twinlm: Classic twin model for quantitative traits
In mets: Analysis of Multivariate Event Times

Description Usage Arguments Value Author(s) See Also Examples

Fits a classical twin model for quantitative traits.

twinlm(formula, data, id, zyg, DZ, group = NULL, group.equal = FALSE,
  strata = NULL, weight = NULL, type = c("ace"), twinnum = "twinnum",
  binary = FALSE, keep = weight, estimator = "gaussian",
  constrain = TRUE, control = list(), messages = 1, ...)

`formula`	Formula specifying effects of covariates on the response
`data`	`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
`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. 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 `data` it will automatically be created.
`binary`	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).
`keep`	Vector of variables from `data` that are not specified in `formula`, to be added to data.frame of the SEM
`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 lower-level functions

Returns an object of class twinlm.

Klaus K. Holst

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
## 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)
## 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)