simex (version 1.0)

mcsimex: The Misclassification SIMEX

Description

Implementation of the Misclassification SIMEX Algorithm as described by K�chenhoff, Mwalili and Lesaffre.

Usage

mcsimex(model
	, SIMEXvariable
	, mc.matrix
	, lambda = c(0.5,1,1.5,2)
	, B = 100
	, jackknife.estimation = "quad"
	, asymptotic = TRUE
	, fitting.method = "quad")

Arguments

model

The naive model, the misclassified variable must be a factor

mc.matrix

If one variable is misclassified it can be a matrix. If more than one variable is misclssified it must be a list of the misclassification matrices, names must match with the SIMEXvariabel names, column- and row-names must match with the factor levels. If

lambda

vector of exponents for the misclassification matrix (withou 0)

SIMEXvariable

vector of names of the variables for which the MCSIMEX-method should be applied

B

number of iterations for each lambda

fitting.method

linear, quadratic and loglinear 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, the option x =TRUE must be enabled in the naive model.

Value

• object of class MCSIMEX
• coefficients
  corrected coefficients of the MCSIMEX-model
• SIMEX.estimates
  the MCSIMEX-estimates of the coefficients for each lambda
• lambda
  the values of lambda
• model
  naive model
• mc.matrix
  the misclassification matrix
• B
  the number of iterations
• extrapolation
  model-object of the extrapolation step
• fitting.method
  the fitting method used in the exrapolation step
• SIMEXvariable
  name of the SIMEXvariables
• call
  the function call,
• variance.asymptotic
  the asymptotic variance estimates
• variance.jackknife
  the jackknife variance estimates
• extrapolation.variance
  the model-object of the variance extrapolation
• variance.jackknife.lambda
  data set for the extrapolation
• theta
  all estimated coefficients for each lambda and B
• ...

Details

if mc.matrix is a function the first argument of that function must be the whole dataset used in the naive model, the second argument must be the exponent (lambda) for the the misclassification. The function must return a data.frame containing the misclassified SIMEXvariable. An example can be found below. Asymptotic variance estimation is only implemented for lm and glm the loglinear fit has the form g(lambda,GAMMA) = exp(gamma0+gamma1*lambda). It is realized via the log() function. To avoid negaitve values the minimum +1 of the dataset is added and after the prediction later subtracted. exp(predict(...)) - min(data)-1

References

K�chenhoff, H., Mwalili, S. M. and Lesaffre (2005) E. A general method for dealing with misclassification in regression: the Misclassification SIMEX. Biometrics,in press

See Also

misclass, simex,refit

Examples

x <- rnorm(200,0,1.142)
z <- rnorm(200,0,2)
y <- factor(rbinom(200,1,(1/(1+exp(-1*(-2 + 1.5*x -0.5*z))))))
Pi <- matrix(data = c(0.9,0.1,0.3,0.7), nrow =2, byrow =FALSE)
dimnames(Pi) <- list(levels(y),levels(y))
ystar <- misclass(data.frame(y), list(y = Pi), k=1)[,1]
naive.model <- glm(ystar ~ x + z, family = binomial, x=TRUE, y =TRUE)
true.model  <- glm(y ~ x + z, family = binomial)
simex.model <- mcsimex(naive.model, mc.matrix = Pi, SIMEXvariable = "ystar")

op <-par(mfrow = c(2,3))
invisible(lapply(simex.model$theta, boxplot, notch=TRUE, outline =FALSE, names=c(0.5,1,1.5,2)))
plot(simex.model)
par(op)


## example for a function which can be supplied to the function mcsimex()
## "xm" is the variable which is to be misclassified

my.mc <- function(datas,k){
	xm <- datas$"xm"
	p1 <- matrix(data = c(0.75,0.25,0.25,0.75), nrow =2, byrow = FALSE)
	colnames(p1) <- levels(xm)
	rownames(p1) <- levels(xm)
	p0 <- matrix(data = c(0.8,0.2,0.2,0.8), nrow =2, byrow =FALSE)
	colnames(p0) <- levels(xm)
	rownames(p0) <- levels(xm)
	xm[datas$y=="1"] <- misclass(data.frame(xm=xm[datas$y=="1"]),list(xm=p1), k=k)[,1]
	xm[datas$y=="0"] <- misclass(data.frame(xm=xm[datas$y=="0"]),list(xm=p0), k=k)[,1]
	xm <- factor(xm)
	return(data.frame(xm))
	}