misclassGLM {misclassGLM}

GLM estimation under misclassified covariate

Description

misclassGLM computes estimator for a GLM with a misclassified covariate using additional side information on the misclassification process

Usage

misclassGLM(
  Y,
  X,
  setM,
  P,
  na.action = na.omit,
  family = gaussian(link = "identity"),
  control = list(),
  par = NULL,
  x = FALSE,
  robust = FALSE
)

Arguments

Y

a vector of integers or numerics. This is the dependent variable.

X

a matrix containing the independent variables.

setM

(optional) matrix, rows containing potential patterns for a misclassified (latent) covariate M in any coding for a categorical independent variable, e.g. dummy coding (default: Identity).

P

probabilities corresponding to each of the potential pattern conditional on the other covariates denoted in x.

na.action

how to treat NAs

family

a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See family for details of family functions.)

control

options for the optimization procedure (see optim, ucminf for options and details).

par

(optional) starting parameter vector

x

logical, add covariates matrix to result?

robust

logical, if true the computed asymptotic standard errors are replaced by their robust counterparts.

Examples

## simulate data

data <- simulate_GLM_dataset()


## estimate model without misclassification error

summary(lm(Y ~ X + M2, data))


## estimate model with misclassification error

summary(lm(Y ~ X + M, data))


## estimate misclassification probabilities

Pmodel <- glm(M2 ~ M + X, data = data, family = binomial("logit"))
summary(Pmodel)


## construct a-posteriori probabilities from Pmodel

P <- predict(Pmodel, newdata = data, type = "response")
P <- cbind(1 - P, P)
dimnames(P)[[2]] <- c("M0", "M1") ## speaking names


## estimate misclassGLM

est <- misclassGLM(Y = data$Y,
                   X = as.matrix(data[, 2, drop = FALSE]),
                   setM = matrix(c(0, 1), nrow = 2),
                   P = P)
summary(est)


## and bootstrapping the results from dataset
## Not run: 
  summary(boot.misclassGLM(est,
                           Y = data$Y,
                           X = data.matrix(data[, 2, drop = FALSE]),
                           Pmodel = Pmodel,
                           PX = data,
                           repetitions = 100))

## End(Not run)