twinlm: Classic twin model for quantitative traits

In twinlm: Linear models for twin data

Description

Fits a classical twin model for quantitative traits.

Usage

  twinlm(formula, data, id, zyg, DZ, DZos, weight = NULL,
    type = c("ace"), twinnum = "twinnum", binary = FALSE,
    probitscale = 1, keep = weight, estimator = "gaussian",
    ...)

Arguments

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.
DZos	Optional. Character defining the level in the zyg variable corresponding to the oppposite sex dyzogitic twins.
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
probitscale	Defines the scale in the liability-model
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.
...	Additional arguments parsed on to lower-level functions

Value

Returns an object of class twinlm.

Author(s)

Klaus K. Holst

See Also

twinsim

Examples

## Simulate data
set.seed(1)
d <- twinsim(2000,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(y1 ~ 1, data=d, DZ="DZ", zyg="zyg", id="id")
ace
## An AE-model could be fitted as
ae <- twinlm(y1 ~ 1, data=d, DZ="DZ", zyg="zyg", id="id", type="ae")
## LRT:
compare(ae,ace)
## AIC
AIC(ae)-AIC(ace)
## To adjust for the covariates we simply alter the formula statement
ace2 <- twinlm(y1 ~ x11+x12, data=d, DZ="DZ", zyg="zyg", id="id", type="ace")
## Summary/GOF
summary(ace2)
## An interaction could be analyzed as:
ace3 <- twinlm(y1 ~ x11+x12 + x11:I(x12<0), data=d, DZ="DZ", zyg="zyg", id="id", type="ace")
## Categorical variables are also supported
d2 <- transform(d,x12cat=cut(x12,3,labels=c("Low","Med","High")))
ace4 <- twinlm(y1 ~ x11+x12cat, data=d2, DZ="DZ", zyg="zyg", id="id", type="ace")
## plot the model structure

plot(ace4)

twinlm documentation built on May 2, 2019, 4:42 p.m.