This function outputs plots and ggplot() objects of the functional weights found by the fnn.fit() model.
fnn.fnc(model, domain_range, covariate_scaling = F)
| model | A keras model as outputted by |
|---|---|
| domain_range | List of size k. Each element of the list is a 2-dimensional vector containing the upper and lower
bounds of the k-th functional weight. Must be the same covariates as input into |
| covariate_scaling | If True, then data will be internally scaled before model development. |
The following are returned:
FNC_Coefficients -- The estimated coefficients defining the basis expansion for each of the k functional weights.
saved_plot -- A list of size k of ggplot() objects.
No additional details for now.
# libraries library(fda) # loading data tecator = FNN::tecator # define the time points on which the functional predictor is observed. timepts = tecator$absorp.fdata$argvals # define the fourier basis nbasis = 29 spline_basis = create.fourier.basis(tecator$absorp.fdata$rangeval, nbasis) # convert the functional predictor into a fda object and getting deriv tecator_fd = Data2fd(timepts, t(tecator$absorp.fdata$data), spline_basis) tecator_deriv = deriv.fd(tecator_fd) tecator_deriv2 = deriv.fd(tecator_deriv) # Non functional covariate tecator_scalar = data.frame(water = tecator$y$Water) # Response tecator_resp = tecator$y$Fat # Getting data into right format tecator_data = array(dim = c(nbasis, length(tecator_resp), 3)) tecator_data[,,1] = tecator_fd$coefs tecator_data[,,2] = tecator_deriv$coefs tecator_data[,,3] = tecator_deriv2$coefs # Getting data ready to pass into function ind = 1:165 tec_data_train <- array(dim = c(nbasis, length(ind), 3)) tec_data_train = tecator_data[, ind, ] tecResp_train = tecator_resp[ind] scalar_train = data.frame(tecator_scalar[ind,1]) # Setting up network tecator_fnn = fnn.fit(resp = tecResp_train, func_cov = tec_data_train, scalar_cov = scalar_train, basis_choice = c("fourier", "fourier", "fourier"), num_basis = c(5, 5, 7), hidden_layers = 4, neurons_per_layer = c(64, 64, 64, 64), activations_in_layers = c("relu", "relu", "relu", "linear"), domain_range = list(c(850, 1050), c(850, 1050), c(850, 1050)), epochs = 300, learn_rate = 0.002) # Functional weights for this model est_func_weights = fnn.fnc(tecator_fnn, domain_range = list(c(850, 1050), c(850, 1050), c(850, 1050)))