bptwin  R Documentation 
Liabilitythreshold model for twin data
bptwin( x, data, id, zyg, DZ, group = NULL, num = NULL, weights = NULL, weights.fun = function(x) ifelse(any(x <= 0), 0, max(x)), strata = NULL, messages = 1, control = list(trace = 0), type = "ace", eqmean = TRUE, pairs.only = FALSE, samecens = TRUE, allmarg = samecens & !is.null(weights), stderr = TRUE, robustvar = TRUE, p, indiv = FALSE, constrain, varlink, ... )
x 
Formula specifying effects of covariates on the response. 
data 

id 
The name of the column in the dataset containing the twinid variable. 
zyg 
The name of the column in the dataset containing the zygosity variable. 
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 
weights 
Weight matrix if needed by the chosen estimator (IPCW) 
weights.fun 
Function defining a single weight each individual/cluster 
strata 
Strata 
messages 
Control amount of messages shown 
control 
Control argument parsed on to the optimization routine. Starting values may be parsed as ' 
type 
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). 
eqmean 
Equal means (with type="cor")? 
pairs.only 
Include complete pairs only? 
samecens 
Same censoring 
allmarg 
Should all marginal terms be included 
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 logLikelihood and score function 
indiv 
If TRUE the score and logLikelihood contribution of each twinpair 
constrain 
Development argument 
varlink 
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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