simexreg: Simulation and Extrapolation for Measurement Error Correction

In LindaValeri/CMAverse: Causal Mediation Analysis

Simulation and Extrapolation for Measurement Error Correction

Description

simexreg is used to correct a regression object with a variable measured with error via SIMEX by Cook et al. (1994) and Küchenhoff et al. (2006).

Usage

simexreg(
  reg = NULL,
  formula = NULL,
  data = NULL,
  weights = NULL,
  MEvariable = NULL,
  MEvartype = NULL,
  MEerror = NULL,
  variance = FALSE,
  lambda = c(0.5, 1, 1.5, 2),
  B = 200
)

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

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

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

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

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

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

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

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

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

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

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

Arguments

reg	naive regression object. See Details.
formula	regression formula
data	new dataset for reg
MEvariable	variable measured with error
MEvartype	type of the variable measured with error. Can be continuous or categorical (first 3 letters are enough).
MEerror	the standard deviation of the measurement error (when MEvartype is continuous) or the misclassification matrix (when MEvartype is categorical).
variance	a logical value. If TRUE, estimate the var-cov matrix of coefficients through Jackknife. Default is FALSE.
lambda	a vector of lambdas for SIMEX. Default is c(0.5, 1, 1.5, 2).
B	number of simulations for SIMEX. Default is 200.
object	an object of class simexreg
...	additional arguments
x	an object of class simexreg
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 in the regression formula, an object of class simexreg is returned:

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

...

Methods (by generic)

• coef(simexreg): Extract coefficients corrected by simexreg

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

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

• formula(simexreg): Extract the regression formula

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

• predict(simexreg): Predict with new data

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

• print(simexreg): Print results of simexreg nicely

• summary(simexreg): Summarize results of simexreg nicely

• update(simexreg): Update simexreg

Functions

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

References

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

Cook JR, Stefanski LA (1994). Simulation-extrapolation estimation in parametric measurement error models. Journal of the American Statistical Association, 89(428): 1314 - 1328.

Küchenhoff H, Mwalili SM, Lesaffre E (2006). A general method for dealing with misclassification in regression: the misclassification SIMEX. Biometrics. 62(1): 85 - 96.

Stefanski LA, Cook JR (1995). Simulation-extrapolation: the measurement error jackknife. Journal of the American Statistical Association. 90(432): 1247 - 56.

See Also

rcreg, cmsens, cmest.

Examples

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

# 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_simex <- simexreg(reg = reg_naive, data = data, 
MEvariable = "x2_error", MEvartype = "con", MEerror = 0.5, variance = TRUE)
coef(reg_simex)
vcov(reg_simex)
sigma(reg_simex)
formula(reg_simex)
family(reg_simex)
predict(reg_simex, newdata = data[1, ])
reg_simex_model <- model.frame(reg_simex)
reg_simex_update <- update(reg_simex, data = data, weights = rep(1, n))
reg_simex_summ <- summary(reg_simex)
                
# glm
n <- 1000
x1 <- rnorm(n, mean = 5, sd = 3)
x2_true <- sample(x = c(1:3), size = n, prob = c(0.2,0.3,0.5), replace = TRUE)
MEerror <- matrix(c(0.8,0.1,0.1,0.2,0.7,0.1,0.05,0.25,0.7), nrow = 3)
x2_error <- x2_true
for (j in 1:3) {
  x2_error[which(x2_error == c(1:3)[j])] <-
    sample(x = c(1:3), size = length(which(x2_error == c(1:3)[j])),
           prob = MEerror[, j], replace = TRUE)
}
x2_true <- as.factor(x2_true)
x2_error <- as.factor(x2_error)
x3 <- rnorm(n, mean = 2, sd = 1)
linearpred <- 1 + 0.3 * x1 - 1.5*(x2_true == 2) - 2.5*(x2_true == 3) - 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_simex <- simexreg(reg = reg_naive, data = data, 
MEvariable = "x2_error", MEerror = MEerror, variance = TRUE, MEvartype = "cat")

## End(Not run)
                  

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