Description Usage Arguments Value Author(s) See Also Examples
Fits a classical twin model for quantitative traits.
1 2 3 4 
formula 
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. If this argument is missing, the reference level (i.e. the first level) will be interpreted as the dyzogitic twins 
group 
Optional. Variable name defining group for interaction analysis (e.g., gender) 
group.equal 
If TRUE marginals of groups are asummed to be the same 
strata 
Strata variable name 
weight 
Weight matrix if needed by the chosen estimator. For use with Inverse Probability Weights 
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). 
twinnum 
The name of the column in the dataset numbering the
twins (1,2). If it does not exist in 
binary 
If 
keep 
Vector of variables from 
estimator 
Choice of estimator/model 
constrain 
Development argument 
control 
Control argument parsed on to the optimization routine 
messages 
Control amount of messages shown 
... 
Additional arguments parsed on to lowerlevel functions 
Returns an object of class twinlm
.
Klaus K. Holst
bptwin
, twinlm.time
, twinlm.strata
, twinsim
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  ## Simulate data
set.seed(1)
d < twinsim(1000,b1=c(1,1),b2=c(),acde=c(1,1,0,1))
## E(yz1,z2) = z1  z2. var(A) = var(C) = var(E) = 1
## E.g to fit the data to an ACEmodel without any confounders we simply write
ace < twinlm(y ~ 1, data=d, DZ="DZ", zyg="zyg", id="id")
ace
## An AEmodel could be fitted as
ae < twinlm(y ~ 1, data=d, DZ="DZ", zyg="zyg", id="id", type="ae")
## LRT:
lava::compare(ae,ace)
## AIC
AIC(ae)AIC(ace)
## To adjust for the covariates we simply alter the formula statement
ace2 < twinlm(y ~ x1+x2, data=d, DZ="DZ", zyg="zyg", id="id", type="ace")
## Summary/GOF
summary(ace2)
## An interaction could be analyzed as:
ace3 < twinlm(y ~ x1+x2 + x1:I(x2<0), data=d, DZ="DZ", zyg="zyg", id="id", type="ace")
ace3
## Categorical variables are also supported##'
d2 < transform(d,x2cat=cut(x2,3,labels=c("Low","Med","High")))
ace4 < twinlm(y ~ x1+x2cat, data=d2, DZ="DZ", zyg="zyg", id="id", type="ace")
## plot the model structure
## Not run:
plot(ace4)
## End(Not run)

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