# plot.fosr.vs: Plot for Function-on Scalar Regression with variable... In yakuan-chen/fosr.vs: An implementation of Function-on-Scalar Regression with Variable Selection

## Description

Given a "`fosr.vs`" object, produces a figure of estimated coefficient functions.

## Usage

 ```1 2``` ```## S3 method for class 'fosr.vs' plot.fosr.vs(object, ...) ```

## Arguments

 `object` an object of class "`fosr.vs`". `...` additional arguments.

## Value

a figure of estimated coefficient functions.

## Author(s)

Yakuan Chen [email protected]

`fosr.vs`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32``` ```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.mcp = fosr.vs(Y~., data = data[1:80,], method="grMCP") plot(fit.mcp) ```