# aGE

##### aGE interaction test

aGE interaction test

##### Usage

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

##### Arguments

- Y
a numeric vector of phenotype values

- G
a matrix 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 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
only have one option: "Simulation", also called 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"

- stepwise
an option to speed up the simulation procedure for large n.perm number in real-data application. Up to $n.perm=10^8$

##### 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(Y=exD$Y, G=exD$G, cov=exD$X, model='binomial', nonparaE=FALSE, stepwise=FALSE)
}
# }
```

*Documentation reproduced from package aGE, version 0.0.9, License: GPL-3*