plot_pxg: Plot phenotype vs genotype
In rqtl/qtl2plot: Data Visualization for QTL Experiments

Description Usage Arguments Hidden graphics parameters See Also Examples

View source: R/plot_pxg.R

Plot phenotype vs genotype for a single putative QTL and a single phenotype.

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plot_pxg(geno, pheno, sort = TRUE, SEmult = NULL, pooledSD = TRUE,
  swap_axes = FALSE, jitter = 0.2, force_labels = TRUE,
  alternate_labels = FALSE, omit_points = FALSE, ...)

`geno`	Vector of genotypes, as produced by `qtl2geno::maxmarg()` with specific `chr` and `pos`.
`pheno`	Vector of phenotypes.
`sort`	If TRUE, sort genotypes from largest to smallest.
`SEmult`	If specified, interval estimates of the within-group averages will be displayed, as `mean +/- SE * SEmult`.
`pooledSD`	If TRUE and `SEmult` is specified, calculated a pooled within-group SD. Otherwise, get separate estimates of the within-group SD for each group.
`swap_axes`	If TRUE, swap the axes, so that the genotypes are on the y-axis and the phenotype is on the x-axis.
`jitter`	Amount to jitter the points horizontally, if a vector of length > 0, it is taken to be the actual jitter amounts (with values between -0.5 and 0.5).
`force_labels`	If TRUE, force all genotype labels to be shown.
`alternate_labels`	If TRUE, place genotype labels in two rows
`omit_points`	If TRUE, omit the points, just plotting the averages (and, potentially, the +/- SE intervals).
`...`	Additional graphics parameters, passed to `graphics::plot()`.

A number of graphics parameters can be passed via .... For example, bgcolor to control the background color, and seg_width, seg_lwd, and seg_col to control the lines at the confidence intervals. Further, hlines, hlines_col, hlines_lwd, and hlines_lty to control the horizontal grid lines. (Use hlines=NA to avoid plotting horizontal grid lines.) Similarly vlines, vlines_col, vlines_lwd, and vlines_lty for vertical grid lines. These are not included as formal parameters in order to avoid cluttering the function definition.

plot_coef()

# load qtl2geno package for data and genoprob calculation
library(qtl2geno)

# read data
iron <- read_cross2(system.file("extdata", "iron.zip", package="qtl2geno"))

# insert pseudomarkers into map
map <- insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- calc_genoprob(iron, map, error_prob=0.002)

# inferred genotype at a 28.6 cM on chr 16
geno <- maxmarg(probs, map, chr=16, pos=28.6, return_char=TRUE)

# plot phenotype vs genotype
plot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)))

# include +/- 2 SE intervals
plot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         SEmult=2)

# plot just the means
plot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         omit_points=TRUE)

# plot just the means +/- 2 SEs
plot_pxg(geno, log10(iron$pheno[,1]), ylab=expression(log[10](Liver)),
         omit_points=TRUE, SEmult=2)