Liability-threshold model for twin data
bptwin(
x,
data,
id,
zyg,
DZ,
group = NULL,
num = NULL,
weights = NULL,
weights.fun = function(x) ifelse(any(x Formula specifying effects of covariates on the response.
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.
The name of the column in the dataset containing the twin-id variable.
The name of the column in the dataset containing the zygosity variable.
Character defining the level in the zyg variable corresponding to the dyzogitic twins.
Optional. Variable name defining group for interaction analysis (e.g., gender)
Optional twin number variable
Weight matrix if needed by the chosen estimator (IPCW)
Function defining a single weight each individual/cluster
Strata
Control amount of messages shown
Control argument parsed on to the optimization routine. Starting values may be parsed as 'start'.
Character defining the type of analysis to be performed. Should be a subset of "acde" (additive genetic factors, common environmental factors, dominant genetic factors, unique environmental factors).
Equal means (with type="cor")?
Include complete pairs only?
Same censoring
Should all marginal terms be included
Should standard errors be calculated?
If TRUE robust (sandwich) variance estimates of the variance are used
Parameter vector p in which to evaluate log-Likelihood and score function
If TRUE the score and log-Likelihood contribution of each twin-pair
Development argument
Link function for variance parameters
Additional arguments to lower level functions
Klaus K. Holst
twinlm, twinlm.time, twinlm.strata, twinsim
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)
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