Type:	Package
Title:	Single-Index Neural Network for Skewed Heavy-Tailed Data
Version:	0.1.1
Maintainer:	Qingyang Liu <rh8liuqy@gmail.com>
Description:	Provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. The residuals are assumed to follow a skewed T distribution, a skewed normal distribution, or a normal distribution. More details can be found at Liu, Huang, and Bai (2024) <doi:10.1016/j.csda.2024.108012>.
License:	GPL (≥ 3)
Encoding:	UTF-8
RdMacros:	Rdpack
SystemRequirements:	Python (>= 3.8.0); PyTorch (https://pytorch.org/); NumPy (https://numpy.org/); SciPy (https://scipy.org/); sklearn (https://scikit-learn.org/stable/);
RoxygenNote:	7.3.2
Imports:	reticulate (≥ 1.37.0), stats (≥ 4.3.0), Rdpack (≥ 2.6)
NeedsCompilation:	no
Packaged:	2025-01-07 04:07:56 UTC; Kevin_Liu
Author:	Qingyang Liu [aut, cre], Shijie Wang [aut], Ray Bai [aut], Dipankar Bandyopadhyay [aut]
Repository:	CRAN
Date/Publication:	2025-01-07 16:50:13 UTC

The 'DNNSIM' package.

Description

Provides a deep neural network model with a monotonic increasing single index function tailored for periodontal disease studies. The residuals are assumed to follow a skewed T distribution, a skewed normal distribution, or a normal distribution. More details can be found at Liu, Huang, and Bai (2024) doi:10.1016/j.csda.2024.108012.

Value

This is the summary page. No return value.

Author(s)

Maintainer: Qingyang Liu rh8liuqy@gmail.com (ORCID)

Authors:

• Shijie Wang shijiew.usc@gmail.com

• Ray Bai rbai@mailbox.sc.edu (ORCID)

• Dipankar Bandyopadhyay dbandyop@vcu.edu

Define and train the DNN-SIM model

Description

Define and train the DNN-SIM model

Usage

DNN_model(
  formula,
  data,
  model,
  num_epochs,
  verbatim = TRUE,
  CV = FALSE,
  CV_K = 10,
  bootstrap = FALSE,
  bootstrap_B = 1000,
  bootstrap_num_epochs = 100,
  U_new = FALSE,
  U_min = -4,
  U_max = 4,
  random_state = 100
)

Arguments

Details

The DNNSIM model is defined as:

Y = g(\mathbf{X} \boldsymbol{\beta}) + e.

The residuals e follow a skewed T distribution, skewed normal distribution, or normal distribution. The single index function g is assumed to be a monotonic increasing function.

Value

A list consisting of the point estimation, g function estimation (optional), cross-validation results (optional) and bootstrap results(optional).

References

Liu Q, Huang X, Bai R (2024). “Bayesian Modal Regression Based on Mixture Distributions.” Computational Statistics & Data Analysis, 108012. doi:10.1016/j.csda.2024.108012.

Examples

# check python module dependencies
if (reticulate::py_module_available("torch") &
    reticulate::py_module_available("numpy") &
    reticulate::py_module_available("sklearn") &
    reticulate::py_module_available("scipy")) {

  # set the random seed
  set.seed(100)

  # simulate some data
  df1 <- data_simulation(n=100,beta=c(1,1,1),w=0.3,
                         sigma=0.1,delta=10.0,seed=100)

  # the cross-validation and bootstrap takes a long time
  DNN_model_output <- DNN_model(y ~ X1 + X2 + X3 - 1,
                                data = df1,
                                model = "ST-GX-D",
                                num_epochs = 5,
                                verbatim = FALSE,
                                CV = TRUE,
                                CV_K = 2,
                                bootstrap = TRUE,
                                bootstrap_B = 2,
                                bootstrap_num_epochs = 5,
                                U_new = TRUE,
                                U_min = -4.0,
                                U_max = 4.0)
  print(DNN_model_output)
}

Simulate data for the DNN-SIM model

Description

Simulate data for the DNN-SIM model

Usage

data_simulation(n, beta, w, sigma, delta, seed)

Arguments

Details

This is a simple data generation function for a simulation study. All elements of the design matrix X follow a uniform distribution from -3.0 and 3.0 independently and identically. The true g function is the standard logistic function.

Value

a dataframe of the simulated response variable y and the design matrix X.

References

Liu Q, Huang X, Bai R (2024). “Bayesian Modal Regression Based on Mixture Distributions.” Computational Statistics & Data Analysis, 108012. doi:10.1016/j.csda.2024.108012.

Examples

# check python module dependencies
if (reticulate::py_module_available("torch") &
    reticulate::py_module_available("numpy") &
    reticulate::py_module_available("sklearn") &
    reticulate::py_module_available("scipy")) {
  df1 <- data_simulation(n=50,beta=c(1,1,1),w=0.3,
                         sigma=0.1,delta=4.0,seed=100)
  print(head(df1))
}