| Type: | Package |
| Title: | Regression Calibration Using Reliability Studies |
| Version: | 0.2.0 |
| Maintainer: | Bowen Liu <bowenliu@hsph.harvard.edu> |
| Description: | Implements regression calibration methods for correcting measurement error in regression models using external or internal reliability studies. Methods are described in Carroll, Ruppert, Stefanski, and Crainiceanu (2006) "Measurement Error in Nonlinear Models: A Modern Perspective" <doi:10.1201/9781420010138>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| Imports: | stats, sandwich |
| Suggests: | mgcv, knitr, rmarkdown |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.3.2 |
| URL: | https://lbw080526.github.io/RegCalReliab/, https://github.com/lbw080526/RegCalReliab |
| BugReports: | https://github.com/lbw080526/RegCalReliab/issues |
| NeedsCompilation: | no |
| Packaged: | 2025-09-29 20:44:26 UTC; liubowen |
| Author: | Bowen Liu [aut, cre, cph], Yu Lu [aut], Molin Wang [aut] |
| Repository: | CRAN |
| Date/Publication: | 2025-10-06 08:00:19 UTC |
Unified Regression Calibration Wrapper (External Reliability Study)
Description
A single formula interface for regression calibration in external reliability studies. The user simply specifies 'link = "linear"', '"logistic"', or '"log"', and the wrapper selects the appropriate model: * '"linear"' → Gaussian (identity link) * '"logistic"' → Binomial (logit link) * '"log"' → Poisson (log link)
Usage
RC_ExReliab(
formula,
main_data,
rep_data,
link = c("linear", "logistic", "log"),
return_details = FALSE
)
Arguments
formula |
A formula or character string such as 'Y ~ sbp(sbp2, sbp3) + chol(chol2, chol3) + age + weight'. Terms of the form 'var(rep1, rep2, ...)' are treated as error-prone exposures with replicates in 'rep_data'; other terms are treated as covariates W. |
main_data |
Data frame holding the outcome, error-prone exposures, and covariates. |
rep_data |
Data frame holding replicate columns referenced in 'formula'. |
link |
Character; one of '"linear"', '"logistic"', or '"log"'. |
return_details |
Logical; if 'TRUE', return parsed, prepared, and RC internals. |
Value
A list with: * 'uncorrected': naive regression estimates * 'corrected' : sandwich-corrected regression calibration estimates * optional 'details' if 'return_details = TRUE'
Examples
library(mgcv)
set.seed(123)
add_err <- function(v, sd = sqrt(0.4)) v + rnorm(length(v), 0, sd)
## --- Example 1: External 1Z 0W ---
x <- rnorm(3000)
z.main <- x[1:1500] + rnorm(1500, 0, sqrt(0.4))
z_rep <- rbind(
cbind(add_err(x[1501:2000]), add_err(x[1501:2000]), NA, NA),
cbind(add_err(x[2001:2400]), add_err(x[2001:2400]), add_err(x[2001:2400]), NA),
cbind(add_err(x[2401:3000]), add_err(x[2401:3000]),
add_err(x[2401:3000]), add_err(x[2401:3000]))
)
colnames(z_rep) <- paste0("z_", 1:4)
Y <- rbinom(1500, 1, plogis(-2.3 + log(1.5) * x[1:1500]))
main_data <- data.frame(Y = Y, z = z.main)
rep_data <- data.frame(z_rep, check.names = FALSE)
res1 <- RC_ExReliab(Y ~ z(z_1, z_2, z_3, z_4), main_data, rep_data, link = "logistic")
res1$corrected
## --- Example 2: External 1Z 1W ---
x <- rnorm(3000)
W_main <- rnorm(1500)
W_rep <- rnorm(1500)
z.main <- x[1:1500] + rnorm(1500, 0, sqrt(0.4))
z_rep <- rbind(
cbind(add_err(x[1501:2000]), add_err(x[1501:2000]), NA, NA),
cbind(add_err(x[2001:2400]), add_err(x[2001:2400]), add_err(x[2001:2400]), NA),
cbind(add_err(x[2401:3000]), add_err(x[2401:3000]),
add_err(x[2401:3000]), add_err(x[2401:3000]))
)
colnames(z_rep) <- paste0("z_", 1:4)
Y <- rbinom(1500, 1, plogis(-2.3 + log(1.5) * x[1:1500] + 0.5 * W_main))
main_data <- data.frame(Y = Y, z = z.main, W = W_main)
rep_data <- data.frame(z_rep, W = W_rep, check.names = FALSE)
res2 <- RC_ExReliab(Y ~ z(z_1, z_2, z_3, z_4) + W, main_data, rep_data, link = "logistic")
res2$corrected
## --- Example 3: External 2Z 0W ---
x <- mgcv::rmvn(3000, c(0, 0), matrix(c(1, 0.3, 0.3, 1), 2))
z.main <- x[1:1500, ] + matrix(rnorm(1500 * 2, 0, sqrt(0.4)), 1500, 2)
colnames(z.main) <- c("z1", "z2")
z1_rep <- rbind(
cbind(add_err(x[1501:2000, 1]), add_err(x[1501:2000, 1]), NA, NA),
cbind(add_err(x[2001:2400, 1]), add_err(x[2001:2400, 1]), add_err(x[2001:2400, 1]), NA),
cbind(add_err(x[2401:3000, 1]), add_err(x[2401:3000, 1]),
add_err(x[2401:3000, 1]), add_err(x[2401:3000, 1]))
)
colnames(z1_rep) <- paste0("z1_", 1:4)
z2_rep <- rbind(
cbind(add_err(x[1501:2000, 2]), add_err(x[1501:2000, 2]), NA, NA),
cbind(add_err(x[2001:2400, 2]), add_err(x[2001:2400, 2]), add_err(x[2001:2400, 2]), NA),
cbind(add_err(x[2401:3000, 2]), add_err(x[2401:3000, 2]),
add_err(x[2401:3000, 2]), add_err(x[2401:3000, 2]))
)
colnames(z2_rep) <- paste0("z2_", 1:4)
Y <- rbinom(1500, 1, plogis(-2.3 + log(1.5) * rowSums(x[1:1500, ])))
main_data <- data.frame(Y = Y, z1 = z.main[, 1], z2 = z.main[, 2])
rep_data <- data.frame(z1_rep, z2_rep, check.names = FALSE)
res3 <- RC_ExReliab(
Y ~ z1(z1_1, z1_2, z1_3, z1_4) + z2(z2_1, z2_2, z2_3, z2_4),
main_data, rep_data, link = "logistic"
)
res3$corrected
Unified Regression Calibration Wrapper (Internal Reliability Study)
Description
A single formula interface for regression calibration in internal reliability studies. The user simply specifies 'link = "linear"', '"logistic"', or '"log"', and the wrapper selects the appropriate model: * '"linear"' → Gaussian (identity link) * '"logistic"' → Binomial (logit link) * '"log"' → Poisson (log link)
Usage
RC_InReliab(
formula,
main_data,
link = c("linear", "logistic", "log"),
return_details = FALSE
)
Arguments
formula |
A formula or character string such as 'Y ~ sbp(sbp2, sbp3) + chol(chol2, chol3) + age + weight'. Terms of the form 'var(rep1, rep2, ...)' are treated as error-prone exposures with replicates in 'main_data'; other terms are treated as covariates W. |
main_data |
Data frame holding the outcome, replicate error-prone exposures, and any covariates. |
link |
Character; one of '"linear"', '"logistic"', or '"log"'. |
return_details |
Logical; if 'TRUE', return parsed, prepared, and RC internals. |
Value
A list with: * 'uncorrected': naive regression estimates * 'corrected' : sandwich-corrected regression calibration estimates * optional 'details' if 'return_details = TRUE'
Examples
set.seed(123)
add_err <- function(v, sd = sqrt(0.4)) v + rnorm(length(v), 0, sd)
## --- Example 1: Internal 1Z 0W ---
x <- rnorm(3000)
z <- rbind(
cbind(add_err(x[1:1500]), NA, NA, NA),
cbind(add_err(x[1501:2000]), add_err(x[1501:2000]), NA, NA),
cbind(add_err(x[2001:2400]), add_err(x[2001:2400]), add_err(x[2001:2400]), NA),
cbind(add_err(x[2401:3000]), add_err(x[2401:3000]),
add_err(x[2401:3000]), add_err(x[2401:3000]))
)
colnames(z) <- paste0("z_", 1:4)
Y <- rbinom(3000, 1, plogis(-2.65 + log(1.5) * x))
main_data <- data.frame(Y, z)
res1 <- RC_InReliab(Y ~ myz(z_1, z_2, z_3, z_4),
main_data = main_data,
link = "logistic")
res1$corrected
## --- Example 2: Internal 1Z 1W ---
x <- rnorm(3000)
W1 <- rnorm(3000)
z <- rbind(
cbind(add_err(x[1:1500]), NA, NA, NA),
cbind(add_err(x[1501:2000]), add_err(x[1501:2000]), NA, NA),
cbind(add_err(x[2001:2400]), add_err(x[2001:2400]), add_err(x[2001:2400]), NA),
cbind(add_err(x[2401:3000]), add_err(x[2401:3000]),
add_err(x[2401:3000]), add_err(x[2401:3000]))
)
colnames(z) <- paste0("z_", 1:4)
Y <- rbinom(3000, 1, plogis(-2.65 + log(1.5) * x + 0.5 * W1))
main_data <- data.frame(Y, z, W1)
res2 <- RC_InReliab(Y ~ myz(z_1, z_2, z_3, z_4) + W1,
main_data = main_data,
link = "logistic")
res2$corrected
## --- Example 3: Internal 2Z 0W ---
x <- mgcv::rmvn(3000, c(0,0), matrix(c(1,0.3,0.3,1), 2))
z1 <- rbind(
cbind(add_err(x[1:1500, 1]), NA, NA, NA),
cbind(add_err(x[1501:2000, 1]), add_err(x[1501:2000, 1]), NA, NA),
cbind(add_err(x[2001:2400, 1]), add_err(x[2001:2400, 1]), add_err(x[2001:2400, 1]), NA),
cbind(add_err(x[2401:3000, 1]), add_err(x[2401:3000, 1]),
add_err(x[2401:3000, 1]), add_err(x[2401:3000, 1]))
)
colnames(z1) <- paste0("z1_", 1:4)
z2 <- rbind(
cbind(add_err(x[1:1500, 2]), NA, NA, NA),
cbind(add_err(x[1501:2000, 2]), add_err(x[1501:2000, 2]), NA, NA),
cbind(add_err(x[2001:2400, 2]), add_err(x[2001:2400, 2]), add_err(x[2001:2400, 2]), NA),
cbind(add_err(x[2401:3000, 2]), add_err(x[2401:3000, 2]),
add_err(x[2401:3000, 2]), add_err(x[2401:3000, 2]))
)
colnames(z2) <- paste0("z2_", 1:4)
Y <- rbinom(3000, 1, plogis(-2.65 + log(1.5) * rowSums(x)))
main_data <- data.frame(Y, z1, z2)
res3 <- RC_InReliab(
Y ~ myz1(z1_1, z1_2, z1_3, z1_4) + myz2(z2_1, z2_2, z2_3, z2_4),
main_data = main_data,
link = "logistic")
res3$corrected