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\title{Genomic control for various model of inheritance using VIF}
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GC(data, p, x, method = "regress", n,
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index.filter = NULL, proportion = 1, clust = 0,
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vart0 = 0, tmp = 0, CA = FALSE, p.table = 0)
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\item{data}{Input vector of Chi square statistic}
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\item{method}{Function of error to be optimized. Can be
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"regress", "median" or "ks.test"}
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\item{p}{Input vector of allele frequencies}
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\item{x}{Model of inheritance (0 for recessive,0.5 for
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additive, 1 for dominant)}
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\item{index.filter}{Indexes for variables that will be
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use for analisis in data vector}
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\item{n}{size of the sample}
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\item{proportion}{The proportion of lowest P (Chi2) to be
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used when estimating the inflation factor Lambda for
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"regress" method only}
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\item{clust}{For developers only}
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\item{vart0}{For developers only}
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\item{tmp}{For developers only}
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\item{CA}{For developers only}
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\item{p.table}{For developers only}
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A list with elements \item{Zx}{output vector corrected
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Chi square statistic} \item{vv}{output vector of VIF}
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\item{exeps}{output vector of exepsons (NA)}
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\item{calrate}{output vector of calrate} \item{F}{F}
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This function estimates corrected statistic using genomic
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control for diffrent models (recessive,dominant,additive
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etc.), using VIF. VIF coefficients are estimated by
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optimizing diffrent error functions: regress, median and
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# truncate the data to make the example faster
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ge03d2 <- ge03d2[seq(from=1,to=nids(ge03d2),by=2),seq(from=1,to=nsnps(ge03d2),by=3)]
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qts <- mlreg(dm2~sex,data=ge03d2,gtmode = "dominant")
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chi2.1df <- results(qts)$chi2.1df
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result <- GC(p=freq,x=1,method = "median",CA=FALSE,data=chi2.1df,n=nids(ge03d2))
added
removed
man/GC.Rd
