# DistributionPlotBinomial: Distribution Plot In polySegratioMM: Bayesian mixture models for marker dosage in autopolyploids

## Description

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

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```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) ```

## Arguments

 `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

## Details

Based on functions in package `Rcmdr`

None.

## Author(s)

Peter Baker [email protected]

`Rcmdr` `Binomial` `Normal` `GammaDist`
 ```1 2 3 4 5 6 7 8 9``` ```## Binomial distribution DistributionPlotBinomial() DistributionPlotBinomial(size=20, prob=0.2) ## Gamma distribution DistributionPlotGamma() ## Normal distribution DistributionPlotNorm() ```