mets (version 0.2.4)

bptwin: Liability model for twin data

Description

Liability-threshold model for twin data

Usage

bptwin(formula, data, id, zyg, MZ, DZ, group = NULL,
    num = NULL, weight = NULL,
    biweight = function(x) 1/min(x), strata = NULL,
    messages = 1, control = list(trace = 0), type = "ace",
    eqmean = TRUE, pairsonly = FALSE, samecens = TRUE,
    allmarg = samecens & !is.null(weight), stderr = TRUE,
    robustvar = TRUE, p, indiv = FALSE, constrain,
    bound = FALSE, ...)

Arguments

formula

Formula specifying effects of covariates on the response.

data

data.frame with one observation pr row. In addition a column with the zygosity (DZ or MZ given as a factor) of each individual much be specified as well as a twin id variable giving a unique pair of numbers/factors to each twin pair.

id

The name of the column in the dataset containing the twin-id variable.

zyg

The name of the column in the dataset containing the zygosity variable.

MZ

Character defining the level in the zyg variable corresponding to the monozygotic twins. Optional if 'DZ' argument is used. If both MZ and DZ arguments are omitted the reference level (i.e. the first level) will be interpreted as the dyzogitic twi

DZ

Character defining the level in the zyg variable corresponding to the dyzogitic twins.

group

Optional. Variable name defining group for interaction analysis (e.g., gender)

num

Optional twin number variable

weight

Weight matrix if needed by the chosen estimator. For use with Inverse Probability Weights

biweight

Function defining the bivariate weight in each cluster

strata

Strata

messages

Control amount of messages shown

control

Control argument parsed on to the optimization routine. Starting values may be parsed as 'start'.

type

Character defining the type of analysis to be performed. Should be a subset of "aced" (additive genetic factors, common environmental factors, unique environmental factors, dominant genetic factors).

eqmean

Equal means (with type="u")?

pairsonly

Include complete pairs only?

stderr

Should standard errors be calculated?

robustvar

If TRUE robust (sandwich) variance estimates of the variance are used

p

Parameter vector p in which to evaluate log-Likelihood and score function

indiv

If TRUE the score and log-Likelihood contribution of each twin-pair

constrain

Development argument

samecens

Same censoring

allmarg

Should all marginal terms be included

bound

Development argument

...

Additional arguments to lower level functions

Examples

data(twinstut)
b0 <- bptwin(stutter~sex,
             data=droplevels(subset(twinstut,zyg%in%c("mz","dz"))),
             id="tvparnr",zyg="zyg",DZ="dz",type="ae")
summary(b0)