Description Usage Arguments Value Examples
Gibbs Sampler for simulating draws of parameters for the Bayesian Piece Wise Polinomial model described on the thesis.
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Y |
Response vector of n binary observatios (integers 0,1 - vector of size n) Can be encoded as a factor a numeric vector. |
X |
Design matrix of n observations and d covariables (numeric - n*d) |
M |
M minus 1 is the degree of the polinomial (integer - M > 0) |
J |
Number of intervals in each dimention (integer - J > 1) |
K |
Order of continuity in the derivatives (integrer - K < M) |
precision_w |
If using the default sigmas for w, a diagonal matrix will be used. Precision controls its magnitude (numeric - precision > 0) |
draws |
N<c3><ba>mber of samples to draw from the Gibbs Sampler (integer - draw > 0) |
tau |
the initial position of nodes selected by the user. although<c2><b7> arbitraty they need to match the dimentions. (numeric - (J-1)*d) of size d) beta (numeric - matrix of size d*d) |
w_init |
Inital value for the Gibbs Sampler Chain (numeric - matrix of size N*d) |
mu_w_0 |
Prior Mean of Beta (numeric - matrix of size N*d) |
sigma_w_0_inv |
sigma_w_0_inv:= Prior Inverse if the Variance-Covariance Matrices of w (list - d elements, each element is a numeric matrix of size N*N) |
indep_terms |
Keeps the independent terms in the PWP expansion. Leaving those terms might lead to identificability errors. (logical) |
verb |
short for verbose, if TRUE, prints aditional information (logical) |
debug |
If TRUE, print even more info to help with debugging (logical) |
An object of the class "bpwpm" containing at the following components:
A data frame containing the Gibbs sampler simulation for beta
A list of d elements. Each one is a data frame containign the simulation of the w_j parameters for each dimetnion j.
The PWP Expansion for input matrix X and nodes selected on percentiles
Nodes used for training
Initial parameters
Initial parameters
Initial parameters
Number of dimentions
Logical. If independent terms are keept
A string that prints the basic information of the mode. Used for the summary function.
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