DistributionPlotBinomial: Distribution Plot
In polySegratioMM: Bayesian Mixture Models for Marker Dosage in Autopolyploids

Description Usage Arguments Details Value Author(s) See Also Examples

Plots probability density function given the parameters. May be useful when investigating parameter choice for prior distributions.

DistributionPlotBinomial(size = 200, prob = 0.5,
xlab = "Number of Successes", ylab = "Probability Mass", signif.digits = 3,
main = paste("Binomial Distribution: n =", size, "p =",
signif(prob, digits = signif.digits)))

DistributionPlotGamma(shape = 1, rate = 1, length = 100, xlab = "x",
ylab = "Density", main = bquote(paste("Gamma Distribution: ", alpha,
"=", .(signif(shape, digits = signif.digits)), ",", beta, "=",
.(signif(rate, digits = signif.digits)))), signif.digits = 3)

DistributionPlotNorm(mean = 0, sd = 1, length = 100, xlab = "x", ylab =
"Density", main = bquote(paste("Normal Distribution: ", mu, "=",
.(signif(mean, digits = signif.digits)), ",", sigma, "=", .(signif(sd,
digits = signif.digits)))), signif.digits = 3)

`size`	number of trials (Binomial)
`prob`	probability of success (Binomial)
`shape`	shape parameter. Must be strictly positive. (Gamma)
`rate`	an alternative way to specify the scale (Gamma)
`mean`	mean (Normal)
`sd`	standard deviation (Normal)
`xlab`	x-axis label
`ylab`	y-axis label
`signif.digits`	number of significant digits for default `main` title
`main`	title for plot
`length`	Number of points to use for obtaining a smooth curve

Based on functions in package Rcmdr

None.

Peter Baker p.baker1@uq.edu.au

Rcmdr Binomial Normal GammaDist

## Binomial distribution
DistributionPlotBinomial()
DistributionPlotBinomial(size=20, prob=0.2)

## Gamma distribution
DistributionPlotGamma()

## Normal distribution
DistributionPlotNorm()