Liability-threshold model for twin data
Usage
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,
...
)Arguments
- x
Formula specifying effects of covariates on the response.
- data
data.framewith 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.
- 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 '
start'.- 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
See also
twinlm, twinlm.time, twinlm.strata, twinsim
Examples
data(twinstut)
b0 <- bptwin(stutter~sex,
data=droplevels(subset(twinstut,zyg%in%c("mz","dz"))),
id="tvparnr",zyg="zyg",DZ="dz",type="ae")
#> Warning: setting environment(<primitive function>) is not possible and trying it is deprecated
#> Warning: setting environment(<primitive function>) is not possible and trying it is deprecated
#> Warning: setting environment(<primitive function>) is not possible and trying it is deprecated
summary(b0)
#>
#> Estimate Std.Err Z p-value
#> (Intercept) -3.70371 0.24449 -15.1485 < 2.2e-16 ***
#> sexmale 0.83310 0.08255 10.0920 < 2.2e-16 ***
#> log(var(A)) 1.18278 0.17179 6.8851 5.774e-12 ***
#> ---
#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#>
#> Total MZ/DZ Complete pairs MZ/DZ
#> 8777/12511 3255/4058
#>
#> Estimate 2.5% 97.5%
#> A 0.76545 0.70500 0.82590
#> E 0.23455 0.17410 0.29500
#> MZ Tetrachoric Cor 0.76545 0.69793 0.81948
#> DZ Tetrachoric Cor 0.38272 0.35210 0.41253
#>
#> MZ:
#> Estimate 2.5% 97.5%
#> Concordance 0.01560 0.01273 0.01912
#> Casewise Concordance 0.42830 0.36248 0.49677
#> Marginal 0.03643 0.03294 0.04027
#> Rel.Recur.Risk 11.75741 9.77237 13.74246
#> log(OR) 3.52382 3.13466 3.91298
#> DZ:
#> Estimate 2.5% 97.5%
#> Concordance 0.00558 0.00465 0.00670
#> Casewise Concordance 0.15327 0.13749 0.17050
#> Marginal 0.03643 0.03294 0.04027
#> Rel.Recur.Risk 4.20744 3.78588 4.62900
#> log(OR) 1.69996 1.57262 1.82730
#>
#> Estimate 2.5% 97.5%
#> Broad-sense heritability 0.76545 0.70500 0.82590
#>