GenABEL (version 1.8-0)

PGC: Polynomial genomic control

Description

This function estimates the genomic controls for different models and degrees of freedom, using polinomial function. Polinomial coefficients are estimated by optimizing different error functions: regress, median, ks.test or group regress.

Usage

PGC(data, method = "group_regress", p, df, pol.d = 3, plot = TRUE, index.filter = NULL, start.corr = FALSE, proportion = 1, n_quiantile = 5, title_name = "Lambda", type_of_plot = "plot", lmax = NULL, color = "red")

Arguments

data

Input vector of Chi square statistic

method

Function of error to be optimized. Can be "regress", "median", "ks.test" or "group_regress"

p

Input vector of allele frequencies

df

Number of degrees of freedom

pol.d

The degree of polinomial function

plot

If TRUE, plot of lambda will be produced

start.corr

For regress method use it only when you want to make calculations faster

index.filter

Index of variables in data vector, that will be used in analysis if zero - all variables will be used

proportion

The proportion of lowest P (Chi2) to be used when estimating the inflation factor Lambda for "regress" method only

n_quiantile

The number of groups for "group_regress" method

title_name

The title name for plot

type_of_plot

For developers only

lmax

The threshold for lambda for plotting (optional)

color

The color of the plot

Value

A list with elements

data

Output vector corrected Chi square statistic

b

Polinomial coefficients

Examples

require(GenABEL.data)
data(ge03d2)
ge03d2 <- ge03d2[seq(from=1,to=nids(ge03d2),by=2),seq(from=1,to=nsnps(ge03d2),by=3)]
qts <- mlreg(dm2~1,data=ge03d2,gtmode = "additive")
chi2.1df <- results(qts)$chi2.1df
s <- summary(ge03d2)
freq <- s$Q.2
result=PGC(data=chi2.1df,method="median",p=freq,df=1, pol.d=2, plot=TRUE, lmax=1.1,start.corr=FALSE)