Fits a classical twin model for quantitative traits.
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. If this argument is missing, the reference level (i.e. the first level) will be interpreted as the dyzogitic twins
Optional. Variable name defining group for interaction analysis (e.g., gender)
If TRUE marginals of groups are asummed to be the same
Strata variable name
Weights matrix if needed by the chosen estimator. For use with Inverse Probability Weights
Character defining the type of analysis to be performed. Can be a subset of "aced" (additive genetic factors, common environmental factors, unique environmental factors, dominant genetic factors). Other choices are:
"0" (or "sat"): Saturated model where twin 1 and twin 2 within each twin pair may have a different marginal distribution.
"1" (or "flex","zyg"): Within twin pairs the marginal distribution is the same, but the marginal distribution may differ between MZ and DZ twins. A free correlation structure within MZ and DZ twins.
"2" (or "u", "eqmarg"): All individuals have the same marginals but a free correlation structure within MZ and DZ twins.
The default value is an additive polygenic model type="ace".
The name of the column in the dataset numbering the
twins (1,2). If it does not exist in data it will
automatically be created.
If TRUE a liability model is fitted. Note that if the right-hand-side of the formula is a factor, character vector, og logical variable, then the liability model is automatically chosen (wrapper of the bptwin function).
If non-zero (number of bins) a liability model is fitted.
Vector of variables from data that are not
specified in formula, to be added to data.frame of the SEM
Choice of estimator/model
Development argument
Control argument parsed on to the optimization routine
Control amount of messages shown
Additional arguments parsed on to lower-level functions
Returns an object of class twinlm.
bptwin, twinlm.time, twinlm.strata, twinsim
## Simulate data
set.seed(1)
d <- twinsim(1000,b1=c(1,-1),b2=c(),acde=c(1,1,0,1))
## E(y|z1,z2) = z1 - z2. var(A) = var(C) = var(E) = 1
## E.g to fit the data to an ACE-model without any confounders we simply write
ace <- twinlm(y ~ 1, data=d, DZ="DZ", zyg="zyg", id="id")
ace
#> Estimate Std. Error Z value Pr(>|z|)
#> y -0.019439 0.041817 -0.4649 0.642
#> sd(A) 0.902004 0.203739 4.4273 9.544e-06
#> sd(C) 1.137025 0.132852 8.5586 < 2.2e-16
#> sd(E) 1.728992 0.037408 46.2194 < 2.2e-16
#>
#> MZ-pairs DZ-pairs
#> 1000 1000
#>
#> Variance decomposition:
#> Estimate 2.5% 97.5%
#> A 0.15966 0.01867 0.30065
#> C 0.25370 0.13920 0.36820
#> E 0.58664 0.53677 0.63650
#>
#>
#> Estimate 2.5% 97.5%
#> Broad-sense heritability 0.15966 0.01867 0.30065
#>
#> Estimate 2.5% 97.5%
#> Correlation within MZ: 0.41336 0.36229 0.46196
#> Correlation within DZ: 0.33353 0.27933 0.38561
#>
#> 'log Lik.' -8779.953 (df=4)
#> AIC: 17567.91
#> BIC: 17590.31
## An AE-model could be fitted as
ae <- twinlm(y ~ 1, data=d, DZ="DZ", zyg="zyg", id="id", type="ae")
## LRT:
lava::compare(ae,ace)
#>
#> - Likelihood ratio test -
#>
#> data:
#> chisq = 17.207, df = 1, p-value = 3.353e-05
#> sample estimates:
#> log likelihood (model 1) log likelihood (model 2)
#> -8788.556 -8779.953
#>
## AIC
AIC(ae)-AIC(ace)
#> [1] 15.20656
## 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)
#> Estimate Std. Error Z value Pr(>|z|)
#> y -0.026049 0.034844 -0.7476 0.4547
#> sd(A) 1.066060 0.072890 14.6256 <2e-16
#> sd(C) 0.980740 0.073569 13.3309 <2e-16
#> sd(E) 0.979980 0.021887 44.7736 <2e-16
#> y~x1 1.006963 0.021900 45.9807 <2e-16
#> y~x2 -0.993802 0.021962 -45.2512 <2e-16
#>
#> MZ-pairs DZ-pairs
#> 1000 1000
#>
#> Variance decomposition:
#> Estimate 2.5% 97.5%
#> A 0.37156 0.27300 0.47012
#> C 0.31446 0.22643 0.40250
#> E 0.31398 0.28381 0.34414
#>
#>
#> Estimate 2.5% 97.5%
#> Broad-sense heritability 0.37156 0.27300 0.47012
#>
#> Estimate 2.5% 97.5%
#> Correlation within MZ: 0.68602 0.65467 0.71502
#> Correlation within DZ: 0.50024 0.45538 0.54257
#>
#> 'log Lik.' -7449.697 (df=6)
#> AIC: 14911.39
#> BIC: 14945
## Reduce Ex.Timings
## 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
#> Estimate Std. Error Z value Pr(>|z|)
#> y -0.026089 0.034847 -0.7487 0.4541
#> sd(A) 1.065529 0.072975 14.6012 <2e-16
#> sd(C) 0.981354 0.073598 13.3340 <2e-16
#> sd(E) 0.980018 0.021889 44.7711 <2e-16
#> y~x1 1.010637 0.030279 33.3778 <2e-16
#> y~x2 -0.993859 0.021964 -45.2498 <2e-16
#> y~x1:I(x2 < 0)TRUE -0.007626 0.043403 -0.1757 0.8605
#>
#> MZ-pairs DZ-pairs
#> 1000 1000
#>
#> Variance decomposition:
#> Estimate 2.5% 97.5%
#> A 0.37117 0.27253 0.46981
#> C 0.31484 0.22673 0.40296
#> E 0.31399 0.28382 0.34415
#>
#>
#> Estimate 2.5% 97.5%
#> Broad-sense heritability 0.37117 0.27253 0.46981
#>
#> Estimate 2.5% 97.5%
#> Correlation within MZ: 0.68601 0.65466 0.71501
#> Correlation within DZ: 0.50043 0.45553 0.54279
#>
#> 'log Lik.' -7449.682 (df=7)
#> AIC: 14913.36
#> BIC: 14952.57
## 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")