| Title: | Time Series Modeling for Climate Change Proxies |
|---|---|
| Description: | Implementation of the Wilkinson and Ivany (2002) approach to paleoclimate analysis, applied to isotope data extracted from clams. |
| Authors: | Jonathan M. Lees |
| Maintainer: | Jonathan M. Lees<[email protected]> |
| License: | GPL |
| Version: | 2.1-3 |
| Built: | 2025-01-29 06:09:18 UTC |
| Source: | https://github.com/cran/ClamR |
This program implements and improves upon the Wilkinson and Ivany approach to climate time series modeling. The jackknife is used to estimate the 95 percent confidence bounds for the modeled estimates. dx should be chosen to be approximately half a cycle or more.
Jonathan M. Lees
Maintainer: Jonathan M. Lees<[email protected]>
Wilkinson, B. H. and Ivany, L. C., Paleoclimatic inference from stable isotope profiles of accretionary biogenic hardparts; a quantitative approach to the evaluation of incomplete data, Palaeogeography, Palaeoclimatology, Palaeoecology, vol. 185, no. 1-2, pp.95-114, 01 Sep 2002.
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
## Not run: data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 gout = proxyJK(x, y, dx) plotproxy1(x, y, gout) par(mfrow=c(2,1)) plotproxy.error(x, y, gout, type = 1) plotproxy.error(x, y, gout, type = 2) par(mfrow=c(2,1)) plotproxy.error(x, y, gout, type = 2) plotproxy.all2(gout,YAXstyle=1 ) ## End(Not run)## Not run: data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 gout = proxyJK(x, y, dx) plotproxy1(x, y, gout) par(mfrow=c(2,1)) plotproxy.error(x, y, gout, type = 1) plotproxy.error(x, y, gout, type = 2) par(mfrow=c(2,1)) plotproxy.error(x, y, gout, type = 2) plotproxy.all2(gout,YAXstyle=1 ) ## End(Not run)
Proxy data from the Orknay Islands
data(CLAM1)data(CLAM1)
The format is: List of 2 $ x: num [1:91] 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 ... $ y: num [1:91] 2.14 2.11 2.4 2.21 2.32 2.44 2.85 2.78 2.27 2.05 ...
Duplicate data has been removed.
One-year data interval (4.68-9.31 mm) of
record of an archaeological limpet Patella vulgata
(specimen QG2-1064-1) from Orkney, Scotland (Surge and Barrett, 2012).
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
data(CLAM1) ## maybe str(CLAM1) ; plot(CLAM1) ... plot(CLAM1$x, CLAM1$y, type="b", xlab="Distance", ylab="d18O" ) ## Not run: ###### this is an example from Wang et al.: #### it takes too long to run on CRAN, but should work shellx=CLAM1$x[38:70] shelly=CLAM1$y[38:70] window_shell=windowsize(shellx,shelly,1.8,9.4,0.2) #the window size is 5mm, and make all the plots together gout_shell = proxyJK(shellx, shelly, 5) par(mfrow=c(3,2)) plot(shellx,shelly,type="b", xlab="Distance from Margin (mm)", ylab=expression(delta*"18O(ppm VPDB)"), xlim=c(4,10), ylim = c(1.5,4)) plot((window_shell$win)/2,window_shell$error,xlab="Window Size (mm)", ylab="Error", xlim=c(1.6/2,9.4/2), ylim=c(0,0.5)) abline(v=4.63/2, lty="dotdash",col="black") abline(v=5/2, col="black") plotproxy1(shellx, shelly, gout_shell, xlim=c(4,10), ylim = c(1.5,4), xlab="Distance from Margin (mm)", ylab=expression(delta*"18O(ppm VPDB)"), main="") plotproxy.all(gout_shell,YAXstyle=1, xlim=c(4,10), ylim1=c(0,4), ylim2=c(-15,5)) plotproxy.error(shellx, shelly, gout_shell, type = 1, xlim=c(4,10), ylim = c(1.5,4), xlab="Distance from Margin (mm)", ylab=expression(delta*"18O(ppm VPDB)")) plotproxy.error(shellx, shelly, gout_shell, type = 2, xlim=c(4,10), ylim = c(1.5,4), xlab="Distance from Margin (mm)", ylab=expression(delta*"18O(ppm VPDB)")) ## End(Not run)data(CLAM1) ## maybe str(CLAM1) ; plot(CLAM1) ... plot(CLAM1$x, CLAM1$y, type="b", xlab="Distance", ylab="d18O" ) ## Not run: ###### this is an example from Wang et al.: #### it takes too long to run on CRAN, but should work shellx=CLAM1$x[38:70] shelly=CLAM1$y[38:70] window_shell=windowsize(shellx,shelly,1.8,9.4,0.2) #the window size is 5mm, and make all the plots together gout_shell = proxyJK(shellx, shelly, 5) par(mfrow=c(3,2)) plot(shellx,shelly,type="b", xlab="Distance from Margin (mm)", ylab=expression(delta*"18O(ppm VPDB)"), xlim=c(4,10), ylim = c(1.5,4)) plot((window_shell$win)/2,window_shell$error,xlab="Window Size (mm)", ylab="Error", xlim=c(1.6/2,9.4/2), ylim=c(0,0.5)) abline(v=4.63/2, lty="dotdash",col="black") abline(v=5/2, col="black") plotproxy1(shellx, shelly, gout_shell, xlim=c(4,10), ylim = c(1.5,4), xlab="Distance from Margin (mm)", ylab=expression(delta*"18O(ppm VPDB)"), main="") plotproxy.all(gout_shell,YAXstyle=1, xlim=c(4,10), ylim1=c(0,4), ylim2=c(-15,5)) plotproxy.error(shellx, shelly, gout_shell, type = 1, xlim=c(4,10), ylim = c(1.5,4), xlab="Distance from Margin (mm)", ylab=expression(delta*"18O(ppm VPDB)")) plotproxy.error(shellx, shelly, gout_shell, type = 2, xlim=c(4,10), ylim = c(1.5,4), xlab="Distance from Margin (mm)", ylab=expression(delta*"18O(ppm VPDB)")) ## End(Not run)
Application to modern climate record at Croig Cave and make comparison between reconstructed temperatures and instrumentally measured temperatures.
data("climate")data("climate")
A data frame with 360 observations on the following 3 variables.
Montha numeric vector
overalla numeric vector
Temperaturea numeric vector
Monthly sea surface temperature (SST) record for the years 1961-1990 derived from observations near Croig Cave, an archaeological site on the Isle of Mull in the Hebrides Islands west of mainland Scotland (Extended Reconstructed Sea Surface Temperature, Smith and Reynolds, 2004).
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
data(climate) climate_month <- climate$overall climate_temp <- climate$Temperature plot(climate_month,climate_temp,type="l", xlab="month",ylab=expression(paste("Temperature ("^"o","C)")))data(climate) climate_month <- climate$overall climate_temp <- climate$Temperature plot(climate_month,climate_temp,type="l", xlab="month",ylab=expression(paste("Temperature ("^"o","C)")))
Two years of data from Elliot
data("elliot_yr1")data("elliot_yr1")
The format is: List of 4 $ date1 : num [1:28] 1995 1995 1995 1995 1995 ... $ d18o1 : num [1:28] -0.036 0.244 0.525 0.332 0.148 -0.43 -0.583 -0.366 -0.641 -0.86 ... $ date_temp1: num [1:31] 1995 1995 1995 1995 1995 ... $ d18o_pred1: num [1:31] -0.58 0 0.54 0.88 0.66 0.35 0.09 -0.09 -0.53 -1.1 ...
Data consists of date, , temperature and predicted anomaly for two years of data.
Data sets are
selected from the record of a modern
Mercenaria mercenaria shell collected live from Cedar Key in northern
Florida and analyzed by
Elliot et al. (2003). Series elliot_yr1 records one summer
(including the most negative ).
Series elliot_yr2 records one winter (including the most positive ).
Because the modern Mercenaria mercenaria shell by Elliot et al. (2003)
is well dated and its in situ records of SST and salinity are
available, the predicted are also derived
from the local instrumental data. Temp is the predicted for the summer interval of
Year1 and Temp2 is the predicted
for the winter interval of Year2.
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
data(elliot_yr1) plot(elliot_yr1$date1,elliot_yr1$d18o1,xlab="Age(years)", ylab=expression(delta*'18O(ppm VPDB)'), xlim=c(1994.9,1996.2), ylim=c(-2.5,2))data(elliot_yr1) plot(elliot_yr1$date1,elliot_yr1$d18o1,xlab="Age(years)", ylab=expression(delta*'18O(ppm VPDB)'), xlim=c(1994.9,1996.2), ylim=c(-2.5,2))
Winter season of data from Elliot data.
data("elliot_yr2")data("elliot_yr2")
The format is: List of 4 $ date1 : num [1:28] 1995 1995 1995 1995 1995 ... $ d18o1 : num [1:28] -0.036 0.244 0.525 0.332 0.148 -0.43 -0.583 -0.366 -0.641 -0.86 ... $ date_temp1: num [1:31] 1995 1995 1995 1995 1995 ... $ d18o_pred1: num [1:31] -0.58 0 0.54 0.88 0.66 0.35 0.09 -0.09 -0.53 -1.1 ...
Data consists of date, , temperature and predicted data for winter season of data.
See explanation in elliot_yr1.
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
data(elliot_yr2) plot(elliot_yr2$date2,elliot_yr2$d18o2,xlab="date(year)", ylab=expression(delta*'18O(ppm VPDB)'), xlim=c(1995.2,1996.85), ylim=c(-2.5,1.8))data(elliot_yr2) plot(elliot_yr2$date2,elliot_yr2$d18o2,xlab="date(year)", ylab=expression(delta*'18O(ppm VPDB)'), xlim=c(1995.2,1996.85), ylim=c(-2.5,1.8))
Make an X-Y plot with error bars.
error.bar(x, y, lo, hi, pch = 1, col =1, barw = 0.1, add = FALSE, ...)error.bar(x, y, lo, hi, pch = 1, col =1, barw = 0.1, add = FALSE, ...)
x |
X-values |
y |
Y-values |
lo |
Lower limit of error bars |
hi |
Upper limit of error bars |
pch |
plotting character |
col |
color |
barw |
width of the bar |
add |
logical, add=FALSE starts a new plot |
... |
other plotting parameters |
graphical side effects
Jonathan M. Lees<[email protected]>
x = 1:10 y = 2*x+5 zup = rnorm(10) zup = zup-min(zup)+.5 zdown = rnorm(10) zdown = zdown-min(zdown)+.2 #### example with same error on either side: error.bar(x, y, y-zup, y+zup, pch = 1, col = 'brown' , barw = 0.1, add = FALSE) #### example with different error on either side: error.bar(x, y, y-zdown, y+zup, pch = 1, col = 'brown' , barw = 0.1, add = FALSE)x = 1:10 y = 2*x+5 zup = rnorm(10) zup = zup-min(zup)+.5 zdown = rnorm(10) zdown = zdown-min(zdown)+.2 #### example with same error on either side: error.bar(x, y, y-zup, y+zup, pch = 1, col = 'brown' , barw = 0.1, add = FALSE) #### example with different error on either side: error.bar(x, y, y-zdown, y+zup, pch = 1, col = 'brown' , barw = 0.1, add = FALSE)
Find the next integer power of 2
NextPow2(x)NextPow2(x)
x |
integer |
integer that is a power of 2 higher than given integer
Jonathan M. Lees<[email protected]>
NextPow2(600) NextPow2(1023) NextPow2(1025)NextPow2(600) NextPow2(1023) NextPow2(1025)
Early Oligocene otolith from the US Gulf Coast.
data(otolith)data(otolith)
A data frame with 63 observations on the following 2 variables.
distancea numeric vector
d18Oa numeric vector
Duplicate data have been removed.
record of an aragonite otolith from the early
Oligocene Rosefield Clay in the US Gulf Coast (Ivany, 2000).
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.\
data(otolith) plot(otolith$distance, otolith$d180)data(otolith) plot(otolith$distance, otolith$d180)
Plot output of proxyJK, the jackknife estimate of the time series analysis fitting curves.
plotproxy.error(x,y,gout, type=1, xlim=NULL, ylim=NULL, ylab="", xlab="", main="" ) plotproxy.all(gout, ylab1="", ylab2="",xlab="", main="", xlim=NULL, ylim1=NULL, ylim2=NULL, legposition="topleft", YAXstyle=0, pbox=TRUE, legnames = c('Phs', 'Pos', 'Amp', 'Prd') ) plotproxy1(x, y, gout, xlim = NULL, ylim = NULL, ylab = "", xlab = "", main = "") plotproxy.error11(x, y, gout, type = 1, xlim = NULL, ylim = NULL, ylab = "", xlab = "", main = "") plotproxy.all2(gout, ylab1 = "", ylab2 = "", xlab = "", main = "", xlim = NULL, ylim1 = NULL, ylim2 = NULL, legposition = "topleft", YAXstyle = 0, pbox = TRUE, legnames = c("Phs", "Pos", "Amp", "Prd"))plotproxy.error(x,y,gout, type=1, xlim=NULL, ylim=NULL, ylab="", xlab="", main="" ) plotproxy.all(gout, ylab1="", ylab2="",xlab="", main="", xlim=NULL, ylim1=NULL, ylim2=NULL, legposition="topleft", YAXstyle=0, pbox=TRUE, legnames = c('Phs', 'Pos', 'Amp', 'Prd') ) plotproxy1(x, y, gout, xlim = NULL, ylim = NULL, ylab = "", xlab = "", main = "") plotproxy.error11(x, y, gout, type = 1, xlim = NULL, ylim = NULL, ylab = "", xlab = "", main = "") plotproxy.all2(gout, ylab1 = "", ylab2 = "", xlab = "", main = "", xlim = NULL, ylim1 = NULL, ylim2 = NULL, legposition = "topleft", YAXstyle = 0, pbox = TRUE, legnames = c("Phs", "Pos", "Amp", "Prd"))
x |
original x values from file |
y |
original y values from file |
gout |
output of proxyJK |
type |
type of error bar plotting: 1 = bars, 2=shaded |
xlim |
2-vector(limit on x-axis) |
ylim |
2-vector(limit on xy-axis) |
ylim1 |
2-vector(limit on xy-axis) |
ylim2 |
2-vector(limit on xy-axis) |
ylab |
character, y-axis label |
ylab1 |
character, y-axis label |
ylab2 |
character, y-axis label |
xlab |
character, x-axis label |
main |
character, title label |
legposition |
legend position |
YAXstyle |
Style for Y-axis |
pbox |
logical |
legnames |
names for legend |
Takes output directly from program
graphical side effects
Jonathan M. Lees<[email protected]>
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
proxyJK
## Not run: ## example to read in your data from csv file (for non ClamR data ## fn = "donna_viking_1.csv" ## C1 = scan(file=fn, what=list(mm="", o18=""), sep=",") ## x = as.numeric(C1$mm) ## y = as.numeric(C1$o18) ## x = x[!is.na(y)] ## y = y[!is.na(y)] data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 gout = proxyJK(x, y, dx) plotproxy1(x,y,gout) ## End(Not run)## Not run: ## example to read in your data from csv file (for non ClamR data ## fn = "donna_viking_1.csv" ## C1 = scan(file=fn, what=list(mm="", o18=""), sep=",") ## x = as.numeric(C1$mm) ## y = as.numeric(C1$o18) ## x = x[!is.na(y)] ## y = y[!is.na(y)] data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 gout = proxyJK(x, y, dx) plotproxy1(x,y,gout) ## End(Not run)
Runs one cycle of optimum Wilkinson curve fitting for a single sinusoid fitting.
proxyA(ax, ay, xin)proxyA(ax, ay, xin)
ax |
x-axis values |
ay |
y-axis values |
xin |
starting model: c( Phs,Pos,Amp,Prd) |
This program implements the Wilkinson and ivany approach to climate time series modeling. This is used in the more sophisticated proxyJK code.
Optimum model, vector of 4 values
Uses stats package routine optim for optimization
Jonathan M. Lees<[email protected]>
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.\
Wilkinson, B. H. and Ivany, L. C., Paleoclimatic inference from stable isotope profiles of accretionary biogenic hardparts; a quantitative approach to the evaluation of incomplete data, Palaeogeography, Palaeoclimatology, Palaeoecology, vol. 185, no. 1-2, pp.95-114, 01 Sep 2002
proxyJK
data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 Aout = proxyA(x, y, dx)data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 Aout = proxyA(x, y, dx)
Perform a jackknife estimate of proxy curve fitting for time series analysis.
proxyJK(x, y, dx)proxyJK(x, y, dx)
x |
x-axis values |
y |
y-axis values |
dx |
width of window to span in time |
Routine that improves on the Wilkinson and Ivany(2002) approach to climate time series modeling. The jackknife is used to estimate the 95 percent confidence bounds for the modeled estimates. dx should be chosen to be approximately half a cycle or more.
List:
OUT |
list( par, mid, ax, predmid,JKest, JKvar, PSTILDE ) |
omids |
output midpoints |
pmids |
values at output midpoints |
x |
input x |
y |
input y |
predy |
predicted y from spline fit |
See proxyA for a duplication of the Wilkinson codes.
Jonathan M. Lees<[email protected]>
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
proxyA
## Not run: ########## this is for reading in data ######## fn = "/home/lees/DONNA/donna_viking_1.csv" ## fn = "donna_viking_1.csv" ######## C1 = scan(file=fn, what=list(mm="", o18=""), sep=",") ######## x = as.numeric(C1$mm) ######## y = as.numeric(C1$o18) ########x = x[!is.na(y)] ########y = y[!is.na(y)] data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 gout = proxyJK(x, y, dx) plotproxy1(x, y, gout) par(mfrow=c(2,1)) plotproxy.error(x, y, gout, type = 1) plotproxy.error(x, y, gout, type = 2) par(mfrow=c(2,1)) plotproxy.error(x, y, gout, type = 2) plotproxy.all2(gout, YAXstyle=1 ) ## End(Not run)## Not run: ########## this is for reading in data ######## fn = "/home/lees/DONNA/donna_viking_1.csv" ## fn = "donna_viking_1.csv" ######## C1 = scan(file=fn, what=list(mm="", o18=""), sep=",") ######## x = as.numeric(C1$mm) ######## y = as.numeric(C1$o18) ########x = x[!is.na(y)] ########y = y[!is.na(y)] data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 gout = proxyJK(x, y, dx) plotproxy1(x, y, gout) par(mfrow=c(2,1)) plotproxy.error(x, y, gout, type = 1) plotproxy.error(x, y, gout, type = 2) par(mfrow=c(2,1)) plotproxy.error(x, y, gout, type = 2) plotproxy.all2(gout, YAXstyle=1 ) ## End(Not run)
Rescale a vector to fit in a certain range
RESCALE(x, nx1, nx2, minx, maxx)RESCALE(x, nx1, nx2, minx, maxx)
x |
vector |
nx1 |
new minimum |
nx2 |
new maximum |
minx |
old min |
maxx |
old max |
Used for graphics.
scale vector is returned
Jonathan M. Lees<jonathan.lees.edu>
x = rnorm(10) RESCALE(x, 3, 9, min(x), max(x) )x = rnorm(10) RESCALE(x, 3, 9, min(x), max(x) )
Application to estimated temperatures from archaeological RWP shell 103a-39-1 with 3 years of temperature data.
data("rwp_limpet")data("rwp_limpet")
A data frame with 74 observations on the following 8 variables.
distance_alla numeric vector
temp_alla numeric vector
distance1a numeric vector
temp1a numeric vector
distance2a numeric vector
temp2a numeric vector
distance3a numeric vector
temp3a numeric vector
RWP shell 103a-39-1
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
data(rwp_limpet) plot(rwp_limpet$distance_all, rwp_limpet$temp_all, type='b')data(rwp_limpet) plot(rwp_limpet$distance_all, rwp_limpet$temp_all, type='b')
Sine Model of climate time series
SinMod(x, myEx, dC2)SinMod(x, myEx, dC2)
x |
input model x consisting of 4 values, phase, position, amplitude, period |
myEx |
externally defined X-values |
dC2 |
externally defined observations at X |
This is the function used in optimization of sinusoidal fits to climate data.
squared sum of difference between obsereved and predicted
Uses stats package for optimization
Jonathan M. Lees<[email protected]>
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
optim, proxyA, proxyJK
data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 A1 = proxyA(x, y, dx) RMSout = SinMod(A1$par, x, y)data(CLAM1) x = CLAM1$x y = CLAM1$y dx = 3.392 A1 = proxyA(x, y, dx) RMSout = SinMod(A1$par, x, y)
Estimate optimal window size for seasonal time series analysis.
windowsize(x, y, winmin, winmax, winstep)windowsize(x, y, winmin, winmax, winstep)
x |
original x values from file |
y |
original y values from file |
winmin |
Minimum window size |
winmax |
Maximum window size |
winstep |
step size |
win |
optimum window length |
error |
error for win |
Jonathan M. Lees<[email protected]>
Wang, T., Surge, D., and Lees, J. M., (2015) ClamR: A Statistical Evaluation of Isotopic and Temperature Records in Sclerochronologic Studies. Palaeogeography, Palaeoclimatology, Palaeoecology, doi:10.1016/j.palaeo.2015.07.008.
## Not run: data(CLAM1) shellx=CLAM1$x[38:70] shelly=CLAM1$y[38:70] window_shell=windowsize(shellx,shelly,1.8,9.4,0.2) ## End(Not run)## Not run: data(CLAM1) shellx=CLAM1$x[38:70] shelly=CLAM1$y[38:70] window_shell=windowsize(shellx,shelly,1.8,9.4,0.2) ## End(Not run)