ordGEE2 {mgee2}

ordGEE2

Description

This function provides a naive approach to estimate the data without any correction or misclassification parameters. This may lead to biased estimation for response parameters.

Usage

ordGEE2(formula, id, data, corstr = "exchangeable", maxit = 50, tol = 0.001)

Arguments

formula

a formula object: a symbolic description of the model with error-prone response, error-prone covariates and other covariates.

id

a character object which records individual id in the data.

data

a dataframe or matrix of the observed data, including id, error-prone ordinal response error-prone ordinal covaritaes, other covariates.

corstr

a character object. The default value is "exchangeable", corresponding to the structure where the association between two paired responses is considered to be a constant. The other option is "log-linear" which indicates the log-linear association between two paired responses.

maxit

an integer which specifies the maximum number of iterations. The default is 50.

tol

a numeric object which indicates the tolerance threshold. The default is 1e-3.

Details

In addition to developing the package mgee2 to implement the methods of Chen et al.(2014) which accommodate misclassification effects in inferential procedures, we also implement the naive method of ignoring the feature of misclassification, and call the resulting function ordGEE2. This function can be used together with the precedingly described mgee2k or mgee2v to evaluate the impact of not addressing misclassification effects

Value

A list with component

beta

the coefficients in the order of 1) all non-baseline levels for response, 2) covariates - same order as specified in the formula

alpha

the coefficients for paired responses global odds ratios. Number of alpha coefficients corresponds to the paired responses odds ratio structure selected in "corstr"; when corstr="exchangeable", only one baseline alpha is fitted.

variance

variance-covariance matrix of all fitted parameters

convergence

a logical variable, TRUE if the model converges

iteration

number of iterations for the model to converge

differ

a list of difference of estimation for convergence

##

call

Function called

References

Z. Chen, G. Y. Yi, and C. Wu. Marginal analysis of longitudinal ordinal data with misclassification inboth response and covariates. Biometrical Journal, 56(1):69-85, Oct. 2014

Xu, Yuliang, Shuo Shuo Liu, and Y. Yi Grace. 2021. “mgee2: An R Package for Marginal Analysis of Longitudinal Ordinal Data with Misclassified Responses and Covariates.” The R Journal 13 (2): 419.

Examples

  data(obs1)
  obs1$Y <- as.factor(obs1$Y)
  obs1$X <- as.factor(obs1$X)
  obs1$visit <- as.factor(obs1$visit)
  obs1$treatment <- as.factor(obs1$treatment)
  obs1$S <- as.factor(obs1$S)
  obs1$W <- as.factor(obs1$W)
  naigee.fit = ordGEE2(formula = S~W+treatment+visit, id = "ID",
                       data = obs1, corstr = "exchangeable")