Description Usage Arguments Details Value Author(s) References See Also Examples
Functions for visualizing association test results by means of a quantile-quantile (Q-Q) 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 Q-Q plot of the raw p-values in x
against a uniform
distribution of expected p-values is created, where the theoretical
p-values are computed using the ppoints
function.
In this case, the log-transformed observed p-values contained in x
are plotted on the vertical axis and the log-transformed expected
p-values 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 p-values are plotted on the vertical axis.
If qqplot
is called for two
AssocTestResultRanges
object x
and
y
, the log-transformed raw p-values of x
and y
are plotted against each other, where the p-values of x
are plotted on
the horizontal axis and the p-values 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 p-values.
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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