MCID (version 0.1.0)

cv.pmcid: Selection of the tuning parameter for determining the MCID at the population level

Description

cv.pmcid returns the optimal tuning parameter \(\delta\) selected from a given grid by using k-fold cross-validation. The tuning parameter is selected for determining the MCID at the population level

Usage

cv.pmcid(x, y, delseq, k = 5, maxit = 100, tol = 0.01)

Arguments

x

a continuous variable denoting the outcome change of interest

y

a binary variable indicating the patient-reported outcome derived from the anchor question

delseq

a vector containing the candidate values for the tuning parameter \(\delta\), where \(\delta\) is used to control the difference between the 0-1 loss and the surrogate loss. We recommend selecting the possible values from the neighborhood of the standard deviation of x

k

the number of groups into which the data should be split to select the tuning parameter \(\delta\) by cross-validation. Defaults to 5

maxit

the maximum number of iterations. Defaults to 100

tol

the convergence tolerance. Defaults to 0.01

Value

a list including the selected tuning parameter and the value of the corresponding target function

Examples

# NOT RUN {
n <- 500
deltaseq <- seq(0.1, 1, 0.1)
a <- 0.2
b <- -0.1
p <- 0.5

set.seed(115)
y <- 2 * rbinom(n, 1, p) - 1
y_1 <- which(y == 1)
y_0 <- which(y == -1)
x <- c()
x[y_1] <- rnorm(length(y_1), a, 0.1)
x[y_0] <- rnorm(length(y_0), b, 0.1)

sel <- cv.pmcid(x = x, y = y, delseq = deltaseq, k = 5,
         maxit = 100, tol = 1e-02)
sel$'Selected delta'
sel$'Function value'

# }