Type:	Package
Title:	Fits Regression Models When the Outcome is Partially Missing
Version:	1.2.1
Date:	2023-10-26
Description:	Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.
License:	GPL (≥ 3)
Encoding:	UTF-8
Language:	en-GB
LazyData:	true
URL:	https://github.com/shug0131/mreg
RoxygenNote:	7.2.3
Suggests:	testthat (≥ 3.0.0)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2023-10-26 15:57:24 UTC; sjb277
Author:	Simon Bond [aut, cre], Vern Farewell [aut]
Maintainer:	Simon Bond <simon.bond7@nhs.net>
Repository:	CRAN
Date/Publication:	2023-10-26 16:30:04 UTC

mreg: Implements the techniques of exact likelihood when the discrete outcome can be missing in a regression mode

Description

Implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics, Submitted. The main function is mreg.

References

Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics

wrapper to evaluate a density function given by a character string as an argument

Description

wrapper to evaluate a density function given by a character string as an argument

Usage

densityfn(x, family, ...)

Arguments

Value

the value of the density

adds together two formula

Description

adds together two formula

Usage

form.add(formula1, formula2)

Arguments

Value

another formula

To perform regression when discrete outcome variables are missing

Description

This software was created for the paper referred to below. If a longitudinal data base has regularly updated explanatory variables, but whose outcome variable is only intermittently collected then we can still perform exact maximum likelihood estimation of a regression model if the outcome variable is discrete.

Usage

mreg(
  formula,
  data,
  patid,
  start.theta = NULL,
  modify = unity,
  modify.p = 0,
  mod.formula = ~1,
  density.name = "negbin",
  link = "log",
  iterlim = 100,
  gradtol = 1e-06,
  steptol = 1e-06,
  na.action = NULL,
  print.level = 2,
  zero.start = FALSE
)

Arguments

Value

It returns an object of class mreg which is similar to a lm object. It has print and summary methods to display the fitted parameters and standard errors.

References

Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics

See Also

print.mreg, summary.mreg, paper, unity

Examples

	data(public)
## Not run: 
mod1 <- mreg( damaged~offset(log(intervisit.time))+esr.init,
data=public,patid=ptno,print.level=2, iterlim=1000 )
mod.ncar <-mreg(damaged ~ offset(log(intervisit.time)) + esr.init +
         tender + effused + clinic.time, data = public, patid = ptno,
         modify = paper, modify.p = 5, mod.formula = ~art.dur.init,
       density.name = "negbin.ncar", iterlim = 1000, print.level = 2)

## End(Not run)

An example of a function to calculate the effect on the location that depends on previous outcome in an mreg model

Description

In a regression model one may want to let the linear predictor depend on previous values of the outcome variable in longitudinal data. When the outcome variable is missing we can still do this but we have to create a function that calculates a vector of linear predictors with one element for each of the possible preceding values of the outcome.

Usage

paper(x, y, mod.Z)

Arguments

Details

This is an example function that was used in the paper referred to below. The user may write their own function as long as it takes the arguments specified above and returns a vector the same length as x that will be added to the linear predictor. It is up to the user to ensure that their function identifies the correct column of mod.Z using indices ([] that correspond to the desired variables in mod.formula. If any function other than unity (which does nothing to the linear predictor) is used then it is not possible to produce fitted values or residuals.

Value

A numeric vector the same length as x that will be added to the linear predictor. It also has two subsidiary attributes: par.names names to be used to label the associated coefficients, and par.dim the length of this vector of coefficients.

See Also

unity mreg

Examples

## The function is currently defined as
function(x,y, mod.Z){
  #x is the imputed response
  #y is the set of parameters
 #mod.Z is a VECTOR/matrix of explanatory variables
 rad.type <- cut(x, breaks=c(-1,0,4,9,50))
 if( is.vector(mod.Z)){
   arthdur.first <- rep(mod.Z[2],length(x))
 }
 else{
   arthdur.first <- rep(mod.Z[1,2], length(x))
 }
 X <- model.matrix( ~rad.type+I(x==0):arthdur.first)
 structure( X[,-1, drop=FALSE]%*%y, par.names= colnames( X)[-1],par.dim=dim(X)[2]-1)
}

Prints the coefficients from an mreg object

Description

Prints the coefficients from an mreg object

Usage

## S3 method for class 'mreg'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

Value

It prints the coefficients from an mreg regression model.

See Also

mreg summary.mreg

Subset of Arthritis Data

Description

This is a subset of 100 patients and variables from the clinical data used in the paper below. Not to be used in publications without permission of the authors.

@format A data frame with 943 observations on the following 8 variables.

damaged

The number of radiologically damaged joints

intervisit.time

Time between clinic visits

esr.init

The first ESR measurement

art.dur.init

The duration of arthritis at first clinic visit

tender

Count of the number of tender joints

effused

Count of the number of effused joints

ptno

Anonymous patient number

clinic.time

Total time spend under clinical care

Usage

public

Format

An object of class data.frame with 943 rows and 8 columns.

Source

Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes

wrapper to generate random samples from a density function given by a character string as an argument

Description

wrapper to generate random samples from a density function given by a character string as an argument

Usage

randfn(n, family, ...)

Arguments

Value

the vector of samples

Prints a summary of an mreg object

Description

@description Similar to summary.glm, it prints the coefficients, standard errors, Wald tests, residuals (if available) and dispersion from an mreg object.

Usage

## S3 method for class 'mreg'
summary(
  object,
  digits = max(3, getOption("digits") - 3),
  symbolic.cor = object$symbolic.cor,
  signif.stars = getOption("show.signif.stars"),
  ...
)

Arguments

Value

prints to output a description of the fitted model.

See Also

mreg print.mreg

The default value of 'modify' function in 'mreg'

Description

If the location term in a regression model does not depend on any previously observed values of the outcome in a longitudinal data set, then we obtain simplification in our estimation procedure when the outcomes can be missing. Using the default value of unity for the argument modify in the mreg function does this.

Usage

unity(x, y, mod.Z)

Arguments

Details

This function is the default value for the argument modify for mreg. It does nothing to the linear predictor term. For this function unity there are no such covariates. A default value for mod.formula is ~1.

Value

A vector of zeroes the same length is the argument x.