misclassMlogit: Mlogit estimation under misclassified covariate

In misclassGLM: Computation of Generalized Linear Models with Misclassified Covariates Using Side Information

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

data <- simulate_mlogit_dataset()


## estimate model without misclassification error

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

summary(mlogit(Y ~ 1 | X + M, data2, reflevel = "3"))


## 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

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
## Not run: 
summary(boot.misclassMlogit(est,
                         Y = Yneu,
                         X = data.matrix(data[, 2, drop = FALSE]),
                         Pmodel = Pmodel,
                         PX = data,
                         repetitions = 100))

## End(Not run)

misclassGLM documentation built on Nov. 19, 2023, 9:06 a.m.