deepGLM {dnn}

Deep learning for the generalized linear model

Description

Fit generalized linear models (Gaussian, Binomial and Poisson) using deep learning neural network (DNN). The glm formula is specified by giving a symbolic description of the predictor and a description of the error distribution.

Usage

deepGlm(formula, model, family = c("gaussian", "binomial",
        "poisson"), data, epochs = 200, lr_rate = 1e-04,
         batch_size = 64, alpha = 0.7, lambda = 1, verbose = 0,
         weights = NULL, ...)

Arguments

formula

a formula expression as for other regression models. The response is usually an object for glm response variable. See the documentation for 'glm', 'lm' and 'formula' for details.

model

a deep neural network model, created by function dNNmodel().

family

a description of the error distribution and link function to be used in the model. This can be either a character string of 'gaussian', 'binomial', or 'poisson', naming a family function, or result of a call to a family function (See 'family' for details of family functions).)

data

a data.frame in which to interpret the variables named in the formula.

epochs

number of deep learning epochs, default is 200.

batch_size

batch size, default is 64. 'NaN' may be generated if batch size is too small and there is not event in a batch.

lr_rate

learning rate for the gradient descent algorithm, default is lr_rate = 1e-04.

weights

an optional vector of 'prior weights' to be used in the fitting process. Should be NULL or a numeric vector.

alpha

momentum rate for the gradient descent method, alpha takes value in [0, 1), default is alpha = 0.70.

lambda

L2 regularization parameter for deep learning.

verbose

verbose = 1 for print out verbose during the model fit, 0 for not print.

...

optional arguments

Details

See dNNmodel for details on how to specify a deep learning model.

The following parameters in 'dnnControl' will be used to control the model fit process.

'epochs': number of deep learning epochs, default is 30.

'verbose': verbose = 1 for print out verbose during the model fit, 0 for not print.

When the variance for covariance matrix X is too large, please use xbar = scale(x) to standardize X.

Value

An object of class "deepGlm" is returned. The deepGlm object contains the following list components:

x

Covariates for glm model

y

Object for glm model

model

dnn model

predictor

predictor score mu = f(x)

risk

risk score = exp(predictor)

Note

For glm models with Gaussian, Binomial and Poisson only

Author(s)

Chen, B. E.

References

Chollet, F. and Allaire J. J. (2017). Deep learning with R. Manning.

See Also

deepAFT, dNNmodel, predict.deepGlm, print.deepSurv, glm

Examples

## Example for deep learning for glm models
  set.seed(101)
### define model layers
  model = dNNmodel(units = c(4, 3, 1), activation = c("elu", "sigmoid", "sigmoid"), 
                   input_shape = 3)
  x = matrix(runif(15), nrow = 5, ncol = 3)
  y = exp(x[, 1] + rnorm(5))
  
  fit = deepGlm(y ~ x, model, family = "gaussian")