stratigraph (version 0.66)

dloc: Draws a line of correlation between two sections

Description

Calculates and draws a line of correlation between two sections.

Usage

dloc(x, y, weights = NULL, pin.ends = FALSE, method = 'genetic', end.segs = ceiling(length(x)/3), pop = 100, max.gen = 200, mut = 0.01, recomb = 'roulette', ext = 0.5, tol = 0.005, start = 'lm', verbose = 2, plot = 1)
dcloc(x, y, pins, weights = NULL, method = 'genetic', end.segs = 3, pop = 100, max.gen = 200, mut = 0.01, recomb = 'roulette', ext = 0.5, tol = 0.005, start = 'lm', verbose = 2, plot = 1)

Arguments

x

a vector of the depths of datums in the better ('reference') section

y

a vector of the depths of datums in the worse section.

pins

a vector of fixed points (marker beds) through which the LOC is constrained to pass.

weights

a vector of weights the same length as x and y for weighted least squares.

pin.ends

whether the ends should be pinned to (fixed at) the first and last points: TRUE or 'both' fixes both top and bottom, 'top' and 'bottom' fix one or the other and FALSE (the default) allows both to float.

method

currently only 'genetic' supported.

end.segs

the maximum number of line segments in the LOC to consider.

pop

the population size of evolving LOCs.

max.gen

the maximum number of generations to evolve.

mut

mutation rate; the fraction of the the range of supplied values given to rnorm() as standard deviation of the mutation of solutions each generation.

recomb

type of recombination; in this case only 'roulette' is supported, producing 'fitness proportionate selection'.

ext

extinction rate; the proportion of solutions that go extinct each generation.

tol

a tolerance at which to stop evolving; not yet implemented.

start

the solution from which to begin: either 'lm' for a least-squares linear model or 'uniform' for a random-uniform model.

verbose

0, 1, 2; larger number gives more information.

plot

0, 1, 2; larger number provides more plots.

Value

Returns a list of three things: locs, bestloc and bestsse. locs gives the ending population of LOCs; best loc gives the best LOC; and bestsse gives the sum of squared errors for the best LOC. Each of these has a length equal to the number of different numbers of segments considered, so if 1, 2, 3, 4, and 5-segment LOCs were considered, locs, bestloc and bestsse will each have length 5.

References

Zhang, T (2000) Artificial Intelligence Models for Quantitative Biostratigraphy. PhD. Dissertation, University of Illinois, Chicago.

Zhang, T. and R. Plotnick (2006) `Graphic correlation using genetic algorithms' Mathematical Geology 38(7):781--800.

Miller, F.X., 1977 `The graphic correlation method in biostratigraphy' in Concepts and Methods in Biostratigraphy (Kauffman et al., eds.), pp. 165--186.

See Also

plot.strat.column, stratigraph

Examples

## Not run: 
# 
# # simplest example
# #x <- c(0,1,2,3,5,7,9,17,18,20) + rnorm(10)/5
# #y <- c(0,1,2,4,4,4,4,8,9,10) + rnorm(10)/5
# x <- c(0.103, 1.336, 2.036, 2.928, 5.123, 6.738,
#        8.998,17.145,17.960,19.753)
# y <- c(0.152, 0.855, 1.784, 4.072, 4.055, 3.992,
#        4.336, 7.963, 9.238,10.162)
# 
# xy.loc <- dloc(x, y, end.segs = 3, pop = 10, max.gen = 10,
#                start = 'uniform', plot = 2)
# 
# xy.loc <- dloc(x, y, pin.ends = 'top', end.segs = 3,
#                pop = 100, max.gen = 5, start = 'lm', plot = 1)
# 
# xy.loc <- dloc(x, y)
# 
# xy.cloc <- dcloc(x, y, pins = list(x = c(5,10), y = c(4,6)),
#                  max.gen = 10)
# 
# ## End(Not run)