Description Usage Arguments Details Value See Also Examples
Creates an interactive graph of a scatterplot of two phenotypes, plus optionally the LOD curves for the two traits along one chromosome, with a slider for selecting the locations of two QTL which are then indicated on the LOD curves and the corresponding genotypes used to color the points in the scatterplot.
1 2 3 4 5 6 7 8 9 10 11 |
cross |
(Optional) Object of class '"cross"', see [qtl::read.cross()]. |
scanoneOutput |
(Optional) object of class '"scanone"', as output from [qtl::scanone()]. |
pheno.col |
Vector indicating two phenotype column in cross object; either numeric or character strings (the latter being the phenotype column names). |
lodcolumn |
Vector of two numeric values indicating LOD score columns to plot. |
chr |
A single chromosome ID, as a character string. |
interval |
A numeric vector of length 2, defining an interval that indicates what portion of the chromosome should be included. |
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.) |
[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")'.
Individual IDs (viewable when hovering over a point in the scatterplot of the two phenotypes) 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.
[iplotScanone()], [iplotMScanone()], [iplotPXG()]
1 2 3 4 5 6 7 8 9 10 | library(qtl)
data(fake.bc)
fake.bc <- calc.genoprob(fake.bc[5,], step=1) # select chr 5
out <- scanone(fake.bc, method="hk", pheno.col=1:2)
ipleiotropy(fake.bc, out)
# omit the LOD curves
ipleiotropy(fake.bc)
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