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refund (version 0.1-1)

vb_cs_wish: Cross-sectional FoSR using Variational Bayes and Wishart prior

Description

Fitting function for function-on-scalar regression for cross-sectional data. This function estimates model parameters using VB and estimates the residual covariance surface using a Wishart prior.

Usage

vb_cs_wish(formula, data = NULL, verbose = TRUE, Kt = 5, alpha = 0.1,
  min.iter = 10, max.iter = 50, Aw = NULL, Bw = NULL, v = NULL)

Arguments

formula
a formula indicating the structure of the proposed model.
data
an optional data frame, list or environment containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which the function is called.
verbose
logical defaulting to TRUE -- should updates on progress be printed?
Kt
number of spline basis functions used to estimate coefficient functions
alpha
tuning parameter balancing second-derivative penalty and zeroth-derivative penalty (alpha = 0 is all second-derivative penalty)
min.iter
minimum number of interations of VB algorithm
max.iter
maximum number of interations of VB algorithm
Aw
hyperparameter for inverse gamma controlling variance of spline terms for population-level effects; if NULL, defaults to Kt/2.
Bw
hyperparameter for inverse gamma controlling variance of spline terms for population-level effects; if NULL, defaults to 1/2 tr(mu.q.betaof the model
v
hyperparameter for inverse Wishart prior on residual covariance; if NULL, Psi defaults to an FPCA decomposition of the residual covariance in which residuals are estimated based on an OLS fit of the model (note the "nugget effect" on this c

References

Goldsmith, J., Kitago, T. (Under Review). Assessing Systematic Effects of Stroke on Motor Control using Hierarchical Function-on-Scalar Regression.