jomo (version 2.6-2)

jomo.coxph.MCMCchain: coxph Compatible JM Imputation - A tool to check convergence of the MCMC

Description

This function is similar to the jomo.coxph function, but it returns the values of all the parameters in the model at each step of the MCMC instead of the imputations. It is useful to check the convergence of the MCMC sampler.

Usage

jomo.coxph.MCMCchain(formula, data, beta.start = NULL, l1cov.start = NULL,
                 l1cov.prior = NULL, nburn = 1000, start.imp = NULL,
                 betaY.start = NULL, output = 1, out.iter = 10)

Arguments

formula

an object of class formula: a symbolic description of the model to be fitted. It is possible to include in this formula interactions (through symbols '*' and '

data

A data.frame containing all the variables to include in the imputation model. Columns related to continuous variables have to be numeric and columns related to binary/categorical variables have to be factors.

beta.start

Starting value for beta, the vector(s) of fixed effects for the joint model for the covariates. For each n-category variable we have a fixed effect parameter for each of the n-1 latent normals. The default is a matrix of zeros.

l1cov.start

Starting value of the level-1 covariance matrix of the joint model for the covariates. Dimension of this square matrix is equal to the number of covariates (continuous plus latent normals) in the imputation model. The default is the identity matrix.

l1cov.prior

Scale matrix for the inverse-Wishart prior for the covariance matrix. The default is the identity matrix.

betaY.start

Starting value for betaY, the vector of fixed effects for the substantive analysis model. The default is the complete records estimate.

nburn

Number of burn in iterations. Default is 1000.

output

When set to any value different from 1 (default), no output is shown on screen at the end of the process.

out.iter

When set to K, every K iterations a message "Iteration number N*K completed" is printed on screen. Default is 10.

start.imp

Starting value for the missing data in the covariates of the substantive model. n-level categorical variables are substituted by n-1 latent normals.

Value

A list is returned; this contains the final imputed dataset (finimp) and several 3-dimensional matrices, containing all the values drawn for each parameter at each iteration: these are fixed effect parameters of the covariates beta (collectbeta), level 1 covariance matrices (collectomega), fixed effect estimates of the substantive model. If there are some categorical outcomes, a further output is included in the list, finimp.latnorm, containing the final state of the imputed dataset with the latent normal variables.

Examples

# NOT RUN {
    #Load data and define substantive model
    data(surdata)
    formula<-as.formula(Surv(time, status) ~ measure + sex + I(measure^2))
    
    #Run imputation
    
if (requireNamespace("survival", quietly = TRUE)) {
  library(survival)
  imp<-jomo.coxph.MCMCchain(formula,surdata, nburn = 100)
  }
    
# }