NNMoMo (version 0.1.0)

lcNN: Create a Lee-Carter Model with a Neural Network

Description

Utility function to initialize a NNMoMo object representing a neural network Lee-Carter model. Important settings of the neural network can be specified here.

Usage

lcNN(
  activation = c("linear", "tanh", "relu"),
  model_type = c("FCN", "LCN", "CNN"),
  loss_type = c("MSE", "Poisson"),
  q_e = 10,
  q_z1 = 10
)

Value

An object of class NNMoMo.

Arguments

activation

Sets the activation function of the \(k_t\)-subnet of the neural network. The first activation function is set by this parameter, the second is linear by default. "tanh" defines a tangens hyperbolicus activation function, "relu" is a Rectified Linear Unit function and "linear" sets a linear activation function.

model_type

Defines the feed-forward architecture of the neural network. "FCN" sets a fully connected network for the first layer of the \(k_t\)-subnet, "CNN" sets a convolutional neural network layer for this subset and "LCN" defines a locally connected network layer (note that the "LCN" option has some stability issues and therefore the use of "FCN" or "CNN" is recommended).

loss_type

Defines the type of the loss function. As a default value, "MSE" (Mean Squared Error) is set. This function minimizes the squared difference between the observed and estimated mortality rates \(m_{x,t}\). "Poisson" evaluates the likelihood of the observed deaths.

q_e

Sets the dimension of the embedding vectors for country and sex speratly. The dimension of the full embedding vector is hence 2*q_e. A value between 5 and 15 is recommended.

q_z1

Sets the dimension of the first layer of the \(k_t\)-subnet. A value between 5 and 50 is recommended.

Examples

# Sets up the neural network with the recommended settings:
model_1 <- lcNN()

# Sets up a fully customized netural network:
model_2 <- lcNN(activation = "tanh",
              model_type = "CNN",
              loss_type = "Poisson",
              q_e = 5,
              q_z1 = 25)

# These models can be used for fitting.