0.033, 0.007, 0.022, 0.014, 0.043,
0.023, 0.01, 0.062, 0.003, 0.012, 0.015, 0.031, 0.008, 0.019,
0.014, 0.022, 0.002, 0, 0.001, 0.019, 0.018, 0.002, 0.008, 0.016,
0.029, 0.015, 0.002, 0.016, 0.023, 0.01, 0.014, 0.003, 0.017,
0.005, 0.033, 0.001, 0.009, 0.004, 0.004, 0.012, 0.018, 0.011,
0.023, 0.011, 0.005, 0.003, 0.005, 0.033, 0.01, 0.005, 0.012,
0.022, 0.03)), row.names = c(NA, -800L), class = "data.frame")
df1_2<-structure(list(ann_t1 = c(-3.57, -0.81, -1.62, -1.82, -2.11,
-2.36, -2.57, -2.72, -2.89, -3.04, -3.18, -3.23, -3.24, -3.31,
-3.41, -3.36, -3.46, -3.44, -3.52, -3.6, -3.52, -3.35, -3.44,
-3.54, -3.61, -3.43, -3.53, -3.54, -3.61, -3.68, -3.54, -3.61,
-3.65, -3.7, -3.76, -3.81, -3.84, -3.7, -3.67, -3.48, -3.24,
-3.35, -3.43, -3.18, -3.29, -3.41, -3.5, -3.45, -3.54, -3.62,
-3.69, -3.75, -3.8, -3.77, -3.64, -3.7, -3.75, -3.8, -3.84, -3.64,
-3.7, -3.73, -3.77, -3.72, -3.78, -3.79, -3.84, -3.9, -3.93,
-3.96, -3.99, -3.69, -3.74, -3.75, -3.8, -3.79, -3.83, -3.52,
-3.6, -3.67, -3.72, -3.77, -3.81, -3.78, -3.69, -3.75, -3.8,
-3.84, -3.88, -3.78, -3.81, -3.64, -3.65, -3.6, -3.43, -3.52,
-3.33, -3.43, -3.51, -3.36, -3.41, -3.48, -3.57, -3.5, -3.57,
-3.44, -3.4, -3.51, -3.58, -3.63, -3.63, -3.7, -3.76, -3.73,
-3.7, -3.75, -3.76, -3.55, -3.44, -3.52, -3.6, -3.55, -3.63,
-3.55, -3.62, -3.69, -3.71, -3.77, -3.81, -3.85, -3.87, -3.92,
-3.86, -3.76, -3.81, -3.75, -3.67, -3.68, -3.74, -3.69, -3.72,
-3.66, -3.68, -3.7, -3.61, -3.5, -3.59, -3.65, -3.67, -3.74,
-3.74, -3.71, -3.73, -3.69, -3.39, -3.08, -2.83, -2.99, -2.89,
-3.05, -3.18, -3.31, -3.42, -3.5, -3.21, -3.33, -3.43, -3.38,
-3.48, -3.54, -3.62, -3.68, -3.75, -3.51, -3.6, -3.66, -3.74,
-3.62, -3.63, -3.7, -3.64, -3.71, -3.77, -3.81, -3.9, -3.9, -3.28,
-3.19, -3.31, -3.42, -3.51, -3.59, -3.64, -3.66, -3.32, -3.42,
-3.49, -3.56, -3.64, -3.7, -3.76, -3.63, -3.69, -3.59, -3.65,
-3.54, -3.31, -3.42, -3.43, -3.51, -3.46, -3.55, -3.62, -3.51,
-3.4, -3.49, -3.58, -3.58, -3.54, -3.61, -3.61, -3.67, -3.72,
-3.6, -3.56, -3.33, -3.33, -3.3, -3.2, -3.33, -3.44, -3.49, -3.6,
-3.53, -3.61, -3.68, -3.63, -3.69, -3.42, -3.51, -3.35, -3.45,
-3.34, -3.44, -3.49, -3.41, -3.51, -3.52, -3.56, -3.63, -3.7,
-3.75, -3.57, -3.64, -3.54, -3.61, -3.69, -3.47, -3.49, -3.4,
-3.43, -3.53, -3.61, -3.68, -3.61, -3.69, -3.75, -3.8, -3.84,
-3.88, -3.91, -3.83, -3.86, -3.93, -3.95, -3.98, -3.82, -3.89,
-3.93, -3.95, -3.97, -3.95, -3.98, -3.74, -3.63, -3.7, -3.59,
-3.65, -3.71, -3.77, -3.81, -3.81, -3.76, -3.57, -3.64, -3.54,
-3.62, -3.69, -3.74, -3.78, -3.58, -3.56, -3.63, -3.69, -3.67,
-3.67, -3.73, -3.79, -3.84, -3.88, -3.91, -3.85, -3.86, -3.82,
-3.78, -3.84, -3.85, -3.74, -3.73, -3.79, -3.83, -3.87, -3.72,
-3.7, -3.55, -3.6, -3.53, -3.61, -3.67, -3.67, -3.7, -3.75, -3.69,
-3.69, -3.74, -3.71, -3.76, -3.81, -3.82, -3.73, -3.58, -3.65,
-3.65, -3.67, -3.71, -3.63, -3.7, -3.75, -3.59, -3.59, -3.66,
-3.57, -3.57, -3.57, -3.64, -3.46, -3.42, -3.49, -3.56, -3.64,
-3.63, -3.68, -3.74, -3.78, -3.82, -3.86, -3.9, -3.93, -3.95,
-3.98, -3.99, -4, -4.01, -3.98, -4.01, -4.02, -4.05, -4.05, -3.79,
-3.84, -3.89, -3.83, -3.89, -3.78, -3.83, -3.86, -3.68, -3.74,
-3.75, -3.8, -3.82, -3.71, -3.76, -3.8, -3.79, -3.82, -3.83,
-3.75, -3.6, -3.67, -3.72, -3.8, -3.84, -3.88, -3.75, -3.76,
-3.74, -3.81, -3.84, -3.75, -3.75, -3.75, -3.8, -3.84, -3.67,
-3.73, -3.78, -3.83, -3.86, -3.88, -3.82, -3.86, -3.89, -3.91,
-3.95, -3.93, -3.88, -3.92, -3.94, -3.96, -3.97, -3.98, -3.99,
-3.8, -3.85, -3.86, -3.79, -3.74, -3.77, -3.82, -3.72, -3.78,
-3.61, -3.67, -3.67, -3.72, -3.78, -3.69, -3.74, -3.79, -3.83,
-3.87, -3.9, -3.94, -3.95, -3.97, -3.99, -4.01, -3.94, -3.96,
-3.99, -3.9, -3.91, -3.95, -3.98, -3.99, -3.76, -3.82, -3.73,
-3.71, -3.77, -3.67, -3.66, -3.72, -3.65, -3.71, -3.63, -3.66,
-3.72, -3.73, -3.69, -3.75, -3.81, -3.84, -3.86, -3.9, -3.93,
-3.97, -4, -4.02, -3.97, -3.99, -4.01, -4.01, -4.03, -3.94, -3.84,
-3.88, -3.87, -3.82, -3.85, -3.85, -3.89, -3.92, -3.94, -3.96,
-3.98, -3.92, -3.96, -3.98, -3.9, -3.93, -3.94, -3.97, -3.92,
-3.94, -3.97, -3.99, -4.03, -4.04, -4.05, -4.05, -4.06, -4.07,
-4.07, -3.75, -3.3, -3.41, -3.19, -3.29, -3.4, -3.49, -3.52,
-3.56, -3.41, -3.5, -3.42, -3.51, -3.56, -3.58, -3.65, -3.68,
-3.73, -3.78, -3.57, -3.47, -3.56, -3.63, -3.69, -3.76, -3.8,
-3.85, -3.88, -3.92, -3.88, -3.93, -3.87, -3.9, -3.8, -3.74,
-3.8, -3.83, -3.7, -3.73, -3.78, -3.81, -3.85, -3.89, -3.92,
-3.95, -3.97, -3.99, -3.98, -3.79, -3.6, -3.53, -3.61, -3.6,
-3.62, -3.69, -3.74, -3.79, -3.77, -3.77, -3.62, -3.68, -3.74,
-3.79, -3.82, -3.86, -3.88, -3.92, -3.97, -3.99, -4.01, -3.97,
-4, -3.85, -3.83, -3.86, -3.91, -3.94, -3.93, -3.94, -3.68, -3.74,
-3.81, -3.6, -3.62, -3.63, -3.62, -3.37, -3.46, -3.43, -2.65,
-2.84, -2.95, -2.88, -3.04, -3.05, -3.09, -3.23, -3.3, -3.43,
-3.4, -3.37, -3.47, -2.97, -3.09, -3.03, -3.17, -3.31, -3.42,
-3.51, -3.58, -3.61, -3.32, -3.43, -3.27, -3.24, -3.35, -3.35,
-3.45, -3.55, -3.64, -3.62, -3.69, -3.73, -3.61, -3.66, -3.65,
-3.52, -3.57, -3.47, -3.55, -3.64, -3.55, -3.62, -3.68, -3.73,
-3.74, -3.71, -3.77, -3.81, -3.85, -3.6, -3.66, -3.45, -3.51,
-3.28, -3.25, -3.32, -3.43, -3.13, -3.25, -3.36, -3.47, -3.35,
-3.45, -3.5, -3.58, -3.67, -3.75, -3.78, -3.83, -3.67, -3.75,
-3.8, -3.81, -3.91, -3.64, -3.56, -3.6, -3.62, -3.6, -3.55, -3.58,
-3.67, -3.54, -3.62, -3.67, -3.68, -3.61, -3.6, -3.67, -3.73,
-3.77, -3.84, -3.88, -3.77, -3.82, -3.85, -3.88, -3.8, -3.63,
-3.55, -3.58, -3.46, -3.55, -3.62, -3.43, -3.32, -3.4, -3.49,
-3.58, -3.64, -3.71, -3.61, -3.68, -3.63, -3.7, -3.76, -3.65,
-3.61, -3.63, -3.7, -3.63, -3.44, -3.52, -3.46, -3.55, -3.63,
-3.67, -3.7, -3.61, -3.68, -3.74, -3.18, -3.3, -3.18, -3.29,
-3.25, -3.36, -3.14, -3.13, -3.19, -3.34, -3.41, -3.4, -3.37,
-3.48, -3.56, -3.46, -3.43, -3.52, -3.59, -3.66, -3.71, -3.55,
-3.45, -3.53, -3.61, -3.68, -3.42, -3.39, -3.48, -3.42, -3.33,
-3.43, -3.4, -3.47, -3.56, -3.58, -3.67, -3.72, -3.67, -3.72,
-3.77, -3.67, -3.74, -3.79, -3.55, -3.52, -3.59, -3.65, -3.71,
-3.4, -3.41, -3.5, -3.58)), row.names = c(NA, -800L), class = "data.frame")
df1_3<-structure(list(ann_t2 = c(0, -3.372, -0.059, -0.005, -0.004,
-0.006, -0.011, -0.003, -0.004, -0.01, -0.012, -0.019, -0.007,
-0.001, -0.024, -0.013, -0.016, -0.001, -0.018, -0.021, -0.041,
-0.002, -0.033, -0.01, -0.037, -0.034, -0.009, -0.008, -0.007,
-0.028, -0.007, -0.004, -0.006, -0.014, -0.015, 0, -0.023, -0.011,
-0.037, -0.053, -0.013, -0.002, -0.059, 0, -0.036, -0.016, -0.021,
-0.025, -0.028, -0.016, -0.015, -0.019, -0.009, -0.024, -0.005,
-0.009, -0.01, -0.017, -0.031, -0.016, -0.003, 0, -0.013, -0.02,
-0.004, -0.033, -0.048, -0.002, -0.025, -0.022, -0.038, -0.003,
-0.004, -0.005, -0.006, -0.012, -0.047, -0.025, -0.007, -0.012,
-0.003, -0.004, -0.009, -0.018, -0.015, -0.019, -0.006, -0.014,
-0.016, -0.001, -0.028, -0.006, -0.016, -0.036, -0.019, -0.043,
-0.017, -0.001, -0.037, -0.009, -0.002, -0.03, -0.022, -0.004,
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)), row.names = c(NA, -800L), class = "data.frame")
df1_4<-structure(list(ann_t3 = c(0, 2.818, -0.049, 0.004, 0.004, -0.005,
-0.009, -0.002, 0.003, 0.009, -0.01, -0.016, -0.006, 0.001, -0.02,
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0.011, 0.013, 0, -0.019, -0.01, -0.031, -0.045, 0.011, -0.002,
-0.049, 0, 0.03, 0.013, -0.018, 0.021, 0.023, 0.014, 0.013, 0.016,
-0.007, -0.02, 0.004, 0.007, 0.008, 0.014, -0.026, 0.013, -0.002,
0, -0.011, 0.017, -0.003, 0.028, 0.04, 0.002, 0.021, 0.019, -0.032,
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-0.031, 0.005, -0.034, -0.002, -0.042, -0.021, -0.005, 0.013,
-0.055, 0, 0.015, 0.035, -0.028, 0, -0.004, 0.009, 0.044, 0.038,
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0.023, -0.018, 0.013, -0.013, 0.016, 0.015, -0.018, -0.011, -0.005,
0.02, -0.014, -0.032, 0, -0.018, 0.022, 0.014, -0.002, -0.003,
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0.01, -0.023, -0.016, 0.026, 0.002, 0, 0.001, -0.026, -0.022,
0.002, 0.01, 0.02, -0.039, -0.017, 0.003, -0.019, -0.026, 0.011,
-0.016, -0.003, 0.02, -0.006, 0.041, 0.001, -0.012, 0.005, 0.005,
-0.016, 0.023, 0.015, -0.032, -0.014, 0.006, 0.004, 0.006, -0.044,
-0.011, 0.005, 0.015, -0.028)), row.names = c(NA, -800L), class = "data.frame")
df_1<-cbind(df1_1,df1_2,df1_3,df1_4)
使用以上数据，我正在训练以下模型：
model1<-neuralnet(realized1~ann_t1+ann_t2+ann_t3, data=df_1, hidden=3, act.fct = "logistic",linear.output = TRUE)
然后，对于以下数据帧(df_2(，我使用训练的模型(模型1(进行预测：
df2_1<-structure(list(ann_t1 = c(-3.84, -3.842, -3.849, -3.805, -3.951,
-4, -3.898, -3.922, -3.962, -4.002, -3.703, -3.59, -3.875, -3.973,
-4.008, -4.021, -3.678, -3.223, -3.751, -3.434, -3.725, -3.671,
-3.903, -3.737, -3.697, -3.912, -3.595, -3.88, -3.782, -3.942,
-3.707, -3.918, -3.987, -3.671, -3.903, -3.981, -4.009, -4.021,
-4.026, -3.707, -3.915, -3.568, -3.643, -3.894, -3.868, -3.97,
-3.778, -3.939, -3.894, -3.979, -3.931, -3.994, -4.013, -3.947,
-3.676, -3.6, -3.878, -3.843, -3.964, -3.876, -3.663, -3.9, -3.982,
-3.961, -4.003, -3.625, -3.785, -3.868, -3.76, -3.94, -3.78,
-3.942, -3.999, -3.907, -3.965, -3.748, -3.93, -3.755, -3.932,
-3.987, -4.013, -3.913, -3.982, -4.013, -4.02, -3.821, -3.953,
-3.628, -3.83, -3.92, -3.99, -3.788, -3.678, -3.792, -3.732,
-3.924, -3.859, -3.967, -3.937, -3.915)), row.names = c(NA, -100L
), class = "data.frame")
df2_2<-structure(list(ann_t2 = c(0, -19.253, -0.212, -0.015, -0.013,
-0.015, -0.023, -0.005, -0.007, -0.018, -0.016, -0.023, -0.01,
-0.001, -0.034, -0.019, -0.018, -0.001, -0.02, -0.019, -0.046,
-0.002, -0.043, -0.012, -0.039, -0.045, -0.01, -0.01, -0.008,
-0.032, -0.008, -0.005, -0.007, -0.013, -0.017, 0, -0.025, -0.014,
-0.046, -0.061, -0.019, -0.002, -0.066, 0, -0.051, -0.022, -0.025,
-0.033, -0.034, -0.02, -0.017, -0.022, -0.01, -0.027, -0.005,
-0.008, -0.01, -0.018, -0.034, -0.018, -0.003, 0, -0.015, -0.023,
-0.004, -0.03, -0.047, -0.002, -0.023, -0.022, -0.034, -0.003,
-0.005, -0.005, -0.007, -0.012, -0.05, -0.028, -0.008, -0.014,
-0.003, -0.005, -0.01, -0.02, -0.018, -0.02, -0.007, -0.012,
-0.016, -0.001, -0.031, -0.007, -0.016, -0.041, -0.022, -0.054,
-0.023, -0.001, -0.047, -0.012)), row.names = c(NA, -100L), class = "data.frame")
df2_3<-structure(list(ann_t3 = c(0, 16.092, -0.178, 0.012, 0.011, -0.013,
-0.019, -0.004, 0.006, 0.015, -0.014, -0.019, -0.009, 0.001,
-0.028, 0.016, -0.015, -0.001, 0.017, -0.016, -0.038, 0.002,
0.036, 0.01, -0.033, 0.037, -0.008, 0.008, 0.007, -0.027, 0.007,
-0.004, 0.006, 0.011, 0.014, 0, -0.021, -0.011, -0.038, -0.051,
0.016, -0.002, -0.055, 0, 0.043, 0.018, -0.021, 0.028, 0.028,
0.017, 0.015, 0.018, -0.008, -0.022, 0.004, 0.007, 0.009, 0.015,
-0.028, 0.015, -0.002, 0, -0.012, 0.02, -0.003, 0.025, 0.039,
0.002, 0.019, 0.018, -0.029, 0.003, -0.004, 0.004, -0.006, 0.01,
-0.042, 0.024, 0.007, 0.012, 0.003, 0.004, -0.008, -0.017, 0.015,
0.017, 0.006, 0.01, -0.013, -0.001, -0.026, -0.006, -0.014, -0.034,
0.019, -0.045, 0.019, -0.001, -0.039, -0.01)), row.names = c(NA,
-100L), class = "data.frame")
df_2<-cbind(df2_1,df2_1,df2_3)
预测(y(代码如下：
y<-as.data.frame(predict(model1,df_2))
但最后一行给出了以下错误：
> y<-as.data.frame(predict(model1,df_2))
Error in h(simpleError(msg, call)) :
error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': requires numeric/complex matrix/vector arguments
为什么会出现此错误，以及如何修复此错误？虽然，我更喜欢使用神经网络软件包，但我也可以使用其他软件包来解决问题。
我看到了一些关于那个问题的话题。但是，我无法通过使用它们来解决问题。
ı将非常高兴得到任何帮助。非常感谢。
我们可以从默认的0.01更改threshold值，因为默认值会导致模型收敛问题。基于?neuralnet的文档
threshold-一个数值，指定误差函数偏导数的阈值作为停止标准。
使用该信息，将值修改为另一个值，即0.02
library(neuralnet)
model1 <- neuralnet(realized1~ann_t1+ann_t2+ann_t3, data=df_1, hidden=3,
threshold = 0.05, act.fct = "logistic",linear.output = TRUE)
y <- as.data.frame(predict(model1,df_2))
str(y)
#'data.frame': 100 obs. of 1 variable:
# $ V1: num -1.06 1.31 -1.13 -1.05 -1.06 ...