| Type: | Package |
| Title: | 'Rcpp' Implementation of Dirichlet Process Regression |
| Version: | 0.1.10 |
| Description: | 'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | Rcpp (≥ 1.0.13) |
| LinkingTo: | Rcpp, RcppArmadillo, RcppGSL |
| Suggests: | testthat (≥ 3.0.0), snpStats |
| Config/testthat/edition: | 3 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | yes |
| Packaged: | 2025-03-19 04:02:14 UTC; moe |
| Author: | Mohammad Abu Gazala [cre, aut], Daniel Nachun [ctb], Ping Zeng [ctb] |
| Maintainer: | Mohammad Abu Gazala <abugazalamohammad@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2025-03-19 15:00:08 UTC |
Fit Dirichlet Process Regression model
Description
Fit a Dirichlet Process Regression model using a specified fitting method. Outcome (y) should be Gaussian and scaled and centered; predictors (x) and covariates (w) should also be scaled and centered but may be of any distribution
Usage
fit_model(
y,
w,
x,
rotate_variables = FALSE,
covariance_matrix = NULL,
fitting_method = "VB",
...
)
Arguments
y |
Numeric vector of outcome |
w |
Numeric matrix of covariates (default = rep(1, length(y))) |
x |
Numeric matrix of predictors |
rotate_variables |
Logical value indicating whether to rotate y, w and x using covariance_matrix (default = FALSE) |
covariance_matrix |
Numeric sample covariance matrix used for rotation of y, w and x - if NULL and rotate_variables is TRUE then the sample covariance matrix is computed from x |
fitting_method |
Character string indicating the method used for fitting the data - possible values are:
|
... |
arguments to pass through to internal methods. |
Details
fit_model() can pass a number of additional parameters to the different fitting methods. These parameters are used for all modes:
n_k: number of mixture components in scale mixture of normals prior (default = 4)
l_min: minimum value of log-likelihood for initial parameter search (default = 1e-7, only modify if you know what you are doing)
l_max: maximum value of log-likelihood for initial parameter search (default = 1e5, only modify if you know what you are doing)
n_regions: number of regions over which to search for maximum log-likelihood (default = 10, only modify if you know what you are doing)
These parameters are only used for the Gibbs and Adaptive Gibbs modes:
w_step: number of burn-in steps for Gibbs sampler (default = 1000)
s_step: number of inference steps for Gibbs sampler (default = 1000)
m_n_k: maximum number of mixture components in scale mixture of normals prior (default = 6, Adaptive Gibbs only)
Value
returns an object of class 'DPR_Model'
Examples
file_path_x <- system.file("extdata", "data/in/x.rds", package = "RcppDPR")
file_path_y <- system.file("extdata", "data/in/y.rds", package = "RcppDPR")
file_path_w <- system.file("extdata", "data/in/w.rds", package = "RcppDPR")
x = readRDS(file_path_x)
y = readRDS(file_path_y)
w = readRDS(file_path_w)
dpr_model <- fit_model(y, w, x, fitting_method = "VB")
Use a DPR model to predict results from new data
Description
Use a DPR model to predict results from new data
Usage
## S3 method for class 'DPR_Model'
predict(object, newdata, ...)
Arguments
object |
an object of class DPR_Model |
newdata |
Numeric matrix representing the input to the model |
... |
ignored args. |
Value
returns Numeric vector of predictions
Examples
n <- 500
p <- 10775
file_path_x <- system.file("extdata", "data/in/x.rds", package = "RcppDPR")
file_path_y <- system.file("extdata", "data/in/y.rds", package = "RcppDPR")
file_path_w <- system.file("extdata", "data/in/w.rds", package = "RcppDPR")
x = readRDS(file_path_x)
y = readRDS(file_path_y)
w = readRDS(file_path_w)
dpr_model <- fit_model(y, w, x, fitting_method = "VB")
new_x <- matrix(rnorm(n = n * p, mean = 0, sd = 1), nrow = n, ncol = p)
new_y <- predict(dpr_model, new_x)