grmsem (version 1.1.0)

grmsem.fcoher: grmsem factorial co-heritability and co-environmentality estimation function.

Description

This function estimates factorial co-heritabilities and factorial co-environmentalities.

Usage

grmsem.fcoher(grmsem.out = NULL)

Arguments

grmsem.out

grmsem.fit or grmsem.stpar object. Default NULL.

Value

grmsem.fcoher returns an extended model.out dataframe, fcoher.model.out, with the following columns:

label

parameter label

estimates

estimated parameters

gradient

gradient

se

SE

Z

Z (Wald) of factor loading

p

p (Wald) of factor loading

Vi

squared factor loading, explained phenotypic variation by the factor

Vi.se

SE of squared factor loading

FCOHER

factorial co-heritability

FCOHER.se

SE of factorial co-heritability

FCOHER.Z

Z (wald) of factorial co-heritability

FCOHER.se

p (Wald) of factorial co-heritability

FCOENV

factorial co-environmentality

FCOENV.se

SE of factorial co-environmentality

FCOENV.Z

Z (wald) of factorial co-environmentality

FCOENV.se

p (Wald) of factorial co-environmentality

Details

The grmsem.fcoher function can be used to estimate factorial co-heritabilities and factorial co-environmentalities for models estimating latent variables (Cholesky, IP or IPC models), based on grmsem.fit or grmsem.stpar objects. The factorial co-heritability of a genetic factor m for trait t is the ratio of the genetic variance explained by factor m (A_mt) to the total genetic variance (A_t): A_mt / A_t. The factorial co-environmentality of a residual factor n for trait t is the ratio of the residual variance explained by factor n (E_nt) to the total residual variance (E_t): E_nt / E_t. All standard errors are derived with the Delta method.

Examples

# NOT RUN {
#(runtime should be less than one minute)
# }
# NOT RUN {
out <- grmsem.fit(ph.small, G.small, LogL = TRUE, estSE = TRUE)
grmsem.fcoher(out)
# }