bptwin | R Documentation |
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 <= 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 twin-id 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 log-Likelihood and score function |
indiv |
If TRUE the score and log-Likelihood contribution of each twin-pair |
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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