Luminescence (version 0.3.1)

plot_Histogram: Plot a histogram with a separate error plot

Description

Function plots a predefined histogram with an accompanying error plot as suggested by Rex Galbraith at the UK LED in Oxford 2010.

Usage

plot_Histogram(values, na.exclude = TRUE, mtext, cex.global, 
    breaks, se, rug, normal_curve, summary, summary.pos, colour, 
    ...)

Arguments

values

data.frame or RLum.Results object (required): for data.frame: two columns: De (values[,1]) and De error (values[,2])

na.exclude

logical (with default): excludes NA values from the data set prior to any further operations.

mtext

character (optional): further sample information (mtext).

cex.global

numeric (with default): global scaling factor.

breaks

(with default): sets breakpoints for histogram. Works as in hist.

se

logical (optional): plots standard error points over the histogram, default is FALSE.

rug

logical (optional): adds rugs to the histogram, default is TRUE.

normal_curve

logical (with default): adds a normal curve to the histogram. Mean and sd are calculated from the input data. More see details section.

summary

character (optional): adds numerical output to the plot. Can be one or more out of: "n" (number of samples), "mean" (mean De value), "median" (median of the De values), "kdemax" (maximum value of prob

summary.pos

numeric (with default): optional position coordinates for the statistical summary. Y-coordinate refers to the right hand y-axis.

colour

numeric or character (with default): optional vector of length 4 which specifies the colours of the following plot items in exactly this order: histogram bars, rug

...

further arguments and graphical parameters passed to plot. If y-axis labels are provided, these must be specified as a vector of length 2 since the plot features two axes (e.g. xlab = c("axis label

Details

If the normal curve is added, the y-axis in the histogram will show the probability density.

See Also

hist, plot

Examples

## load data
data(ExampleData.DeValues, envir = environment())

## plot histogram the easiest way
plot_Histogram(ExampleData.DeValues)

## plot histogram with some more modifications
plot_Histogram(ExampleData.DeValues, 
               rug = TRUE, 
               normal_curve = TRUE, 
               cex.global = 0.9, 
               pch = 2,
               colour = c("grey", "black", "blue", "green"),
               summary = c("n", "mean", "sdrel"),
               summary.pos = c(3500, 140),
               main = "Histogram of De-values",
               mtext = "Example data set", 
               ylab = c(expression(paste(D[e], "Distribution")),
                        "Std.-err."),
               xlim = c(1800, 4000))