toRegularTime: Reinterpolates aggregated simulations into regular time.

In virtualPollen: Simulating Pollen Curves from Virtual Taxa with Different Life and Niche Traits

Reinterpolates aggregated simulations into regular time.

Description

Takes the output of aggregateSimulation, and interpolates it into a regular time grid.

Usage

toRegularTime(
  x = NULL,
  time.column = "Time",
  interpolation.interval = NULL,
  columns.to.interpolate = c("Suitability",
                             "Driver.A",
                             "Pollen")
  )

Arguments

x	list of dataframes (generally the output of aggregateSimulation) or single dataframe with irregular time series.
time.column	character string, default value is "Time".
interpolation.interval	integer, in years, time length encompassed by each sample.
columns.to.interpolate	character string or character vector, columns of simulation output to be interpolated. Any subset of: "Pollen", "Population.mature", "Population.immature", "Population.viable.seeds", "Suitability", "Biomass.total", "Biomass.mature", "Biomass.immature", "Mortality.mature", "Mortality.immature", "Driver.A", "Driver.B".

Details

This function fits a loess model of the form y ~ x, where y is any column given by columns.to.interpolate and x is the column given by the time.column argument. The model is used to interpolate column y on a regular time series of intervals equal to interpolation.interval. If x is a matrix-like list returned by aggregateSimulation (on results of simulateAccumulationRate and simulatePopulation), the first column of the matrix will already have a regular time column, and therefore nothing will be done with this column of the list.

Value

If x is a list of dataframes, the function returns a list with the same structure as the input list. If x is a dataframe, the function returns a dataframe. In any case, output dataframes have the columns "Time" (now regular), and any column listed in columns.to.interpolate. Important: as in the input data, the time column of the output data has lower time for oldest samples and higher time for newest samples.

Author(s)

Blas M. Benito <[e-mail]>

See Also

simulateAccumulationRate, aggregateSimulation

Examples

## Not run: 
#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))

#to regular time
sim.output.aggregated <- toRegularTime(
 x=sim.output.aggregated,
 time.column ="Time",
 interpolation.interval = 10,
 columns.to.interpolate = c("Suitability", "Pollen")
)

#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

## End(Not run)

virtualPollen documentation built on March 18, 2022, 6:16 p.m.