View source: R/aggregateSimulation.R
aggregateSimulation | R Documentation |
simulatePopulation
.Takes the output of simulatePopulation
and aggregates it into centimetres by following a sediment accumulation rate produced by simulateAccumulationRate
. It further samples it at given depth intervals. It intends to simulate a pseudo-realistic sedimentation of the pollen produced by the simulation, and to apply a pollen-sampling pattern to a virtual pollen core.
aggregateSimulation( simulation.output=NULL, accumulation.rate=NULL, sampling.intervals=1 )
simulation.output |
list, output of |
accumulation.rate |
dataframe, output of |
sampling.intervals |
integer, numeric vector, depth interval or intervals between consecutive samples in centimetres. If 1, all samples are returned, if 2, returned samples are separated by 1 cm. |
The function uses the values in the grouping
column of the simulateAccumulationRate
output to aggregate together (by computing the mean
) as many samples as cases in grouping
have the same identificator. Output samples are identified by the average age of the samples within the given centimetre.
A list of dataframes with as many rows as virtual taxa were produced by simulatePopulation
, and the following columns: column 1 is the original data, column 2 is the original data aggregated by the accumulation rate, columns 3 to n are the different sampling intervals defined by the user.
Blas M. Benito <blasbenito@gmail.com>
simulateAccumulationRate
, simulatePopulation
#getting example data data(simulation) data(accumulationRate) #aggregating first simulation outcome sim.output.aggregated <- aggregateSimulation( simulation.output = simulation[1], accumulation.rate = accumulationRate, sampling.intervals = c(2,6)) #comparing simulations par(mfrow = c(3,1)) #notice the subsetting of the given column of the input list plot(sim.output.aggregated[[1,1]]$Time, sim.output.aggregated[[1,1]]$Pollen, type = "l", xlim = c(500, 1000), main = "Annual" ) plot(sim.output.aggregated[[1,2]]$Time, sim.output.aggregated[[1,2]]$Pollen, type = "l", xlim = c(500, 1000), main = "2cm" ) plot(sim.output.aggregated[[1,3]]$Time, sim.output.aggregated[[1,3]]$Pollen, type = "l", xlim = c(500, 1000), main = "6cm" ) #check differences in nrow nrow(sim.output.aggregated[[1,1]]) #original data nrow(sim.output.aggregated[[1,2]]) #2cm nrow(sim.output.aggregated[[1,3]]) #6cm intervals
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