Train model using group exposed to treatment with compliance as binary outcome variable and covariates.
deep_complier_mod(
complier.formula,
exp.data,
treat.var,
algorithm = "adam",
hidden.layer = c(2, 2),
hidden_activation = "relu",
ID = NULL,
epoch = 10,
verbose = 1,
batch_size = 32,
validation_split = NULL,
patience = NULL,
dropout_rate = NULL
)deep.complier.mod model object
formula to fit compliance model (c ~ x) using complier variable and covariates
list object of experimental data.
string specifying the binary treatment variable
string for name of optimizer algorithm. Set to adam. other optimization algorithms available are sgd, rprop, adagrad.
vector specifying the hidden layers and the number of neurons in each layer.
string or vector for activation function used for hidden layers. Defaults to "relu".
string for name of identifier variable.
integer for number of epochs
1 to display model training information and learning curve plot. 0 to suppress messages and plots.
integer for batch size to split the training set. Defaults to 32.
double for proportion of training data to be split for validation.
integer for number of epochs with no improvement after which training will be stopped.
double or vector for proportion of hidden layer to drop out.