magicaxis (version 1.6)

magrun: Running averages

Description

Computes running averages (medians / means / modes), user defined quantiles and standard deviations for x and y scatter data.

Usage

magrun(x, y, bins = 10, type='median', ranges = pnorm(c(-1, 1)), binaxis = "x",
equalN = TRUE, xcut, ycut, log='', Nscale=FALSE, diff=FALSE)

Arguments

x

Data x coordinates. This can be a 1D vector (in which case y is required) or a 2D matrix or data frame, where the first two columns will be treated as x and y.

y

Data y coordinates, optional if x is an appropriate structure.

bins

If a single integer value, how many bins the data should be split into. If a vector is provoided then these values are treated as the explicit bin limits to use.

type

The type of running average to determine. Options are 'median' (the default), 'mean', 'mode' and 'mode2d'. 'median' calculates the median for binned x and y values. 'mean' calculates the mean for binned x and y values. 'mode' uses the default R 'density'

ranges

The quantile ranges desired, can set to NULL if quantiles are not desired. The default adds 1-sigma equivilant quantile ranges.

binaxis

Which axis to bin across. Must be set to 'x' or 'y'.

equalN

Should the data be split into bins with equal numbers of objects (default, TRUE), or into regular spaces from min to max (FALSE). Only relevant if 'bins' paramter is set to a single integer value and 'magrun' is determining the explicit bin limits automat

xcut

A two element vector containing optional lower and upper x limits to apply to the data.

ycut

A two element vector containing optional lower and upper y limits to apply to the data.

log

Specify axes that should be logged. Allowed arguments are 'x'

Nscale

Sets whether the quantile ranges and standard deviations calculated will reduced with respect to the median by the square root of the number of contributing data within each bin. The result of setting Nscale to TRUE is to scale the data like you are calcu

diff

Should the output quantiles and standard deviations be expressed as differences from the chosen type of running avergage (TRUE) or the actual values (default, FALSE). The advantage of the former is plotting the results as errorbars using magerr, which exp

Value

• x
  The chosen averages (default median) of the x bins.
• y
  The chosen averages (default median) of the y bins.
• xquan
  Matrix containing the extra user defined x quantile ranges (columns are in the same order as the requested quantiles). If Nscale is set to TRUE then this is also divided by sqrt the contributing objects in each bin.
• yquan
  Matrix containing the extra user defined y quantile ranges (columns are in the same order as the requested quantiles). If Nscale is set to TRUE then this is also divided by sqrt the contributing objects in each bin.
• xsd
  The standard deviations in the x bins. This is a two column data.frame if 'diff' is set to FALSE, giving the x-sd and x+sd values, or a single vector if 'diff' is set to TRUE. If Nscale is set to TRUE then this is also divided by sqrt the contributing objects in each bin.
• ysd
  The standard deviations in the y bins. This is a two column data.frame if 'diff' is set to FALSE, giving the y-sd and y+sd values, or a single vector if 'diff' is set to TRUE. If Nscale is set to TRUE then this is also divided by sqrt the contributing objects in each bin.
• bincen
  The bin centres used in the chosen binning direction.
• binlim
  The bin limits used in the chosen binning direction.

Details

This function will be default calculate the running median along the x axis for y values, it is intended to be used to trace the spread in scattered data.

See Also

magplot,magaxis,maglab,magerr,magmap

Examples

#Simple example
temp=cbind(seq(0,2,len=1e4),rnorm(1e4))
temprun=magrun(temp)
magplot(temp,col='lightgreen',pch='.')
lines(temprun,col='red')
lines(temprun$x,temprun$yquan[,1],lty=2,col='red')
lines(temprun$x,temprun$yquan[,2],lty=2,col='red')
temprun=magrun(temp,binaxis='y')
lines(temprun,col='blue')
lines(temprun$xquan[,1],temprun$y,lty=2,col='blue')
lines(temprun$xquan[,2],temprun$y,lty=2,col='blue')
#Now with a gradient- makes it clear why the axis choice matters for simple line fitting etc
temp=cbind(seq(0,2,len=1e4),rnorm(1e4)+1+seq(0,2,len=1e4))
temprun=magrun(temp)
magplot(temp,col='lightgreen',pch='.')
lines(temprun,col='red')
lines(temprun$x,temprun$yquan[,1],lty=2,col='red')
lines(temprun$x,temprun$yquan[,2],lty=2,col='red')
temprun=magrun(temp,binaxis='y')
lines(temprun,col='blue')
lines(temprun$xquan[,1],temprun$y,lty=2,col='blue')
lines(temprun$xquan[,2],temprun$y,lty=2,col='blue')
#Compare the different centres.
temp=cbind(seq(0,2,len=1e4),rnorm(1e4)^2+seq(0,2,len=1e4))
temprunmedian=magrun(temp,type='median')
temprunmean=magrun(temp,type='mean')
temprunmode=magrun(temp,type='mode')
temprunmode2d=magrun(temp,type='mode2d')
magplot(temp,col='grey',pch='.',ylim=c(-2,5))
lines(temprunmedian,col='red')
lines(temprunmean,col='green')
lines(temprunmode,col='blue')
lines(temprunmode2d,col='orange')
#Choose your own bins.
temp=cbind(seq(0,2,len=1e4),rnorm(1e4)+1+seq(0,2,len=1e4))
temprun=magrun(temp,bins=c(0.1,0.5,0.7,1.2,1.3,2))
magplot(temp,col='lightgreen',pch='.')
points(temprun,col='red')
#Show the 'error in the mean' type data points. Comparing to the best fit line,
#it is clear they are much more meaningful at reflecting the error in the trend seen,
#but not the distribution (or scatter) of data around this.
temp=cbind(seq(0,2,len=1e3),rnorm(1e3)+1+seq(0,2,len=1e3))
temprun=magrun(temp,bins=5)
temprunNscale=magrun(temp,bins=5,Nscale=TRUE)
magplot(temp,col='lightgreen',pch='.')
magerr(temprun$x,temprun$y,temprun$x-temprun$xquan[,1], temprun$y-temprun$yquan[,1],
temprun$xquan[,2]-temprun$x, temprun$yquan[,2]-temprun$y, lty=2,length=0,col='blue')
magerr(temprunNscale$x,temprunNscale$y,temprunNscale$x-temprunNscale$xquan[,1],
temprunNscale$y-temprunNscale$yquan[,1],temprunNscale$xquan[,2]-temprunNscale$x,
temprunNscale$yquan[,2]-temprunNscale$y,col='red')
abline(lm(temp[,2]~temp[,1]),col='black')
#Or the above type of plot can be done more simply using the 'diff' flag.
temprun=magrun(temp,bins=5,diff=TRUE)
temprunNscale=magrun(temp,bins=5,Nscale=TRUE,diff=TRUE)
magplot(temp,col='lightgreen',pch='.')
magerr(temprun$x,temprun$y,temprun$xquan[,1], temprun$yquan[,1], temprun$xquan[,2],
temprun$yquan[,2],lty=2,length=0,col='blue')
magerr(temprunNscale$x,temprunNscale$y,temprunNscale$xquan[,1], temprunNscale$yquan[,1],
temprunNscale$xquan[,2],temprunNscale$yquan[,2],col='red')
abline(lm(temp[,2]~temp[,1]),col='black')
#Similar, but using the 'sd' output.
magplot(temp,col='lightgreen',pch='.')
magerr(temprun$x,temprun$y,temprun$xsd,temprun$ysd,lty=2,length=0,col='blue')
magerr(temprunNscale$x,temprunNscale$y,temprunNscale$xsd,temprunNscale$ysd,col='red')
abline(lm(temp[,2]~temp[,1]),col='black')