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refund (version 0.1-37)

vb_mult_wish: Multilevel 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. If prior hyperparameters are NULL they are estimated using the data.

Usage

vb_mult_wish(
  formula,
  data = NULL,
  verbose = TRUE,
  Kt = 5,
  alpha = 0.1,
  min.iter = 10,
  max.iter = 50,
  Az = NULL,
  Bz = NULL,
  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 iterations of VB algorithm

max.iter

maximum number of iterations of VB algorithm

Az

hyperparameter for inverse gamma controlling variance of spline terms for subject-level effects

Bz

hyperparameter for inverse gamma controlling variance of spline terms for subject-level effects

Aw

hyperparameter for inverse gamma controlling variance of spline terms for population-level effects

Bw

hyperparameter for inverse gamma controlling variance of spline terms for population-level effects

v

hyperparameter for inverse Wishart prior on residual covariance

Author

Jeff Goldsmith ajg2202@cumc.columbia.edu

References

Goldsmith, J., Kitago, T. (2016). Assessing Systematic Effects of Stroke on Motor Control using Hierarchical Function-on-Scalar Regression. Journal of the Royal Statistical Society: Series C, 65 215-236.