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Computes ecological memory patterns on simulated pollen curves produced by the virtualPollen package.

runExperiment.Rd

Applies computeMemory to assess ecological memory on a large set of virtual pollen curves.

Usage

runExperiment(
  simulations.file = NULL,
  selected.rows = NULL,
  selected.columns = NULL,
  parameters.file = NULL,
  parameters.names = NULL,
  driver.column = NULL,
  response.column = "Pollen",
  subset.response = "none",
  time.column = "Time",
  time.zoom = NULL,
  lags = NULL,
  repetitions = 10
)

Arguments

simulations.file

List of dataframes produced by virtualPollen::simulatePopulation. Each list element is a time series dataframe for one virtual taxon. Can be a 1D list (one sampling scheme) or a 2D matrix-like list (rows = taxa, columns = sampling schemes). See virtualPollen::simulation for an example. Default: NULL.

selected.rows

Numeric vector indicating which virtual taxa (list elements) from simulations.file to analyze. For example, c(1, 3) analyzes the 1st and 3rd taxa. Default: NULL (analyzes all taxa).

selected.columns

Numeric vector indicating which sampling schemes (columns) from simulations.file to analyze. Only relevant when simulations.file has a 2D structure with multiple sampling schemes. Default: NULL (uses the first sampling scheme only).

parameters.file

Dataframe of simulation parameters produced by virtualPollen::parametersDataframe, with one row per virtual taxon. Rows must align with simulations.file. See virtualPollen::parameters for an example. Default: NULL.

parameters.names

Character vector of column names from parameters.file to include in output labels. These help identify which simulation settings produced each result. Example: c("maximum.age", "fecundity"). Default: NULL.

driver.column

Character vector of column names representing environmental drivers in the simulation dataframes. Common choices: "Driver.A", "Driver.B", or "Suitability". Default: NULL.

response.column

Character string naming the response variable column in the simulation dataframes. Use "Pollen" for pollen abundance from virtualPollen::simulation. Default: "Pollen".

subset.response

character string, one of "up", "down" or "none", triggers the subsetting of the input dataset. "up" only models ecological memory on cases where the response's trend is positive, "down" selects cases with negative trends, and "none" selects all cases. Default: "none".

time.column

character string, name of the time/age column. Usually, "Time". Default: "Time".

time.zoom

numeric vector with two numbers defining the time/age extremes of the time interval of interest. Default: NULL.

lags

numeric vector, lags to be used in the equation, in the same units as time. The use of seq to define it is highly recommended. If 0 is absent from lags, it is added automatically to allow the consideration of a concurrent effect. Lags should be aligned to the temporal resolution of the data. For example, if the interval between consecutive samples is 100 years, lags should be something like 0, 100, 200, 300. Lags can also be multiples of the time resolution, such as 0, 200, 400, 600 (when time resolution is 100 years). Default: NULL.

repetitions

integer, number of random forest models to fit. Default: 10.

Value

A list with 2 slots:

  • names matrix of character strings, with as many rows and columns as simulations.file. Each cell holds a simulation name to be used afterwards, when plotting the results of the ecological memory analysis.

  • output a list with as many rows and columns as simulations.file. Each slot holds a an output of computeMemory.

    • memory dataframe with five columns:

      • Variable character, names and lags of the different variables used to model ecological memory.

      • median numeric, median importance across repetitions of the given Variable according to Random Forest.

      • sd numeric, standard deviation of the importance values of the given Variable across repetitions.

      • min and max numeric, percentiles 0.05 and 0.95 of importance values of the given Variable across repetitions.

    • R2 vector, values of pseudo R-squared value obtained for the Random Forest model fitted on each repetition. Pseudo R-squared is the Pearson correlation between the observed and predicted data.

    • prediction dataframe, with the same columns as the dataframe in the slot memory, with the median and confidence intervals of the predictions of all random forest models fitted.

    • multicollinearity multicollinearity analysis on the input data performed with vif_df. A vif value higher than 5 indicates that the given variable is highly correlated with other variables.

See also

computeMemory

Other virtualPollen: experimentToTable(), plotExperiment()

Author

Blas M. Benito <blasbenito@gmail.com>