Description Usage Arguments Value Author(s) References See Also Examples
Calculates and draws a line of correlation between two sections.
1 2 3 4 5 6 7 8 9 10 11 | 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)
|
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. |
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.
Walton Green
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.
plot.strat.column
,
stratigraph
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## 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)
|
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