convertPars: Converts parameters to x,y coordinates (date and pdf) that...

In UCL/ADMUR: Ancient Demographic Modelling Using Radiocarbon

Converts parameters to x,y coordinates (date and pdf) that describe a model

Description

Converts either a vector of parameters, or a matrix of many parameter sets to model x,y coordinates (date and pdf) \loadmathjax

Usage

convertPars(pars, years, type, timeseries = NULL)

Arguments

pars	Either a numeric vector (one parameter set), or a matrix of several parameter sets (one set per row).
years	A vector of years.
type	Choose from the following currently available models. Composite models can be achieved using a vector of more than one type. For example, c('norm','power') will be a composite model, where the first two parameters are the mean and SD, the 3rd and 4th parameters determine the power distribution component, for example if modelling taphonomy.
timeseries	A data frame containing names x and y (date and pdf) must be provided as the timeseries, only if type is 'timeseries'. If 'type' is anything else, timeseries is not required (default = NULL).

Details

Converts model parameters into a timeseries. For example, a parameter search will yield either a single set of parameters, or a matrix with one parameter set per row (such as the 'res' value from mcmc). Either can be handed directly to this function. The structure of the output differs depending on if converting a vector or matrix.

Examples

	#  a random 6-CPL parameter set
	pars <- runif(11)
	x <- convertPars( pars=pars, years=5500:7500, type='CPL')

	#  a matrix of 5 random 6-CPL parameter sets
	pars <- matrix( runif(11*5), 5, 11 )
	x <- convertPars( pars=pars, years=5500:7500, type='CPL')

	#  a random exponential parameter
	pars <- runif(1, -0.01, 0.01)
	x <- convertPars( pars=pars, years=5500:7500, type='exp')

	#  a matrix of 5 random exponential parameter sets
	pars <- matrix( runif(5, -0.01, 0.01), 5, 1 )
	x <- convertPars( pars=pars, years=5500:7500, type='exp')

	#  a random Gaussian parameter pair (mean, sd)
	pars <- runif(2, c(6000,200), c(7000,1000))
	x <- convertPars( pars=pars, years=5500:7500, type='norm')

	# a combination model of a Gaussian (parameters = mean, sd), 
	# and a power model assumed to be a taphonomic effect (parameters = b,c).
	pars <- runif(4, c(6000,200,0,-3), c(7000,1000,20000,0))
	x <- convertPars( pars=pars, years=5500:7500, type=c('norm','power'))

	# 5 combination models of a Gaussian (parameters = mean, sd),
	# and a power model assumed to be a taphonomic effect (parameters = b,c).
	pars <- t(matrix(runif(4*5, c(6000,200,0,-3), c(7000,1000,20000,0)),4,5))
	x <- convertPars( pars=pars, years=5500:7500, type=c('norm','power'))

	# a single random Cauchy parameter pair (location, scale)
	pars <- runif(2, c(6000,200), c(7000,1000))
	x <- convertPars( pars=pars, years=5500:7500, type='cauchy')

	# a combination model of a Cauchy (parameters = location, scale),
	# and a power model assumed to be a taphonomic effect (parameters = b,c).
	pars <- runif(4, c(6000,200,0,-3), c(7000,1000,20000,0))
	x <- convertPars( pars=pars, years=5500:7500, type=c('norm','power'))

	# a single random logistic parameter pair (k, x0)
	pars <- runif(2, c(0,6000), c(0.01,6500))
	x <- convertPars( pars=pars, years=5500:7500, type='logistic')

	# a combination model of a logistic (parameters = k, x0),
	# and a power model assumed to be a taphonomic effect (parameters = b,c).
	pars <- runif(4, c(0,6000,0,-3), c(0.01,6500,20000,0))
	x <- convertPars( pars=pars, years=5500:7500, type=c('logistic', 'power'))

	# a single random sinewave parameter set (f,p,r)
	f <- 1/runif(1,200,1000)
	p <- runif(1,0,2*pi)
	r <- runif(1,0,1)
	x <- convertPars( pars=c(f,p,r), years=5500:7500, type='sine')

	# a combination model of a sinewave (parameters = f,p,r),
	# and a power model assumed to be a taphonomic effect (parameters = b,c).

	f <- 1/runif(1,200,1000)
	p <- runif(1,0,2*pi)
	r <- runif(1,0,1)
	b <- runif(1,0,20000)
	c <- runif(1,-3,0)
	x <- convertPars( pars=c(f,p,r,b,c), years=5500:7500, type=c('sine','power'))
	
	# although a uniform distribution has no parameters, a pdf can still be generated:
	# pars must be set to NA
	x <- convertPars(pars=NA, years=5500:7500, type='uniform')

	# a uniform combined with a power model (assumed to be a taphonomic effect):
	# the parameter for the uniform component must be set to NA
	pars <- c(NA, runif(2, c(0,-3), c(20000,0)))
	x <- convertPars(pars=pars, years=5500:7500, type=c('uniform','power'))
	

UCL/ADMUR documentation built on Sept. 14, 2023, 11:41 a.m.