Title: | 'Rcpp' Implementation of Dirichlet Process Regression |
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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. |
Authors: | Mohammad Abu Gazala [cre, aut], Daniel Nachun [ctb], Ping Zeng [ctb] |
Maintainer: | Mohammad Abu Gazala <[email protected]> |
License: | GPL-3 |
Version: | 0.1.9 |
Built: | 2025-03-15 19:15:38 UTC |
Source: | CRAN |
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
fit_model( y, w, x, rotate_variables = FALSE, covariance_matrix = NULL, fitting_method = "VB", ... )
fit_model( y, w, x, rotate_variables = FALSE, covariance_matrix = NULL, fitting_method = "VB", ... )
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. |
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)
returns an object of class 'DPR_Model'
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")
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
## S3 method for class 'DPR_Model' predict(object, newdata, ...)
## S3 method for class 'DPR_Model' predict(object, newdata, ...)
object |
an object of class DPR_Model |
newdata |
Numeric matrix representing the input to the model |
... |
ignored args. |
returns Numeric vector of predictions
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)
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)