fosr.vs: Function-on Scalar Regression with variable selection

In refund: Regression with Functional Data

Function-on Scalar Regression with variable selection

Description

Implements an iterative algorithm for function-on-scalar regression with variable selection by alternatively updating the coefficients and covariance structure.

Usage

fosr.vs(
  formula,
  data,
  nbasis = 10,
  method = c("ls", "grLasso", "grMCP", "grSCAD"),
  epsilon = 1e-05,
  max.iter_num = 100
)

Arguments

formula	an object of class "{formula}": an expression of the model to be fitted.
data	a data frame that contains the variables in the model.
nbasis	number of B-spline basis functions used.
method	group variable selection method to be used ("grLasso", "grMCP", "grSCAD" refer to group Lasso, group MCP and group SCAD, respectively) or "ls" for least squares estimation.
epsilon	the convergence criterion.
max.iter_num	maximum number of iterations.

Value

A fitted fosr.vs-object, which is a list with the following elements:

formula	an object of class "{formula}": an expression of the model to be fitted.
coefficients	the estimated coefficient functions.
fitted.values	the fitted curves.
residuals	the residual curves.
vcov	the estimated variance-covariance matrix when convergence is achieved.
method	group variable selection method to be used or "ls" for least squares estimation.

Author(s)

Yakuan Chen [e-mail]

References

Chen, Y., Goldsmith, J., and Ogden, T. (2016). Variable selection in function-on-scalar regression. Stat 5 88-101

See Also

{grpreg}

Examples

## Not run: 
set.seed(100)

I = 100
p = 20
D = 50
grid = seq(0, 1, length = D)

beta.true = matrix(0, p, D)
beta.true[1,] = sin(2*grid*pi)
beta.true[2,] = cos(2*grid*pi)
beta.true[3,] = 2

psi.true = matrix(NA, 2, D)
psi.true[1,] = sin(4*grid*pi)
psi.true[2,] = cos(4*grid*pi)
lambda = c(3,1)

set.seed(100)

X = matrix(rnorm(I*p), I, p)
C = cbind(rnorm(I, mean = 0, sd = lambda[1]), rnorm(I, mean = 0, sd = lambda[2]))

fixef = X%*%beta.true
pcaef = C %*% psi.true
error = matrix(rnorm(I*D), I, D)

Yi.true = fixef
Yi.pca = fixef + pcaef
Yi.obs = fixef + pcaef + error

data = as.data.frame(X)
data$Y = Yi.obs
fit.fosr.vs = fosr.vs(Y~., data = data, method="grMCP")
plot(fit.fosr.vs)

## End(Not run)

refund documentation built on Sept. 21, 2024, 1:07 a.m.