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).

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 simexreg
coef(object, ...)

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

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

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

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

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

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

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

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

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

# S3 method for simexreg
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

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.

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,

...

Details

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

Methods (by generic)

  • coef: Extract coefficients corrected by simexreg

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

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

  • formula: Extract the regression formula

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

  • predict: Predict with new data

  • model.frame: Extract the model frame

  • print: Print results of simexreg nicely

  • summary: Summarize results of simexreg nicely

  • print: Print summary of simexreg nicely

  • update: Update simexreg

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

Examples

if (FALSE) { 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") }