Description Usage Arguments Details Value Author(s) References See Also Examples
Functions for visualizing association test results by means of a quantilequantile (QQ) plot
1 2 3 4 5 6 7 8 9 10  ## S4 method for signature 'AssocTestResultRanges,missing'
qqplot(x, y,
xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
common.scale=TRUE, preserveLabels=FALSE, lwd=1,
lcol="red", ...)
## S4 method for signature 'AssocTestResultRanges,AssocTestResultRanges'
qqplot(x, y,
xlab=deparse(substitute(x)), ylab=deparse(substitute(y)),
common.scale=TRUE, preserveLabels=FALSE, lwd=1,
lcol="red", ...)

x,y 
objects of class

xlab 
if 
ylab 
if 
common.scale 
if 
preserveLabels 
if 
lwd 
line width for drawing the diagonal line which theoretically corresponds to the equality of the two distributions; if zero, no diagonal line is drawn. 
lcol 
color for drawing the diagonal line 
... 
all other arguments are passed to

If qqplot
is called for an
AssocTestResultRanges
object without specifying the second argument y
,
a QQ plot of the raw pvalues in x
against a uniform
distribution of expected pvalues is created, where the theoretical
pvalues are computed using the ppoints
function.
In this case, the logtransformed observed pvalues contained in x
are plotted on the vertical axis and the logtransformed expected
pvalues are plotted
on the horizontal axis. If preserveLabels
is TRUE
,
xlab
and ylab
are used as axis labels as usual.
However, if preserveLabels
is FALSE
, which is the
default, xlab
is interpreted as object label for x
, i.e.
the object whose pvalues are plotted on the vertical axis.
If qqplot
is called for two
AssocTestResultRanges
object x
and
y
, the logtransformed raw pvalues of x
and y
are plotted against each other, where the pvalues of x
are plotted on
the horizontal axis and the pvalues of x
are plotted on the
vertical axis.
like the standard qqplot
function from
the stats package, qqplot
returns an invisible list
containing the two sorted vectors of pvalues.
Ulrich Bodenhofer bodenhofer@bioinf.jku.at
http://www.bioinf.jku.at/software/podkat
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## load genome description
data(hgA)
## partition genome into overlapping windows
windows < partitionRegions(hgA)
## load genotype data from VCF file
vcfFile < system.file("examples/example1.vcf.gz", package="podkat")
Z < readGenotypeMatrix(vcfFile)
## read phenotype data from CSV file (continuous trait + covariates)
phenoFile < system.file("examples/example1lin.csv", package="podkat")
pheno <read.table(phenoFile, header=TRUE, sep=",")
## train null model with all covariates in data frame 'pheno'
nm.lin < nullModel(y ~ ., pheno)
## perform association tests
res.p < assocTest(Z, nm.lin, windows, kernel="linear.podkat")
res.s < assocTest(Z, nm.lin, windows, kernel="linear.SKAT")
## plot results
qqplot(res.p)
qqplot(res.p, res.s, xlab="PODKAT results", ylab="SKAT results")
qqplot(res.p, res.s, xlab="PODKAT results", ylab="SKAT results",
preserveLabels=TRUE)
qqplot(res.p, res.s, common.scale=FALSE)

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