Package 'RcppDPR'

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: 'Gibbs' - full Bayesian inference with Gibbs sampler with a fixed n_k 'Adaptive_Gibbs' - adaptive version of Gibbs sample that automatically chooses n_k 'VB' - variational Bayes inference with a fixed n_k
...	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")

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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)

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