rcreg: Regression Calibration for Measurement Error Correction

In LindaValeri/CMAverse: Causal Mediation Analysis

Regression Calibration for Measurement Error Correction

Description

rcreg is used to correct a regression object with a continuous independent variable measured with error via regression calibration by Carroll et al. (1995).

Usage

rcreg(
  reg = NULL,
  formula = NULL,
  data = NULL,
  weights = NULL,
  MEvariable = NULL,
  MEerror = NULL,
  variance = FALSE,
  nboot = 400
)

## S3 method for class 'rcreg'
coef(object, ...)

## S3 method for class 'rcreg'
vcov(object, ...)

## S3 method for class 'rcreg'
sigma(object, ...)

## S3 method for class 'rcreg'
formula(x, ...)

## S3 method for class 'rcreg'
family(object, ...)

## S3 method for class 'rcreg'
predict(object, ...)

## S3 method for class 'rcreg'
model.frame(formula, ...)

## S3 method for class 'rcreg'
print(x, ...)

## S3 method for class 'rcreg'
summary(object, ...)

## S3 method for class 'summary.rcreg'
print(x, digits = 4, ...)

## S3 method for class 'rcreg'
update(object, ..., evaluate = TRUE)

Arguments

reg	naive regression object. See Details.
formula	regression formula
data	new dataset for reg
weights	new weights for reg
MEvariable	variable measured with error
MEerror	standard deviation of the measurement error
variance	a logical value. If TRUE, correct the var-cov matrix of coefficients by bootstrapping. Default is FALSE.
nboot	number of boots for correcting the var-cov matrix of coefficients. Default is 400.
object	an object of class rcreg
...	additional arguments
x	an object of class rcreg
digits	minimal number of significant digits. See print.default.
evaluate	a logical value. If TRUE, the updated call is evaluated. Default is TRUE.

Details

reg fitted by lm, glm (with family gaussian, binomial or poisson), multinom, polr, coxph or survreg is supported.

Value

If MEvariable is not in the regression formula, reg is returned. If MEvariable is a continuous independent variable in the regression formula, an object of class rcreg is returned:

call	the function call,
NAIVEreg	the naive regression object,
ME	a list of MEvariable, MEerror, variance and nboot,
RCcoef	coefficient estimates corrected by regression calibration,
RCsigma	the residual standard deviation of a linear regression object corrected by regression calibration,
RCvcov	the var-cov matrix of coefficients corrected by regression calibration,

...

Methods (by generic)

• coef(rcreg): Extract coefficients corrected by rcreg

• vcov(rcreg): Extract the var-cov matrix of coefficients corrected by rcreg

• sigma(rcreg): Extract the residual standard deviation of a linear regression object corrected by rcreg

• formula(rcreg): Extract the regression formula

• family(rcreg): Extract the family of a regression of class lm or glm

• predict(rcreg): Predict with new data

• model.frame(rcreg): Extract the model frame

• print(rcreg): Print results of rcreg nicely

• summary(rcreg): Summarize results of rcreg nicely

• update(rcreg): Update rcreg

Functions

• print(summary.rcreg): Print summary of rcreg nicely

References

Carrol RJ, Ruppert D, Stefanski LA, Crainiceanu C (2006). Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition. London: Chapman & Hall.

See Also

simexreg, cmsens, cmest

Examples

## Not run: 
rm(list=ls())
library(CMAverse)

# 2 boots are used for illustration
# lm
n <- 1000
x1 <- rnorm(n, mean = 5, sd = 3)
x2_true <- rnorm(n, mean = 2, sd = 1)
error1 <- rnorm(n, mean = 0, sd = 0.5)
x2_error <- x2_true + error1
x3 <- rbinom(n, size = 1, prob = 0.4)
y <- 1 + 2 * x1 + 4 * x2_true + 2 * x3  + rnorm(n, mean = 0, sd = 2)
data <- data.frame(x1 = x1, x2_true = x2_true, x2_error = x2_error,
                   x3 = x3, y = y)
reg_naive <- lm(y ~ x1 + x2_error + x3, data = data)
reg_true <- lm(y ~ x1 + x2_true + x3, data = data)
reg_rc <- rcreg(reg = reg_naive, data = data, MEvariable = "x2_error", MEerror = 0.5, 
variance = TRUE, nboot = 2)
coef(reg_rc)
vcov(reg_rc)
sigma(reg_rc)
formula(reg_rc)
family(reg_rc)
predict(reg_rc, newdata = data[1, ])
reg_rc_model <- model.frame(reg_rc)
reg_rc_update <- update(reg_rc, data = data, weights = rep(1, n))
reg_rc_summ <- summary(reg_rc)

#glm
n <- 1000
x1 <- rnorm(n, mean = 0, sd = 1)
x2_true <- rnorm(n, mean = 1, sd = 1)
error1 <- rnorm(n, mean = 0, sd = 0.5)
x2_error <- x2_true + error1
x3 <- rbinom(n, size = 1, prob = 0.4)
linearpred <- 1 + 0.3 * x1 - 0.5 * x2_true - 0.2 * x3
py <- exp(linearpred) / (1 + exp(linearpred))
y <- rbinom(n, size = 1, prob = py)
data <- data.frame(x1 = x1, x2_true = x2_true, x2_error = x2_error,
                   x3 = x3, y = y)
reg_naive <- glm(y ~ x1 + x2_error + x3, data = data, family = binomial("logit"))
reg_true <- glm(y ~ x1 + x2_true + x3, data = data, family = binomial("logit"))
reg_rc <- rcreg(reg = reg_naive, data = data, 
MEvariable = "x2_error", MEerror = 0.5, variance = TRUE, nboot = 2)

# multinom
n <- 1000
x1 <- rnorm(n, mean = 0, sd = 1)
x2_true <- rnorm(n, mean = 1, sd = 1)
error1 <- rnorm(n, mean = 0, sd = 0.5)
x2_error <- x2_true + error1
x3 <- rbinom(n, size = 1, prob = 0.4)
linearpred1 <- 1 + 0.3 * x1 - 0.5 * x2_true - 0.2 * x3
linearpred2 <- 2 + 1 * x1 - 2 * x2_true - 1 * x3
py2 <- exp(linearpred1) / (1 + exp(linearpred1) + exp(linearpred2))
py3 <- exp(linearpred2) / (1 + exp(linearpred1) + exp(linearpred2))
py1 <- 1 - py2 - py3
y <- sapply(1:n, function(x) sample(size = 1, c(1:3), prob = c(py1[x], py2[x], py3[x])))
data <- data.frame(x1 = x1, x2_true = x2_true, x2_error = x2_error,
                   x3 = x3, y = y)
reg_naive <- nnet::multinom(factor(y) ~ x1 + x2_error + x3, data = data)
reg_true <- nnet::multinom(factor(y) ~ x1 + x2_true + x3, data = data)
reg_rc <- rcreg(reg = reg_naive, data = data, MEvariable = "x2_error", MEerror = 0.5, 
variance = TRUE, nboot = 2)

## End(Not run)                

LindaValeri/CMAverse documentation built on June 15, 2024, 2:04 p.m.