# mreg: To perform regression when discrete outcome variables are... In mreg: Fits Regression Models When the Outcome is Partially Missing

 mreg R Documentation

## 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",
iterlim = 100,
steptol = 1e-06,
na.action = NULL,
print.level = 2,
zero.start = FALSE
)


### Arguments

 formula This is a formula object e.g. Y~A+B to describe the location parameter data This is a data frame in which the variables are recorded patid In a longitudinal context this indexes the individuals. Note that the observations within each patient is assumed to be ordered according the timing of the observations. start.theta Optional vector of starting values for location and nuisance parameters modify We may wish to let the location depend on functions of the previous outcomes. Since these may be missing, we have to provide a function that can cope with all the potential values the outcome may have taken. See paper modify.p This is the dimension of the parameters associated with the modify function. mod.formula If we require other variables to interact with the previous observation we must create a set of variables to use. This is a one-sided formula e.g. ~X+Z, if we wanted to use those variables. density.name This is the density the increment in outcome is assumed to follow. It can be one of three values: negbin, poisson, geometric. link This is the link function g(\mu)=\eta. Where \eta is a linear combination of covariates, and \mu is the expected value of the outcome. The link function can be one of four values: identity, log, logit, hyper. iterlim The maximum number of iterations allowed for the nlm function. gradtol The parameter gradtol for the nlm function. steptol The parameter steptol for the nlm function na.action Parameter is not used: If any covariates are missing the function will return an error. print.level The parameter print.level for the nlm function. Set to the maximum, verbose level. zero.start It may be the case that it is known that the first value of the outcome was zero for all individuals, in which case invoke this TRUE/FALSE option.

### 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

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)


mreg documentation built on Oct. 26, 2023, 5:07 p.m.