simex {simex}R Documentation

Simulation Extrapolation

Description

Implementation of the SIMEX Algorithm for measurement error models according to Cook and Stefanski

Usage

simex(model
        , SIMEXvariable
        , measurement.error
        , lambda = c(0.5,1,1.5,2)
        , B = 100, fitting.method = "quadratic"
        , jackknife.estimation = "quad"
        , asymptotic = TRUE)

Arguments

model the naive model
SIMEXvariable character or vector of characters containing the names of the variables with measurement error
measurement.error vector of standard deviations of the known measurement errors
lambda vector of lambdas for which the simulation step should be done (without 0)
B number of iterations for each lambda
fitting.method fitting method linear,quadratic,nonlinear are implemented. (first 4 letters are enough)
jackknife.estimation specifying the extrapolation method for jackknife variance estimation. Can be set to FALSE if it should not be performed
asymptotic logical, indicating if asymptotic variance estimation should be done, in the Naive model the option x = TRUE have to be set.

Details

nonlinear is implemented as described in Cook and Stefanski, but is numerically not stable. It is not advisable to use this feature. See fit.nls for details. If a nonlinear extrapolation is desired please use the refit function.

Asymptotic is only implemented for naive models of class lm or glm

Value

Returns an object of class SIMEX which contains:

coefficients the corrected coefficients of the SIMEX model,
SIMEX.estimates the estimates for every lambda,
model the naive model,
measurement.error the known error variances,
B the number of iterations,
extrapolation the model object of the extrapolation step,
fitting.method the fitting method of the extrapolation step,
residuals residuals,
fitted.values fitted values,
call the function call,
variance.jackknife the jackknife variance estimate,
extrapolation.variance the model object of the variance extrapolation,
variance.jackknife.lambda the data set for the extrapolation
variance.asymptotic the asymptotic variance estimates
theta estimates for every B and lambda

...

Author(s)

Wolfgang Lederer,wolfgang.lederer@googlemail.com

References

Cook, J.R. and Stefanski, L.A. (1994) Simulation-Extrapolation Estimation in Parametric Measurement error Models. Journal of American Statistical Assosiaction, 89, 1314 – 1328

Carroll, R.J., Küchenhoff,H., Lombard,F. and Stefanski L.A. (1996) Asymptotics for the SIMEX Estimator in Nonlinear Measurement Error Models. Journal of the American Statistical Association, 91, 242 – 250

Carrol, R.J., Ruppert, D. and Stefanski L.A. (1995). Measurement Error in Nonlinear Models. London: Chapman and Hall.

See Also

mcsimex for discreete data with misclassification, lm,glm, refit

Examples

# to test nonlinear extrapolation set.seed(3)
x <- rnorm(200,0,100)
u <- rnorm(200,0,25)
w <- x+u
y <- x +rnorm(200,0,9)
true.model <- lm(y~x) # True model
naive.model <- lm(y~w, x=TRUE)
simex.model <- simex(model = naive.model
        , SIMEXvariable = "w"
        , measurement.error= 25)
plot(x,y)
abline(true.model,col="darkblue")
abline(simex.model,col ="red")
abline(naive.model,col = "green")
legend(min(x),max(y),legend=c("True Model","SIMEX model","Naive Model")
        , col = c("darkblue","red","green"),lty=1)

plot(simex.model, mfrow = c(2,2))

[Package simex version 1.2 Index]