misclassGLM (version 0.3.5)

misclassMlogit: Mlogit estimation under misclassified covariate

Description

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

Usage

misclassMlogit(
  Y,
  X,
  setM,
  P,
  na.action = na.omit,
  control = list(),
  par = NULL,
  baseoutcome = NULL,
  x = FALSE
)

Arguments

Y

a matrix of 0s and 1s, indicating the target class. This is the dependent variable.

X

a matrix containing the independent variables

setM

matrix, rows containing potential patterns for a misclassed (latent) covariate M in any coding for a categorical independent variable, e.g. dummy coding.

P

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

na.action

how to treat NAs

control

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

par

(optional) starting parameter vector

baseoutcome

reference outcome class

x

logical, add covariates matrix to result?

Examples

## simulate data
# \donttest{
data <- simulate_mlogit_dataset()
# }

## estimate model without misclassification error
# \donttest{
library(mlogit)
data2 <- mlogit.data(data, varying = NULL, choice = "Y", shape = "wide")
summary(mlogit(Y ~ 1 | X + M2, data2, reflevel = "3"))
# }

## estimate model with misclassification error
# \donttest{
summary(mlogit(Y ~ 1 | X + M, data2, reflevel = "3"))
# }

## estimate misclassification probabilities
# \donttest{
Pmodel <- glm(M2 ~ M + X, data = data, family = binomial("logit"))
summary(Pmodel)
# }

## construct a-posteriori probabilities from Pmodel
# \donttest{
P <- predict(Pmodel, newdata = data, type = "response")
P <- cbind(1 - P, P)
dimnames(P)[[2]] <- c("M0", "M1") ## speaking names
# }

## estimate misclassGLM
# \donttest{
Yneu <- matrix(rep.int(0, nrow(data) * 3), ncol = 3)
for (i in 1:nrow(data)) Yneu[i, data$Y[i]] <- 1
est <- misclassMlogit(Y = Yneu,
                      X = as.matrix(data[, 2, drop = FALSE]),
                      setM = matrix(c(0, 1), nrow = 2),
                      P = P)
summary(est)
# }

## and bootstrapping the results from dataset
if (FALSE) {
summary(boot.misclassMlogit(est,
                         Y = Yneu,
                         X = data.matrix(data[, 2, drop = FALSE]),
                         Pmodel = Pmodel,
                         PX = data,
                         repetitions = 100))
}