R: Plot Samples

plotSamples {LaplacesDemon} | R Documentation

## Plot Samples

### Description

This function provides basic plots that are extended to include samples.

### Usage

    plotSamples(X, Style="KDE", LB=0.025, UB=0.975, Title=NULL)

### Arguments

`X` | This required argument is a N x S numerical matrix of N records and S samples.
`Style` | This argument accepts the following quoted strings: "barplot", "dotchart", "hist", "KDE", or "Time-Series". It defaults to `Style="KDE"`.
`LB` | This argument accepts the lower bound of a probability interval, which must be in the interval [0,0.5).
`UB` | This argument accepts the upper bound of a probability interval, which must be in the interval (0.5,1].
`Title` | This argument defaults to `NULL`, and otherwise accepts a quoted string that will be the title of the plot.

### Details

The `plotSamples` function extends several basic plots from points to samples. For example, it is common to use the `hist` function to plot a histogram from a column vector. However, the user may desire to plot a histogram of a column vector that was sampled numerous times, rather than a simple column vector, in which a (usually 95%) probability interval is also plotted to show the uncertainty around the sampled median of each bin in the histogram.

The `plotSamples` function extends the `barplot`, `dotchart`, and `hist` functions to include uncertainty due to samples. The `KDE` style of plot is added so that a probability interval is shown around a sampled kernel density estimate of a distribution, and the `Time-Series` style of plot is added so that a probability interval is shown around a sampled univariate time-series.

For each style of plot, three quantiles are plotted: the lower bound (LB), median, and upper bound (UB).

One of many potential Bayesian applications is to examine the uncertainty in a predictive distribution.

### Author(s)

Statisticat, LLC. software@bayesian-inference.com

### Examples

    #library(LaplacesDemon)
    #N <- 100
    #S <- 100
    #X <- matrix(rnorm(N*S),N,S)
    #rownames(X) <- 1:100
    #plotSamples(X, Style="barplot", LB=0.025, UB=0.975)
    #plotSamples(X[1:10,], Style="dotchart", LB=0.025, UB=0.975)
    #plotSamples(X, Style="hist", LB=0.025, UB=0.975)
    #plotSamples(X, Style="KDE", LB=0.025, UB=0.975)
    #plotSamples(X, Style="Time-Series", LB=0.025, UB=0.975)

[Package LaplacesDemon version 16.1.1 Index]