R: Genomic control for various model of inheritance using VIF

VIFGC {GenABEL} | R Documentation

## Genomic control for various model of inheritance using VIF

### Description

This function estimates corrected statistic using genomic control for different models (recessive, dominant, additive etc.), using VIF. VIF coefficients are estimated by optimizing different error functions: regress, median and ks.test.

### Usage

      VIFGC(data, p, x, method = "regress", n,
        index.filter = NULL, proportion = 1, clust = 0,
        vart0 = 0, tmp = 0, CA = FALSE, p.table = 0,
        plot = TRUE, lmax = NULL, color = "red", F = NULL,
        K = NULL, type_of_plot = "plot", ladd = NULL)

### Arguments

`data` | Input vector of Chi square statistic
`method` | Function of error to be optimized. Can be "regress", "median" or "ks.test"
`p` | Input vector of allele frequencies
`x` | Model of inheritance (0 for recessive,0.5 for additive, 1 for dominant, also it could be arbitrary)
`index.filter` | Indexes for variables that will be use for analysis in data vector
`n` | The size of the sample
`proportion` | The proportion of lowest P (Chi2) to be used when estimating the inflation factor Lambda for "regress" method only
`plot` | If TRUE, plot of lambda will be produced
`type_of_plot` | For developers only
`lmax` | The threshold for lambda for plotting (optional)
`color` | The color of the plot
`F` | The estimation of F (optional)
`K` | The estimation of K (optional)
`ladd` | The estimation of lambda for additive model (optional)
`clust` | For developers only
`vart0` | For developers only
`tmp` | For developers only
`CA` | For developers only
`p.table` | For developers only

### Value

A list with elements

`Zx` | output vector corrected Chi square statistic
`vv` | output vector of VIF
`exeps` | output vector of exepsons (NA)
`calrate` | output vector of calrate
`F` | F
`K` | K

### Author(s)

Yakov Tsepilov

### Examples

    require(GenABEL.data)
    data(ge03d2)
    # truncate the data to make the example faster
    ge03d2 <- ge03d2[seq(from=1,to=nids(ge03d2),by=2),seq(from=1,to=nsnps(ge03d2),by=3)]
    qts <- mlreg(dm2~sex,data=ge03d2,gtmode = "dominant")
    chi2.1df <- results(qts)$chi2.1df
    s <- summary(ge03d2)
    freq <- s$Q.2
    result <- VIFGC(p=freq,x=1,method = "median",CA=FALSE,data=chi2.1df,n=nids(ge03d2))

[Package GenABEL version 1.8-0 Index]