qq.chisq(x, df=1, x.max, main="QQ plot", sub=paste("Expected distribution: chi-squared (",df," df)", sep=""), xlab="Expected", ylab="Observed", conc=c(0.025, 0.975), overdisp=FALSE, trim=0.5, slope.one=FALSE, slope.lambda=FALSE, pvals=FALSE, thin=c(0.25,50), oor.pch=24, col.shade="gray", ...)
abs(x.max)
. If x.max
is negative, the y-axis will
extend to abs(x.max)
even if the observed data do notNA
to suppress thisTRUE
, an overdispersion factor, lambda, will be
estimated and used in calculating concentration bandNA
, no thinning will be carried outx.max
are plotted
at x.max
. This argument sets the plotting symbol to be used
for out-of-range observationspoints()
x.max
), and the estimated
dispersion factor, lambda.
thin
, whose value should be a pair of numbers.
The first number must lie
between 0 and 1 and sets the proportion of the X axis over which
thinning is to be applied. The second number should be an integer and
sets the maximum number of points to be plotted in this section.
The "concentration band" for the plot is shown in grey. This region is
defined by upper and lower probability bounds for each order statistic.
The default is to use the 2.5
Note that this is not a simultaneous confidence region; the probability
that the plot will stray outside the band at some point exceeds 95When required, the dispersion factor is estimated by the ratio of the observed trimmed mean to its expected value under the chi-squared assumption.
single.snp.tests
, snp.lhs.tests
,
snp.rhs.tests
## See example the single.snp.tests() function
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