Plot Predictive Interval for Categorical Genotype/Phenotype Groups

Description

predictive.plot should be used to visually investigate loci identified with plot.scanonevar or summary.scanonevar. The user can specify the same mean and variance formulae that were used in the scan, or specify new formulae to investigate interactions.

Usage

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predictive.plot(cross, mean.formula, var.formula, marker.name, phen.name,
  title = paste("Predictive of", response.phen, "from", phen.name, "and",
  marker.name), title.cex = 1, genotype.plotting.names = c("AA", "AB",
  "BB"), ribbon.width = 10, xlim = NA, ylim = NA)

Arguments

cross

The cross object to be plotted

mean.formula

The formula that describes the response, and the covariates and genetic effects that influence it. The left hand side of the ~ must be a single phenotype that is in the cross. The right hand side must use only phenotypes thata are in the cross, markers that are in the cross, and the special terms: mean.QTL.add (additive effect on the mean) and mean.QTL.dom (dominance deviation from additive on the mean).

var.formula

The formula that describes the covariates and the genetic effects that influence residual (environmental) variation. There should be nothing on the left of the ~ (Inferred to be residual variation). The right hand side must use only phenotypes thata are in the cross, markers that are in the cross, and the special terms: var.QTL.add (additive effect on the variance) and var.QTL.dom (dominance deviation from additive on the variance).

marker.name

The name of the marker the effects of which we want to investigate and visualize.

phen.name

The categorical phenotype the effects of which we want to investigate and visualize.

title

Optionally, title for the plot. Defaults to 'Predictive of [response phenotype] from [predictive phenotype (e.g. sex)] and [marker name]

title.cex

Optionally, character expansion for title. Defaults to 1.

genotype.plotting.names

Labels for the genotype groups. Defaults to c('AA', 'AB', 'BB').

ribbon.width

Optionally, width of ribbon connecting same-phenotype (different genotype) groups. Defaults to 10.

xlim

Optionally specify x-axis limits. Defaults to data-dependent.

ylim

Optionally specify y-axis limits. Defaults to data.dependent.

Details

none

Value

None. Only makes plot.

Author(s)

Robert Corty rcorty@gmail.com

Examples

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set.seed(27599)
   my.cross <- sim.cross(map = sim.map(), type = 'f2')
   my.cross <- calc.genoprob(my.cross)
   my.cross$pheno$phenotype <- rnorm(n = 100,
                                     mean = my.cross$geno$`1`$data[,5],
                                     sd = my.cross$geno$`2`$data[,5])
   my.cross$pheno$sex <- rbinom(n = 100, size = 1, prob = 0.5)
   my.cross$pheno$cage <- sample(x = 1:5, size = 100, replace = TRUE)

   predictive.plot(cross = my.cross,
                   mean.formula = 'phenotype ~ sex + mean.QTL.add + mean.QTL.dom',
                   var.formula = '~ sex + var.QTL.add + var.QTL.dom',
                   marker.name = 'D1M5',
                   phen.name = 'sex')

   predictive.plot(cross = my.cross,
                   mean.formula = 'phenotype ~ sex + mean.QTL.add + mean.QTL.dom',
                   var.formula = '~ sex + var.QTL.add + var.QTL.dom',
                   marker.name = 'D2M5',
                   phen.name = 'sex')