vb_concurrent: vb_concurrent

In jeff-goldsmith/vbvs.concurrent: Variational Bayes Variable Selection for the Functional Linear Concurrent Model

Description

Implements variational bayes for the linear functional concurrent model

Usage

vb_concurrent(formula, id.var = NULL, data = NULL, Kt = 5, Kp = 2,
  v1 = 100, standardized = FALSE, t.min = NULL, t.max = NULL)

Arguments

formula	formula for desired regression. should have form y ~ x1 + x2 + ... + x_k | t, where t is the variable that parameterized observed functions
id.var	variable giving subject ID vector
data	optional data frame
Kt	number of spline basis functions for coefficients and FPCs
Kp	number of FPCs to estimate
v1	tuning parameter; normal slab variance
standardized	logical; are covariates already standardized?
t.min	minimum value to be evaluated on the time domain (useful if data are sparse and / or irregular). if 'NULL', taken to be minium observed value.
t.max	maximum value to be evaluated on the time domain (useful if data are sparse and / or irregular). if 'NULL', taken to be minium observed value.

Author(s)

Jeff Goldsmith [e-mail]

jeff-goldsmith/vbvs.concurrent documentation built on Sept. 17, 2019, 2:26 p.m.