cdf_dl {multipleDL}

Calculate conditional CDFs

Description

This functions calculates the conditional CDFs based on the fitted model and new data.

Usage

cdf_dl(mod, new.data, at.y = 0, se = TRUE)

Arguments

mod

the model

new.data

the new data

at.y

a numeric vector of cut-off points P(y <= at.y | new.data)

se

if confidence intervals needed (default = TRUE)

Value

A list containing the following components:

est

a vector of estimated condtional CDFs

se

a vector of estimated standard errors

lb

a vector of estimated lower bounds of 95% confidence intervals

ub

a vector of estimated upper bounds of 95% confidence intervals

Examples

#' @examples
## Multiple DLs
## generate a small example data: 3 sites with different lower and upper DLs
## lower DLs: site 1: - 0.2; site 2: 0.3; site 3: no lower DL
## upper DLs: site 1: no upper DL; site 2: 4; site 3: 3.5
## each site includes 100 subjects
n <- 100
x <- rnorm(n * 3)
e <- rnorm(n * 3)
y <- exp(x + e)
no_dl <- 1e6
data <- data.frame(y = y, x = x, subset = rep(c(1, 2, 3), each=n))
data$dl_l <- ifelse(data$subset == 1, 0.2, ifelse(data$subset == 2, 0.3, -no_dl))
data$dl_u <- ifelse(data$subset == 1, no_dl, ifelse(data$subset == 2, 4, 3.5))
data$delta_l <- ifelse(data$y >= data$dl_l, 1, 0)
data$delta_u <- ifelse(data$y <= data$dl_u, 1, 0)
data$z <- ifelse(data$delta_l == 0, data$dl_l, ifelse(data$delta_u == 0, data$dl_u, data$y))
# model
mod <- multipleDL(formula = z ~ x, data = data,
                 delta_lower = data$delta_l, delta_upper = data$delta_u, link='probit')
# new data
new.data <- data.frame(x = c(0, 1))
conditional_median <- quantile_dl(mod, new.data, probs = 0.5)
conditional_cdf <- cdf_dl(mod, new.data, at.y = 1.5) # P(y <= 1.5 | new.data)