gibbs_cs_wish_tfd: Cross-sectional FoSR using a Gibbs sampler and Wishart prior

In gekim0519/tidyfunfun:

Description

Fitting function for function-on-scalar regression for cross-sectional data. This function estimates model parameters using a Gibbs sampler and estimates the residual covariance surface using a Wishart prior. As an alteration of 'refund::gibbs_cs_wish', this function takes in a dataframewith tfd column as an input.

Usage

gibbs_cs_wish_tfd(formula, Kt = 5, data = NULL, verbose = TRUE,
  N.iter = 5000, N.burn = 1000, alpha = 0.1, min.iter = 10,
  max.iter = 50, Aw = NULL, Bw = NULL, v = NULL, SEED = NULL)

Arguments

formula	a formula indicating the structure of the proposed model.
Kt	number of spline basis functions used to estimate coefficient functions
data	an data frame, list or environment containing the variables in the model. This includes the response variable of the formula which should be a tfd class.
verbose	logical defaulting to TRUE – should updates on progress be printed?
N.iter	number of iterations used in the Gibbs sampler
N.burn	number of iterations discarded as burn-in
alpha	tuning parameter balancing second-derivative penalty and zeroth-derivative penalty (alpha = 0 is all second-derivative penalty)
min.iter	minimum number of iterations
max.iter	maximum number of iterations
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
SEED	seed value to start the sampler; ensures reproducibility

Author(s)

Gaeun Kim [e-mail] and Jeff Goldsmith [e-mail]

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.

Examples

## Not run: 
library(ggplot2)
library(reshape2)
data(dti) 

dti.ols = ols_cs_tfd(cca ~ pasat, data = dti, Kt = 10)
gibbs_dti = gibbs_cs_fpca_tfd(cca ~ pasat, data = dti, Kt = 10, N.iter = 500, N.burn = 200)
gibbs_dti_wish = gibbs_dti_wish = gibbs_cs_wish_tfd(cca ~ pasat, data = dti, Kt = 10, N.iter = 500, N.burn = 200)
models = c("dti.ols", "gibbs_dti", "gibbs_dti_wish")
intercepts = sapply(models, function(u) get(u)$beta.hat[1,])
slopes = sapply(models, function(u) get(u)$beta.hat[2,])

## graph of estimated coefficient functions (intercept, slope)

plot.dat = melt(intercepts); colnames(plot.dat) = c("grid", "method", "value")
ggplot(plot.dat, aes(x = grid, y = value, group = method, color = method)) +
  geom_path() + theme_bw() + ylab("intercept")

plot.dat = melt(slopes); colnames(plot.dat) = c("grid", "method", "value")
ggplot(plot.dat, aes(x = grid, y = value, group = method, color = method)) +
  geom_path() + theme_bw() + ylab("slope")

## End(Not run)

gekim0519/tidyfunfun documentation built on Aug. 2, 2019, 5:18 a.m.