Getting "requires numeric/complex matrix/vector arguments" error using neuralnet package - R

Question

First of all, I am sorry for the long data frames. But I am getting the error with these data frames.

Let I have the below data frame(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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0, -0.033, -0.005, -0.008, -0.018, -0.004, -0.006, -0.005, 0, 
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0, -0.029, -0.03, -0.017, -0.01, -0.023, -0.019, -0.01, -0.014, 
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-0.007, -0.005, -0.007, -0.052, -0.013, -0.006, -0.018, -0.033
)), 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, 
0.011, -0.013, -0.001, 0.015, -0.018, -0.034, 0.002, 0.028, 0.009, 
-0.031, 0.028, -0.008, 0.007, 0.006, -0.023, 0.006, -0.003, 0.005, 
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, 
0.002, -0.004, 0.004, -0.005, 0.01, -0.039, 0.021, 0.005, 0.01, 
0.002, 0.004, -0.007, -0.015, 0.013, 0.016, 0.005, 0.011, -0.014, 
-0.001, -0.023, -0.005, -0.013, -0.03, 0.016, -0.036, 0.014, 
-0.001, -0.031, -0.007, -0.002, 0.025, -0.019, 0.003, -0.026, 
-0.017, 0.028, 0.003, -0.002, -0.006, 0.015, 0.02, -0.009, -0.008, 
0.001, -0.004, -0.029, -0.024, 0.004, 0.012, -0.015, 0.02, -0.017, 
0.008, 0.01, -0.003, 0.019, 0.004, 0.006, 0, 0.021, -0.008, -0.014, 
0.019, -0.012, -0.015, -0.004, 0.012, -0.011, -0.002, -0.013, 
-0.004, -0.004, -0.016, -0.022, 0.025, 0.007, -0.004, 0.029, 
-0.005, -0.008, -0.004, -0.01, -0.044, -0.059, -0.068, 0.011, 
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-0.002, -0.072, -0.03, 0.009, 0.027, 0.014, 0.015, -0.002, -0.005, 
-0.05, 0.002, -0.002, 0.002, 0.021, 0.007, 0.017, -0.021, 0.017, 
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0.019, -0.002, -0.011, -0.01, -0.002, 0.018, -0.015, 0.012, -0.024, 
0.002, -0.007, 0.007, 0.015, -0.014, 0.002, 0.01, 0.001, 0.013, 
0.008, 0.024, 0, 0.004, 0.011, 0.009, -0.008, 0.009, 0.01, -0.01, 
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0.024, -0.016, -0.006, 0.003, 0.025, 0.013, -0.003, 0, -0.03, 
0.015, 0.029, -0.028, -0.004, -0.006, -0.009, -0.039, 0, -0.016, 
-0.126, 0.036, -0.012, -0.042, 0.027, -0.023, -0.016, 0.023, 
-0.005, 0.048, -0.016, -0.017, 0.016, -0.083, -0.004, -0.032, 
0.008, 0.036, 0.027, 0.008, 0.007, -0.005, -0.046, 0.025, -0.036, 
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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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-0.047, -0.022, -0.01, 0.062, -0.004, -0.014, -0.017, 0.034, 
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)

Using the above data, I am training the below model:

model1<-neuralnet(realized1~ann_t1+ann_t2+ann_t3, data=df_1, hidden=3, act.fct = "logistic",linear.output = TRUE)

Then for the below data frame(df_2) I am making predictions using the trained model(model1):

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)

The predictions(y) code is as below:

y<-as.data.frame(predict(model1,df_2))

But last row gives the below error:

> 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

Why do I get this error and how can I fix this error? Although, I prefer using neuralnet package for ann, I am also okey with using another packages that will solve the problem.

I saw a few topics about that problem. But, I couldn't solve the problem by using them.

ı will be very glad for any help. Thanks a lot.

I get error with the predict itself Error in cbind(1, pred) %*% weights[[num_hidden_layers + 1]] : requires numeric/complex matrix/vector arguments — akrun
@akrun, sorry. I didn't understand what you wanted to mean. I think ,the error is not about the predict or df_2. I guess it is about model1. But I am not sure. — oercim
Using your input in model, it says Algorithm did not converge in 1 of 1 repetition(s) within the stepmax.. But, if I change the threshold from default value, it is working i.e. model1<-neuralnet(realized1~ann_t1+ann_t2+ann_t3, data=df_1, hidden=3, threshold = 0.05, act.fct = "logistic",linear.output = TRUE) and now the y<-as.data.frame(predict(model1,df_2)) is fine — akrun
Yes, the issue is that it didn't converge (for me) and the predict extracts the weights and it is NULL in your model1 i.e. model1$weights — akrun
Or even changing the threshold to threshold = 0.02 worked for me — akrun

akrun akrun · Accepted Answer · 2021-06-05T22:08:01

We can change the threshold value from the default 0.01 as the default value is causing model convergence issues. Based on the documentation for ?neuralnet

threshold - a numeric value specifying the threshold for the partial derivatives of the error function as stopping criteria.

Using that info, modify the value to another one i.e. 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 ...

Getting "requires numeric/complex matrix/vector arguments" error using neuralnet package - R

1 Answers