Package {qtlcharts}

Version:	0.22
Date:	2026-06-19
Title:	Interactive Graphics for QTL Experiments
Author:	Karl W Broman [aut, cre], Michael Bostock [ctb, cph] (d3.js library in htmlwidgets/lib, https://d3js.org), jQuery Foundation [cph] (jQuery library in htmlwidgets/lib, https://jquery.com), jQuery contributors [ctb] (jQuery library in htmlwidgets/lib; see https://github.com/jquery/jquery/blob/master/AUTHORS.txt), jQuery UI contributors [ctb] (jQuery UI library in htmlwidgets/lib; see https://jqueryui.com/about/)
Maintainer:	Karl W Broman <broman@wisc.edu>
Description:	Web-based interactive charts (using D3.js) for the analysis of experimental crosses to identify genetic loci (quantitative trait loci, QTL) contributing to variation in quantitative traits. Broman (2015) <doi:10.1534/genetics.114.172742>.
Depends:	R (≥ 2.15)
Imports:	qtl (≥ 1.30-4), htmlwidgets, graphics, stats, utils
Suggests:	htmltools, jsonlite, knitr, devtools, roxygen2, rmarkdown, testthat
License:	GPL-3 | file LICENSE
URL:	https://kbroman.org/qtlcharts/, https://github.com/kbroman/qtlcharts
BugReports:	https://github.com/kbroman/qtlcharts/issues
LazyData:	true
Encoding:	UTF-8
ByteCompile:	true
Config/roxygen2/version:	8.0.0
NeedsCompilation:	no
Packaged:	2026-06-19 12:10:11 UTC; kbroman
Repository:	CRAN
Date/Publication:	2026-06-19 12:20:18 UTC

R/qtlcharts: Interactive charts for QTL data

Description

A QTL is a quantitative trait locus: a genetic locus that contributes to variation in a quantitative trait. The goal of R/qtlcharts is to provide interactive data visualizations for QTL analyses, and to make these visualizations available from R. It is a companion to the R/qtl package.

Details

Vignettes online at the R/qtlcharts website:

• User guide

• Developer guide

• Use with R Markdown [Rmd source]

• List of chart customization options

• Examples of use with Shiny

• Examples of use with Jupyter notebooks

Author(s)

Maintainer: Karl W Broman broman@wisc.edu (ORCID)

Authors:

• Karl W Broman broman@wisc.edu (ORCID)

Other contributors:

• Michael Bostock (d3.js library in htmlwidgets/lib, https://d3js.org) [contributor, copyright holder]

• jQuery Foundation (jQuery library in htmlwidgets/lib, https://jquery.com) [copyright holder]

• jQuery contributors (jQuery library in htmlwidgets/lib; see https://github.com/jquery/jquery/blob/master/AUTHORS.txt) [contributor]

• jQuery UI contributors (jQuery UI library in htmlwidgets/lib; see https://jqueryui.com/about/) [contributor]

See Also

Useful links:

• https://kbroman.org/qtlcharts/

• https://github.com/kbroman/qtlcharts

• Report bugs at https://github.com/kbroman/qtlcharts/issues

Combine multiple runs of estQTLeffects

Description

Combine multiple runs of estQTLeffects by applying cbind to each component

Usage

cbindQTLeffects(..., labels)

Arguments

Value

list of matrices; each component corresponds to a position in the genome and is a matrix with phenotypes x effects

See Also

estQTLeffects()

Examples

library(qtl)
data(fake.f2)
fake.f2 <- calc.genoprob(fake.f2)
sex <- fake.f2$pheno$sex
eff.fem <- estQTLeffects(fake.f2[,sex==0], pheno.col=1)
eff.mal <- estQTLeffects(fake.f2[,sex==1], pheno.col=1)
eff <- cbindQTLeffects(eff.fem, eff.mal, labels=c("female", "male"))

Calculate QTL effects at each position across the genome

Description

Calculates the effects of QTL at each position across the genome using Haley-Knott regression, much like qtl::effectscan(), but considering multiple phenotypes and not plotting the results

Usage

estQTLeffects(cross, pheno.col = 1, what = c("means", "effects"))

Arguments

Details

One should first run qtl::calc.genoprob(); if not, it is run with the default arguments.

The estimated effects will be poorly estimated in the case of selective genotyping, as Haley-Knott regression performs poorly in this case.

Value

list of matrices; each component corresponds to a position in the genome and is a matrix with phenotypes x effects

See Also

iplotMScanone(), qtl::effectscan() cbindQTLeffects()

Examples

data(grav)
library(qtl)
grav <- reduce2grid(calc.genoprob(grav, step=1))
out <- estQTLeffects(grav, phe=seq(1, nphe(grav), by=5))

Anonymized gene expression data

Description

An anonymized set of gene expression values, for 100 genes all influenced by a common locus, plus a vector of genotypes for the 491 individuals.

Usage

data(geneExpr)

Format

A list containing a matrix expr with the gene expression data plus a vector genotype with the genotypes.

Author(s)

Karl W Broman, 2013-05-16

Examples

data(geneExpr)

# heat map of correlation matrix, linked to scatterplots
iplotCorr(geneExpr$expr, geneExpr$genotype, reorder=TRUE)

Arabidopsis QTL data on gravitropism

Description

Data from a QTL experiment on gravitropism in Arabidopsis, with data on 162 recombinant inbred lines (Ler x Cvi). The outcome is the root tip angle (in degrees) at two-minute increments over eight hours.

Usage

data(grav)

Format

An object of class "cross"; see qtl::read.cross().

Source

Mouse Phenome Database, https://phenome.jax.org/projects/Moore1b

References

Moore CR, Johnson LS, Kwak I-Y, Livny M, Broman KW, Spalding EP (2013) High-throughput computer vision introduces the time axis to a quantitative trait map of a plant growth response. Genetics 195:1077-1086 doi:10.1534/genetics.113.153346

Examples

data(grav)
times <- attr(grav, "time")
phe <- grav$pheno

iplotCurves(phe, times, phe[,c(61,121)], phe[,c(121,181)],
            chartOpts=list(curves_xlab="Time (hours)", curves_ylab="Root tip angle (degrees)",
                           scat1_xlab="Angle at 2 hrs", scat1_ylab="Angle at 4 hrs",
                           scat2_xlab="Angle at 4 hrs", scat2_ylab="Angle at 6 hrs"))

Modern boxplot linked to underlying histrograms

Description

Creates an interactive graph for a large set of box plots (rendered as lines connecting the quantiles), linked to underlying histograms.

Usage

iboxplot(
  dat,
  qu = c(0.001, 0.01, 0.1, 0.25),
  orderByMedian = TRUE,
  breaks = 251,
  chartOpts = NULL,
  digits = 5
)

Arguments

Value

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.

See Also

iplotCorr(), scat2scat()

Examples

n.ind <- 500
n.gene <- 10000
expr <- matrix(rnorm(n.ind * n.gene, (1:n.ind)/n.ind*3), ncol=n.gene)
dimnames(expr) <- list(paste0("ind", 1:n.ind),
                       paste0("gene", 1:n.gene))

iboxplot(expr, chartOpts=list(xlab="Mice", ylab="Gene expression"))

Interactive phenotype x genotype plot

Description

Creates an interactive graph of phenotypes vs genotypes at a marker.

Usage

idotplot(
  x,
  y,
  indID = NULL,
  group = NULL,
  horizontal = FALSE,
  chartOpts = NULL,
  digits = 5
)

Arguments

Value

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.

See Also

iplot(), iplotPXG()

Examples

n <- 100
g <- sample(LETTERS[1:3], n, replace=TRUE)
y <- rnorm(n, match(g, LETTERS[1:3])*10, 5)

idotplot(g, y)
idotplot(g, y, horizontal=TRUE)

Interactive heat map

Description

Creates an interactive heatmap, with each cell linked to plots of horizontal and vertical slices

Usage

iheatmap(z, x = NULL, y = NULL, chartOpts = NULL, digits = 5)

Arguments

Details

By default, the z-axis limits are from -max(abs(z)) to max(abs(z)), and negative cells are colored blue to white which positive cells are colored white to red.

Value

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.

See Also

iplotCorr()

Examples

n <- 101
x <- y <- seq(-2, 2, len=n)
z <- matrix(ncol=n, nrow=n)
for(i in seq(along=x))
    for(j in seq(along=y))
        z[i,j] <- x[i]*y[j]*exp(-x[i]^2 - y[j]^2)

iheatmap(z, x, y)

Tool to explore pleiotropy

Description

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.

Usage

ipleiotropy(
  cross,
  scanoneOutput = NULL,
  pheno.col = 1:2,
  lodcolumn = 1:2,
  chr = NULL,
  interval = NULL,
  fillgenoArgs = NULL,
  chartOpts = NULL,
  digits = 5
)

Arguments

Details

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.

Value

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.

See Also

iplotScanone(), iplotMScanone(), iplotPXG()

Examples

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)

Interactive scatterplot

Description

Creates an interactive scatterplot.

Usage

iplot(x, y = NULL, group = NULL, indID = NULL, chartOpts = NULL, digits = 5)

Arguments

Value

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.

See Also

iplotCorr(), iplotCurves(), itriplot(), idotplot(), iplotPXG()

Examples

x <- rnorm(100)
grp <- sample(1:3, 100, replace=TRUE)
y <- x*grp + rnorm(100)

iplot(x, y, grp)

Image of correlation matrix with linked scatterplot

Description

Creates an interactive graph with an image of a correlation matrix linked to underlying scatterplots.

Usage

iplotCorr(
  mat,
  group = NULL,
  rows = NULL,
  cols = NULL,
  reorder = FALSE,
  corr = NULL,
  scatterplots = TRUE,
  chartOpts = NULL,
  digits = 5
)

Arguments

Details

corr may be provided as a subset of the overall correlation matrix for the columns of mat. In this case, the reorder, rows and cols arguments are ignored. The row and column names of corr must match the names of some subset of columns of mat.

Individual IDs are taken from rownames(mat); they must match names(group).

Value

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.

See Also

iheatmap(), scat2scat(), iplotCurves()

Examples

data(geneExpr)

iplotCorr(geneExpr$expr, geneExpr$genotype, reorder=TRUE,
          chartOpts=list(cortitle="Correlation matrix",
                         scattitle="Scatterplot"))

# use Spearman's correlation
corr <- cor(geneExpr$expr, method="spearman", use="pairwise.complete.obs")
# order by hierarchical clustering
o <- hclust(as.dist(1-corr))$order

iplotCorr(geneExpr$expr[,o], geneExpr$genotype, corr=corr[o,o],
          chartOpts=list(cortitle="Spearman correlation",
                         scattitle="Scatterplot"))

Plot of a bunch of curves, linked to points in scatterplots

Description

Creates an interactive graph with a panel having a number of curves (say, a phenotype measured over time) linked to one or two (or no) scatter plots (say, of the first vs middle and middle vs last times).

Usage

iplotCurves(
  curveMatrix,
  times = NULL,
  scatter1 = NULL,
  scatter2 = NULL,
  group = NULL,
  chartOpts = NULL,
  digits = 5
)

Arguments

Value

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.

See Also

iplotCorr(), iplot(), scat2scat()

Examples

# random growth curves, based on some data
times <- 1:16
n <- 100
start <- rnorm(n, 5.2, 0.8)
slope1to5 <- rnorm(n, 2.6, 0.5)
slope5to16 <- rnorm(n, 0.24 + 0.09*slope1to5, 0.195)
y <- matrix(ncol=16, nrow=n)
y[,1] <- start
for(j in 2:5)
    y[,j] <- y[,j-1] + slope1to5
for(j in 6:16)
    y[,j] <- y[,j-1] + slope5to16
y <- y + rnorm(prod(dim(y)), 0, 0.35)


iplotCurves(y, times, y[,c(1,5)], y[,c(5,16)],
            chartOpts=list(curves_xlab="Time", curves_ylab="Size",
                           scat1_xlab="Size at T=1", scat1_ylab="Size at T=5",
                           scat2_xlab="Size at T=5", scat2_ylab="Size at T=16"))

Interactive LOD curve

Description

Creates an interactive graph of a set of single-QTL genome scans, as calculated by qtl::scanone(). If cross or effects are provided, LOD curves will be linked to a panel with estimated QTL effects.

Usage

iplotMScanone(
  scanoneOutput,
  cross = NULL,
  lodcolumn = NULL,
  pheno.col = NULL,
  times = NULL,
  effects = NULL,
  chr = NULL,
  chartOpts = NULL,
  digits = 5
)

Arguments

Details

If cross is provided, Haley-Knott regression is used to estimate QTL effects at each pseudomarker.

Value

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.

See Also

iplotScanone()

Examples

data(grav)
library(qtl)
grav <- calc.genoprob(grav, step=1)
grav <- reduce2grid(grav)

# we're going to subset the phenotypes
phecol <- seq(1, nphe(grav), by=5)

# the times were saved as an attributed
times <- attr(grav, "time")[phecol]

# genome scan
out <- scanone(grav, phe=phecol, method="hk")


# plot with qualitative labels on y-axis
iplotMScanone(out)


# plot with quantitative y-axis
iplotMScanone(out, times=times)

# estimate QTL effect for each time point at each genomic position
eff <- estQTLeffects(grav, phe=seq(1, nphe(grav), by=5), what="effects")


# plot with QTL effects included (and with quantitative y-axis)
iplotMScanone(out, effects=eff, times=times,
              chartOpts=list(ylab="Time (hrs)", eff_ylab="QTL effect"))

Interactive genetic map plot

Description

Creates an interactive graph of a genetic marker map.

Usage

iplotMap(
  map,
  chr = NULL,
  shift = FALSE,
  horizontal = FALSE,
  chartOpts = NULL,
  digits = 5
)

Arguments

Value

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.

See Also

iplotScanone(), iplotPXG()

Examples

library(qtl)
data(hyper)
map <- pull.map(hyper)

iplotMap(map, shift=TRUE)

Interactive phenotype x genotype plot

Description

Creates an interactive graph of phenotypes vs genotypes at a marker.

Usage

iplotPXG(
  cross,
  marker,
  pheno.col = 1,
  horizontal = FALSE,
  chartOpts = NULL,
  fillgenoArgs = NULL,
  digits = 5
)

Arguments

Details

The function qtl::fill.geno() is used to impute missing genotypes, with arguments 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) are taken from the input cross object, using the qtl::getid() function in R/qtl.

By default, points are colored blue and pink according to whether the marker genotype is observed or inferred, respectively.

Value

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.

See Also

idotplot(), iplot(), iplotScanone(), iplotMap()

Examples

library(qtl)
data(hyper)
marker <- sample(markernames(hyper), 1)

iplotPXG(hyper, marker)

# different colors
iplotPXG(hyper, marker, chartOpts=list(pointcolor=c("black", "gray")))

Interactive plot of recombination fractions

Description

Creates an interactive graph of estimated recombination fractions and LOD scores for all pairs of markers.

Usage

iplotRF(cross, chr = NULL, chartOpts = NULL, digits = 5)

Arguments

Details

The heatmap in the plot just depicts the LOD scores for
testing rf=0.5.

Value

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.

See Also

qtl::est.rf(), qtl::plotRF()

Examples

library(qtl)
data(fake.f2)

fake.f2 <- est.rf(fake.f2)

iplotRF(fake.f2)

Interactive LOD curve

Description

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.

Usage

iplotScanone(
  scanoneOutput,
  cross = NULL,
  lodcolumn = 1,
  pheno.col = 1,
  chr = NULL,
  pxgtype = c("ci", "raw"),
  fillgenoArgs = NULL,
  chartOpts = NULL,
  digits = 5
)

Arguments

Details

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.

Value

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.

See Also

iplotMScanone(), iplotPXG(), iplotMap()

Examples

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')

Interactive plot of 2d genome scan

Description

Creates an interactive plot of the results of qtl::scantwo(), for a two-dimensional, two-QTL genome scan.

Usage

iplotScantwo(
  scantwoOutput,
  cross = NULL,
  lodcolumn = 1,
  pheno.col = 1,
  chr = NULL,
  chartOpts = NULL,
  digits = 5
)

Arguments

Details

The estimated QTL effects, and the genotypes in the phenotype x genotype plot, in the right-hand panels, are derived following a single imputation to fill in missing data, and so are a bit crude.

Note that the usual height and width options in chartOpts are ignored here. Instead, you may provide pixelPerCell (number of pixels per cell in the heat map), chrGap (gaps between chr in heat map), wright (width in pixels of right panels), and hbot (height in pixels of each of the lower panels)

Value

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.

See Also

iplotScanone()

Examples

library(qtl)
data(fake.f2)

fake.f2 <- calc.genoprob(fake.f2, step=5)
out <- scantwo(fake.f2, method="hk", verbose=FALSE)

iplotScantwo(out, fake.f2)

Interactive scatterplot with date/time on x-axis

Description

Creates an interactive scatterplot with date/time on the x-axis.

Usage

itimeplot(
  datetime,
  y,
  group = NULL,
  indID = NULL,
  chartOpts = NULL,
  digits = 5
)

Arguments

Value

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.

See Also

iplotCorr(), iplotCurves(), itriplot(), idotplot(), iplotPXG()

Examples

n_pts <- 100
x <-  seq(as.POSIXct("1969-05-01"), as.POSIXct("1969-05-04"), length=n_pts)
grp <- sample(1:3, n_pts, replace=TRUE)
y <- rnorm(n_pts, grp) + rnorm(n_pts)

itimeplot(x, y, grp)


# with shorter time span, times are shown instead of dates
x <-  seq(as.POSIXct("1969-05-01"), as.POSIXct("1969-05-02"), length=n_pts)

itimeplot(x, y, grp, chartOpts=list(xlab="Time"))

#'

Interactive plot of trinomial probabilities

Description

Creates an interactive graph of trinomial probabilities, represented as points within an equilateral triangle.

Usage

itriplot(p, indID = NULL, group = NULL, chartOpts = NULL, digits = 5)

Arguments

Value

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.

See Also

iplot(), iplotPXG(), idotplot()

Examples

n <- 100
p <- matrix(runif(3*n), ncol=3)
p <- p / colSums(p)
g <- sample(1:3, n, replace=TRUE)

itriplot(p, group=g)

Shiny bindings for R/qtlcharts widgets

Description

Output and render functions for using R/qtlcharts widgets within Shiny applications and interactive Rmd documents. For examples, see https://github.com/kbroman/shiny_qtlcharts.

Usage

iboxplot_output(outputId, width = "100%", height = "900")

iboxplot_render(expr, env = parent.frame(), quoted = FALSE)

idotplot_output(outputId, width = "100%", height = "530")

idotplot_render(expr, env = parent.frame(), quoted = FALSE)

iheatmap_output(outputId, width = "100%", height = "1000")

iheatmap_render(expr, env = parent.frame(), quoted = FALSE)

ipleiotropy_output(outputId, width = "100%", height = "580")

ipleiotropy_render(expr, env = parent.frame(), quoted = FALSE)

iplot_output(outputId, width = "100%", height = "580")

iplot_render(expr, env = parent.frame(), quoted = FALSE)

iplotCorr_output(outputId, width = "100%", height = "1000")

iplotCorr_render(expr, env = parent.frame(), quoted = FALSE)

iplotCurves_output(outputId, width = "100%", height = "1000")

iplotCurves_render(expr, env = parent.frame(), quoted = FALSE)

iplotMScanone_output(outputId, width = "100%", height = "580")

iplotMScanone_render(expr, env = parent.frame(), quoted = FALSE)

iplotMap_output(outputId, width = "100%", height = "680")

iplotMap_render(expr, env = parent.frame(), quoted = FALSE)

iplotRF_output(outputId, width = "100%", height = "1000")

iplotRF_render(expr, env = parent.frame(), quoted = FALSE)

iplotScanone_output(outputId, width = "100%", height = "580")

iplotScanone_render(expr, env = parent.frame(), quoted = FALSE)

iplotScantwo_output(outputId, width = "100%", height = "1000")

iplotScantwo_render(expr, env = parent.frame(), quoted = FALSE)

itimeplot_output(outputId, width = "100%", height = "580")

itimeplot_render(expr, env = parent.frame(), quoted = FALSE)

itriplot_output(outputId, width = "100%", height = "530")

itriplot_render(expr, env = parent.frame(), quoted = FALSE)

scat2scat_output(outputId, width = "100%", height = "530")

scat2scat_render(expr, env = parent.frame(), quoted = FALSE)

Arguments

print the installed version of R/qtlcharts

Description

print the installed version of R/qtlcharts

Usage

qtlchartsversion()

Value

Character string with version number

Examples

qtlchartsversion()

Scatterplot driving another scatterplot

Description

A pair of linked scatterplots, where each point the first scatterplot corresponds to a scatter of points in the second scatterplot. The first scatterplot corresponds to a pair of summary measures for a larger dataset.

Usage

scat2scat(scat1data, scat2data, group = NULL, chartOpts = NULL, digits = 5)

Arguments

Value

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.

See Also

iplotCorr()

Examples

# simulate some data
p <- 500
n <- 300
SD <- runif(p, 1, 5)
r <- runif(p, -1, 1)
scat2 <- vector("list", p)
for(i in 1:p)
   scat2[[i]] <- matrix(rnorm(2*n), ncol=2) %*% chol(SD[i]^2*matrix(c(1, r[i], r[i], 1), ncol=2))
scat1 <- cbind(SD=SD, r=r)
# plot it
scat2scat(scat1, scat2)

Set default maximum screen size

Description

Set the default screen size as a global option.

Usage

setScreenSize(size = c("normal", "small", "large"), height, width)

Arguments

Details

Used to set a global option, qtlchartsScreenSize, that contains the maximum height and maximum width for a chart in the browser.

"small", "normal", and "large" correspond to 600x900, 700x1000, and 1200x1600, for height x width, respectively.

Value

None.

Examples

setScreenSize("large")