aGE.joint {aGE} | R Documentation |
aGE joint test
aGE.joint(Y, G, cov = NULL, model = c("gaussian", "binomial"), pow = c(1:6), n.perm = 1000, method = c("Simulation"), nonparaE = F, DF = 10)
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". |
p-values
{ 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') }