Description Usage Arguments Details Value See Also Examples
Creates an interactive graph of a single-QTL genome scan, as calculated by [qtl::scanone()]. If 'cross' is provided, the LOD curves are linked to a phenotype x genotype plot for a marker: Click on a marker on the LOD curve and see the corresponding phenotype x genotype plot.
1 2 3 4 5 6 7 8 9 10 11 |
scanoneOutput |
Object of class '"scanone"', as output from [qtl::scanone()]. |
cross |
(Optional) Object of class '"cross"', see [qtl::read.cross()]. |
lodcolumn |
Numeric value indicating LOD score column to plot. |
pheno.col |
(Optional) Phenotype column in cross object. |
chr |
(Optional) Vector indicating the chromosomes for which LOD scores should be calculated. This should be a vector of character strings referring to chromosomes by name; numeric values are converted to strings. Refer to chromosomes with a preceding - to have all chromosomes but those considered. A logical (TRUE/FALSE) vector may also be used. |
pxgtype |
If phenotype x genotype plot is to be shown, should it be with means +/- 2 SE ('"ci"'), or raw phenotypes ('"raw"')? |
fillgenoArgs |
List of named arguments to pass to [qtl::fill.geno()], if needed. |
chartOpts |
A list of options for configuring the chart (see the coffeescript code). Each element must be named using the corresponding option. |
digits |
Round data to this number of significant digits before passing to the chart function. (Use NULL to not round.) |
If 'cross' is provided, [qtl::fill.geno()] is used to impute missing genotypes. In this case, arguments to [qtl::fill.geno()] are passed as a list, for example 'fillgenoArgs=list(method="argmax", error.prob=0.002, map.function="c-f")'.
With 'pxgtype="raw"', individual IDs (viewable when hovering over a point in the phenotype-by-genotype plot) are taken from the input 'cross' object, using the [qtl::getid()] function in R/qtl.
An object of class 'htmlwidget' that will intelligently print itself into HTML in a variety of contexts including the R console, within R Markdown documents, and within Shiny output bindings.
[iplotMScanone()], [iplotPXG()], [iplotMap()]
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | library(qtl)
data(hyper)
hyper <- calc.genoprob(hyper, step=1)
out <- scanone(hyper)
# iplotScanone with no effects
iplotScanone(out, chr=c(1, 4, 6, 7, 15))
# iplotScanone with CIs
iplotScanone(out, hyper, chr=c(1, 4, 6, 7, 15))
# iplotScanone with raw phe x gen
iplotScanone(out, hyper, chr=c(1, 4, 6, 7, 15),
pxgtype='raw')
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