aGE (version 0.0.9)

aGE.joint: aGE joint test

Description

aGE joint test

Usage

aGE.joint(Y, G, cov = NULL, model = c("gaussian", "binomial"),
  pow = c(1:6), n.perm = 1000, method = c("Simulation"),
  nonparaE = F, DF = 10)

Arguments

Y

a numeric vector of phenotype values

G

a matrix or data frame for all RVs in the test gene or genomic region. The order of rows must match the order of Y. Missing is imputed as 0.

cov

a matrix or data frane with first column as the environmental variable to be tested. The order of rows must match the order of Y.

model

"binomial" for binary traits or "gaussian" for quantitative traits.

pow

Gamma set used to build a family of tests, default=c(1:6) for rare variants

n.perm

number of simulation to calculate the p-values, default=1000. Can increase to higher value depending on the signficiance level.

method

'Simulation': Monte Carlo Method

nonparaE

"T": use cubic splines for the environmental variable to fit the model; "F": use a linear function of the environmental variable to fit the model

DF

degree of freedom to use in the cubic splines, default=10. This option only works when nonparaE is set to "T".

Value

p-values

Examples

# NOT RUN {
{
 set.seed(12345)
 phenotype <- c(rep(1,50),rep(0,50))
 genotype <- data.frame(g1=sample(c(rep(1,10),rep(0,90))),g2=sample(c(rep(1,5), rep(0,95))))
 covariates <- data.frame(Envir=rnorm(100), Age=rnorm(100,60,5))
 exD <- list(Y=phenotype, G=genotype, X=covariates)
 aGE.joint(Y=exD$Y, G=exD$G, cov=exD$X, model='binomial') 
 }
# }