refund (version 0.1-37)

fosr.vs: 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
)

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.

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.

Author

Yakuan Chen

References

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

See Also

{grpreg}

Examples

if (FALSE) {
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)
}